artificial intelligence blockchain building energy management building information modelling data analytics data mining energy efficiency Energy efficient building energy management energy performance indicator flexibility measurement industry 4.0 industry 4.0 maturity assessment Internet of Things knowledge management linked data machine learning manufacturing Ontology ontology engineering ontology population product configuration product lifecycle management production planning and control requirement engineering resource efficient manufacturing smart cities Supply Chain 4.0 sustainability virtual engineering
2023 |
Wicaksono Hendro; Trat, Martin.; Bashyal Atit; Boroukhian Tina; Felder Mine; Ahrens Mischa; Bender Janek; Groß Sebastian; Steiner Daniel; July Christoph; Dorus Christoph; Zoerner Thorsten Artificial Intelligence Enabled Dynamic Demand Response System for Maximizing the Use of Green Electricity in Production Processes, Robotics and Computer-Integrated Manufacturing Journal Article Forthcoming Robotics and Computer-Integrated Manufacturing, Forthcoming. Abstract | BibTeX | Tags: artificial intelligence, Artificial neural network, machine learning, Ontology, production scheduling, reinforcement learning @article{Wicaksono2023, title = {Artificial Intelligence Enabled Dynamic Demand Response System for Maximizing the Use of Green Electricity in Production Processes, Robotics and Computer-Integrated Manufacturing}, author = {Wicaksono, Hendro; Trat, Martin.; Bashyal, Atit; Boroukhian, Tina; Felder, Mine; Ahrens, Mischa; Bender, Janek; Groß, Sebastian; Steiner, Daniel; July, Christoph; Dorus, Christoph; Zoerner, Thorsten}, year = {2023}, date = {2023-12-30}, journal = {Robotics and Computer-Integrated Manufacturing}, abstract = {The transition towards renewable electricity provides opportunities for manufacturing companies to save electricity costs through participating in demand response programs. End-to-end implementation of demand response systems focusing on manufacturing power consumers is still challenging due to multiple stakeholders and subsystems that generate a heterogeneous and large amount of data. This work develops an approach utilizing artificial intelligence for a demand response system that optimizes industrial consumers’ and prosumers’ production-related electricity costs according to time-variable electricity tariffs. It also proposes a semantic middleware architecture that utilizes an ontology as the semantic integration model for handling heterogeneous data models between the system's modules. This paper reports on developing and evaluating multiple machine learning models for power generation forecasting and load prediction, and also mixed-integer linear programming as well as reinforcement learning for production optimization considering dynamic electricity pricing represented as Green Electricity Index (GEI). The experiments show that the hybrid auto-regressive long-short-term-memory model performs best for solar and convolutional neural networks for wind power generation forecasting. Random forest, k-nearest neighbors, ridge, and gradient-boosting regression models perform best in load prediction in the considered use cases. Furthermore, this research found that the reinforcement-learning-based approach can provide generic and scalable solutions for complex and dynamic production environments. Additionally, this paper presents the validation of the developed system in the German industrial environment, involving a utility company and two small to medium-sized manufacturing companies. It shows that the developed system benefits the manufacturing company that implements fine-grained process scheduling most due to its flexible rescheduling capacities.}, keywords = {artificial intelligence, Artificial neural network, machine learning, Ontology, production scheduling, reinforcement learning}, pubstate = {forthcoming}, tppubtype = {article} } The transition towards renewable electricity provides opportunities for manufacturing companies to save electricity costs through participating in demand response programs. End-to-end implementation of demand response systems focusing on manufacturing power consumers is still challenging due to multiple stakeholders and subsystems that generate a heterogeneous and large amount of data. This work develops an approach utilizing artificial intelligence for a demand response system that optimizes industrial consumers’ and prosumers’ production-related electricity costs according to time-variable electricity tariffs. It also proposes a semantic middleware architecture that utilizes an ontology as the semantic integration model for handling heterogeneous data models between the system's modules. This paper reports on developing and evaluating multiple machine learning models for power generation forecasting and load prediction, and also mixed-integer linear programming as well as reinforcement learning for production optimization considering dynamic electricity pricing represented as Green Electricity Index (GEI). The experiments show that the hybrid auto-regressive long-short-term-memory model performs best for solar and convolutional neural networks for wind power generation forecasting. Random forest, k-nearest neighbors, ridge, and gradient-boosting regression models perform best in load prediction in the considered use cases. Furthermore, this research found that the reinforcement-learning-based approach can provide generic and scalable solutions for complex and dynamic production environments. Additionally, this paper presents the validation of the developed system in the German industrial environment, involving a utility company and two small to medium-sized manufacturing companies. It shows that the developed system benefits the manufacturing company that implements fine-grained process scheduling most due to its flexible rescheduling capacities. |
Pidikiti Vamsi Sai; Vijaya, Annas; Fatahi Valilai Omid; Wicaksono Hendro An Ontology Model to Facilitate the Semantic Interoperability in Assessing the Circular Economy Performance of the Automotive Industry Journal Article Forthcoming Procedia CIRP, Forthcoming. Abstract | BibTeX | Tags: automotive industry, circular economy, Ontology, semantic interoperability @article{Pidikiti2023, title = {An Ontology Model to Facilitate the Semantic Interoperability in Assessing the Circular Economy Performance of the Automotive Industry}, author = {Pidikiti, Vamsi Sai; Vijaya, Annas; Fatahi Valilai, Omid; Wicaksono, Hendro}, year = {2023}, date = {2023-10-24}, journal = {Procedia CIRP}, abstract = {Circular economy (CE) focuses on maintaining the value of goods and materials as long as possible, reducing waste and resource usage, and keeping resources within the economy when a product has reached the end of its life. Products and materials have to be utilized many times to produce additional value. In the automotive industry, CE involves processes throughout the value chain comprising multiple dimensions such as energy, materials, lifetime, and utilization. The CE performance of the automotive industry can be measured using key performance indicators (KPIs) from those dimensions. However, since multiple stakeholders are involved throughout the automotive product lifecycle and value chain, calculating KPIs requires data from heterogeneous sources. Thus, a non-uniform understanding of the KPIs among those stakeholders may arise due to a lack of explicit semantic description, including the related assessed CE scenarios, data providers, and data sources. Meanwhile, various sectors have used ontologies to facilitate a common understanding of information structure among systems and organizations. Our paper presents an ontology-based model that enables sharing a common understanding of KPIs used to assess CE performance in the automotive industry. We identify the indicators, the corresponding data requirements, and sources. In this paper, we present the ontology model describing the semantics of data required for the indicators. We also show the deployment model illustrating the implementation of the ontology model in the CE performance assessment phases.}, keywords = {automotive industry, circular economy, Ontology, semantic interoperability}, pubstate = {forthcoming}, tppubtype = {article} } Circular economy (CE) focuses on maintaining the value of goods and materials as long as possible, reducing waste and resource usage, and keeping resources within the economy when a product has reached the end of its life. Products and materials have to be utilized many times to produce additional value. In the automotive industry, CE involves processes throughout the value chain comprising multiple dimensions such as energy, materials, lifetime, and utilization. The CE performance of the automotive industry can be measured using key performance indicators (KPIs) from those dimensions. However, since multiple stakeholders are involved throughout the automotive product lifecycle and value chain, calculating KPIs requires data from heterogeneous sources. Thus, a non-uniform understanding of the KPIs among those stakeholders may arise due to a lack of explicit semantic description, including the related assessed CE scenarios, data providers, and data sources. Meanwhile, various sectors have used ontologies to facilitate a common understanding of information structure among systems and organizations. Our paper presents an ontology-based model that enables sharing a common understanding of KPIs used to assess CE performance in the automotive industry. We identify the indicators, the corresponding data requirements, and sources. In this paper, we present the ontology model describing the semantics of data required for the indicators. We also show the deployment model illustrating the implementation of the ontology model in the CE performance assessment phases. |
2022 |
Wicaksono Hendro; Yuce, Baris; McGlinn Kris; Calli Ozum Smart Cities and Buildings Book Chapter Chapter Smart cities and buildings, pp. 239-263, CRC Press, 1st Edition, 2022, ISBN: 9781003204381. Abstract | Links | BibTeX | Tags: energy efficiency, machine learning, Ontology, smart cities, smart energy, sustainability @inbook{Wicaksono2022b, title = {Smart Cities and Buildings}, author = {Wicaksono, Hendro; Yuce, Baris; McGlinn, Kris; Calli, Ozum}, url = {https://www.taylorfrancis.com/chapters/edit/10.1201/9781003204381-13/smart-cities-buildings-hendro-wicaksono-baris-yuce-kris-mcglinn-ozum-calli}, isbn = {9781003204381}, year = {2022}, date = {2022-05-01}, pages = {239-263}, publisher = {CRC Press}, edition = {1st Edition}, chapter = {Smart cities and buildings}, abstract = {Smart buildings function within the wider context of the smart city, which itself must function within the wider energy and transport (smart) grids. It is essential therefore that smart buildings be integrated into this wider context. This requires intelligent approaches for managing and coordinating the diverse range of processes and technologies involved and a move towards a “digital infrastructure” which can transform how these smart environments operate and can be monitored but, more importantly, can circumvent the constraints of physical infrastructure through the capacity of data centres or the capacity of available communication pipes. This chapter explores the concept of the smart city, and the role that smart buildings, smart energy grids and smart transportation takes within, with a particular emphasis on the state of art with respect to the integration of data across these different domains, from the micro to the macro, from building sensors to smart grids. It explores different data analytics approaches, and it does this with reference to specific use cases, focusing on techniques in the main application areas along with relevant implemented examples while highlighting some of the key challenges currently faced and outlining future pathways for the sector. }, keywords = {energy efficiency, machine learning, Ontology, smart cities, smart energy, sustainability}, pubstate = {published}, tppubtype = {inbook} } Smart buildings function within the wider context of the smart city, which itself must function within the wider energy and transport (smart) grids. It is essential therefore that smart buildings be integrated into this wider context. This requires intelligent approaches for managing and coordinating the diverse range of processes and technologies involved and a move towards a “digital infrastructure” which can transform how these smart environments operate and can be monitored but, more importantly, can circumvent the constraints of physical infrastructure through the capacity of data centres or the capacity of available communication pipes. This chapter explores the concept of the smart city, and the role that smart buildings, smart energy grids and smart transportation takes within, with a particular emphasis on the state of art with respect to the integration of data across these different domains, from the micro to the macro, from building sensors to smart grids. It explores different data analytics approaches, and it does this with reference to specific use cases, focusing on techniques in the main application areas along with relevant implemented examples while highlighting some of the key challenges currently faced and outlining future pathways for the sector. |
2021 |
Wicaksono Hendro; Boroukhian, Tina; Bashyal Atit A Demand-Response System for Sustainable Manufacturing Using Linked Data and Machine Learning Book Chapter Freitag, Michael ; Kotzab, Herbert ; Megow, Nicole (Ed.): pp. 155-181, Springer, 2021, ISBN: 978-3-030-88662-2. Abstract | Links | BibTeX | Tags: artificial intelligence, causal analysis, causal inference, causal model, energy transition, linked data, machine learning, Ontology, project management, structural equation modelling, sustainability @inbook{Wicaksono2021, title = {A Demand-Response System for Sustainable Manufacturing Using Linked Data and Machine Learning}, author = {Wicaksono, Hendro; Boroukhian, Tina; Bashyal, Atit }, editor = {Freitag, Michael and Kotzab, Herbert and Megow, Nicole}, doi = {10.1007/978-3-030-88662-2_8}, isbn = {978-3-030-88662-2}, year = {2021}, date = {2021-12-31}, pages = {155-181}, publisher = {Springer}, abstract = {The spread of demand-response (DR) programs in Europe is a slow but steady process to optimize the use of renewable energy in different sectors including manufacturing. A demand-response program promotes changes of electricity consumption patterns at the end consumer side to match the availability of renewable energy sources through price changes or incentives. This research develops a system that aims to engage manufacturing power consumers through price- and incentive-based DR programs. The system works on data from heterogeneous systems at both supply and demand sides, which are linked through a semantic middleware, instead of centralized data integration. An ontology is used as the integration information model of the semantic middleware. This chapter explains the concept of constructing the ontology by utilizing relational database to ontology mapping techniques, reusing existing ontologies such as OpenADR, SSN, SAREF, etc., and applying ontology alignment methods. Machine learning approaches are developed to forecast both the power generated from renewable energy sources and the power demanded by manufacturing consumers based on their processes. The forecasts are the groundworks to calculate the dynamic electricity price introduced for the DR program. This chapter presents different neural network architectures and compares the experiment results. We compare the results of Deep Neural Network (DNN), Long Short-Term Memory Network (LSTM), Convolutional Neural Network (CNN), and Hybrid architectures. This chapter focuses on the initial phase of the research where we focus on the ontology development method and machine learning experiments using power generation datasets.}, keywords = {artificial intelligence, causal analysis, causal inference, causal model, energy transition, linked data, machine learning, Ontology, project management, structural equation modelling, sustainability}, pubstate = {published}, tppubtype = {inbook} } The spread of demand-response (DR) programs in Europe is a slow but steady process to optimize the use of renewable energy in different sectors including manufacturing. A demand-response program promotes changes of electricity consumption patterns at the end consumer side to match the availability of renewable energy sources through price changes or incentives. This research develops a system that aims to engage manufacturing power consumers through price- and incentive-based DR programs. The system works on data from heterogeneous systems at both supply and demand sides, which are linked through a semantic middleware, instead of centralized data integration. An ontology is used as the integration information model of the semantic middleware. This chapter explains the concept of constructing the ontology by utilizing relational database to ontology mapping techniques, reusing existing ontologies such as OpenADR, SSN, SAREF, etc., and applying ontology alignment methods. Machine learning approaches are developed to forecast both the power generated from renewable energy sources and the power demanded by manufacturing consumers based on their processes. The forecasts are the groundworks to calculate the dynamic electricity price introduced for the DR program. This chapter presents different neural network architectures and compares the experiment results. We compare the results of Deep Neural Network (DNN), Long Short-Term Memory Network (LSTM), Convolutional Neural Network (CNN), and Hybrid architectures. This chapter focuses on the initial phase of the research where we focus on the ontology development method and machine learning experiments using power generation datasets. |
2019 |
Schneider Georg Ferdinand; Wicaksono, Hendro; Ovtcharova Jivka Virtual engineering of cyber-physical automation systems: The case of control logic Journal Article Advanced Engineering Informatics, 39 , pp. 127-143, 2019, ISBN: 1474-0346. Abstract | Links | BibTeX | Tags: Cyber-physical systems, industry 4.0, Ontology, virtual engineering @article{Schneider2019, title = {Virtual engineering of cyber-physical automation systems: The case of control logic}, author = {Schneider, Georg Ferdinand; Wicaksono, Hendro; Ovtcharova, Jivka }, url = {https://www.sciencedirect.com/science/article/pii/S1474034618300740}, doi = {https://doi.org/10.1016/j.aei.2018.11.009.}, isbn = {1474-0346}, year = {2019}, date = {2019-01-31}, journal = {Advanced Engineering Informatics}, volume = {39}, pages = {127-143}, abstract = {Mastering the fusion of information and communication technologies with physical systems to cyber-physical automation systems is of main concern to engineers in the industrial automation domain. The engineering of these systems is challenging as their distributed nature and the heterogeneity of stakeholders and tools involved in their engineering contradict the need for the simultaneous engineering of their cyber and physical parts over their life cycle. This paper presents a novel approach based on the virtual engineering method, which provides support for the simultaneous engineering of the cyber and physical parts of automation systems. The approach extends and integrates the life cycle centered view mandated by current conceptual architectures and the digital twin paradigm with an integrated, iterative engineering method. The benefits of the approach are highlighted in a case study related to the engineering of the control logic of a cyber physical automation system originating from the process engineering domain. We describe for the first time a modular domain ontology, which formally describes the cyber and physical part of the system. We present cyber services built on top of the ontology layer, which allow to automatically verify different control logic types and simultaneously verify cyber and physical parts of the system in an incremental manner.}, keywords = {Cyber-physical systems, industry 4.0, Ontology, virtual engineering}, pubstate = {published}, tppubtype = {article} } Mastering the fusion of information and communication technologies with physical systems to cyber-physical automation systems is of main concern to engineers in the industrial automation domain. The engineering of these systems is challenging as their distributed nature and the heterogeneity of stakeholders and tools involved in their engineering contradict the need for the simultaneous engineering of their cyber and physical parts over their life cycle. This paper presents a novel approach based on the virtual engineering method, which provides support for the simultaneous engineering of the cyber and physical parts of automation systems. The approach extends and integrates the life cycle centered view mandated by current conceptual architectures and the digital twin paradigm with an integrated, iterative engineering method. The benefits of the approach are highlighted in a case study related to the engineering of the control logic of a cyber physical automation system originating from the process engineering domain. We describe for the first time a modular domain ontology, which formally describes the cyber and physical part of the system. We present cyber services built on top of the ontology layer, which allow to automatically verify different control logic types and simultaneously verify cyber and physical parts of the system in an incremental manner. |
2018 |
Howell Shaun; Wicaksono, Hendro; Yuce Baris; McGlinn Kris; Rezgui Yacine User Centered Neuro-Fuzzy Energy Management Through Semantic-Based Optimization Journal Article IEEE Transactions on Cybernetics, pp. 1-15, 2018, ISSN: 2168-2267. Abstract | Links | BibTeX | Tags: Artificial neural network, building energy management, data mining, Fuzzy logic, Genetic algorithm, middleware, Ontology, optimization, semantic web, WebGL @article{Howell2018, title = {User Centered Neuro-Fuzzy Energy Management Through Semantic-Based Optimization}, author = {Howell, Shaun; Wicaksono, Hendro; Yuce, Baris; McGlinn, Kris; Rezgui, Yacine}, url = {https://ieeexplore.ieee.org/document/8412214/}, doi = {10.1109/TCYB.2018.2839700}, issn = {2168-2267}, year = {2018}, date = {2018-07-19}, journal = {IEEE Transactions on Cybernetics}, pages = {1-15}, abstract = {This paper presents a cloud-based building energy management system, underpinned by semantic middleware, that integrates an enhanced sensor network with advanced analytics, accessible through an intuitive Web-based user interface. The proposed solution is described in terms of its three key layers: 1) user interface; 2) intelligence; and 3) interoperability. The system's intelligence is derived from simulation-based optimized rules, historical sensor data mining, and a fuzzy reasoner. The solution enables interoperability through a semantic knowledge base, which also contributes intelligence through reasoning and inference abilities, and which are enhanced through intelligent rules. Finally, building energy performance monitoring is delivered alongside optimized rule suggestions and a negotiation process in a 3-D Web-based interface using WebGL. The solution has been validated in a real pilot building to illustrate the strength of the approach, where it has shown over 25% energy savings. The relevance of this paper in the field is discussed, and it is argued that the proposed solution is mature enough for testing across further buildings.}, keywords = {Artificial neural network, building energy management, data mining, Fuzzy logic, Genetic algorithm, middleware, Ontology, optimization, semantic web, WebGL}, pubstate = {published}, tppubtype = {article} } This paper presents a cloud-based building energy management system, underpinned by semantic middleware, that integrates an enhanced sensor network with advanced analytics, accessible through an intuitive Web-based user interface. The proposed solution is described in terms of its three key layers: 1) user interface; 2) intelligence; and 3) interoperability. The system's intelligence is derived from simulation-based optimized rules, historical sensor data mining, and a fuzzy reasoner. The solution enables interoperability through a semantic knowledge base, which also contributes intelligence through reasoning and inference abilities, and which are enhanced through intelligent rules. Finally, building energy performance monitoring is delivered alongside optimized rule suggestions and a negotiation process in a 3-D Web-based interface using WebGL. The solution has been validated in a real pilot building to illustrate the strength of the approach, where it has shown over 25% energy savings. The relevance of this paper in the field is discussed, and it is argued that the proposed solution is mature enough for testing across further buildings. |
Wicaksono, Hendro Material Ontology: A use case in energy management Workshop Materials Ontology Workshop, European Comission, European Comission - Directorate General for Research & Innovation, Directorate D - Industrial Technologies 2018. BibTeX | Tags: linked data, Ontology, vocabulary @workshop{Wicaksono2018b, title = {Material Ontology: A use case in energy management}, author = {Wicaksono, Hendro}, year = {2018}, date = {2018-06-29}, booktitle = {Materials Ontology Workshop, European Comission}, organization = {European Comission - Directorate General for Research & Innovation, Directorate D - Industrial Technologies}, keywords = {linked data, Ontology, vocabulary}, pubstate = {published}, tppubtype = {workshop} } |
Wicaksono, Hendro Eine Plattform für die ganzheitliche Smart-Energie-Lösung in Smart-City Presentation 09.04.2018. Abstract | Links | BibTeX | Tags: Energy efficient building, energy management, linked data, Ontology, semantic data integration, smart cities @misc{Wicaksono2018, title = {Eine Plattform für die ganzheitliche Smart-Energie-Lösung in Smart-City}, author = {Hendro Wicaksono }, editor = {RENEXPO Forum, Augsburg, 2018}, url = {http://www.renexpo.de/fuer-besucher/forum.html}, year = {2018}, date = {2018-04-09}, abstract = {Vortragsinhalte: - Was ist die eine ganzheitliche Energiemanagement-Plattform? - Was kann man mit der Plattform machen? - Die Anwendungen auf der Plattform - Die Lösungsansatz und Erweiterbarkeit - Anwendungserfahrungen in Städten und Kommunen }, keywords = {Energy efficient building, energy management, linked data, Ontology, semantic data integration, smart cities}, pubstate = {published}, tppubtype = {presentation} } Vortragsinhalte: - Was ist die eine ganzheitliche Energiemanagement-Plattform? - Was kann man mit der Plattform machen? - Die Anwendungen auf der Plattform - Die Lösungsansatz und Erweiterbarkeit - Anwendungserfahrungen in Städten und Kommunen |
Haas, Klemens; Kappe, Simon; Siebert, Martin; Wicaksono, Hendro; Ovtcharova, Jivka Digital Assistance Based on an Ontology Driven Model of the IT-Systems Along the Product Lifecycle Book Chapter Debruyne, Christophe; Panetto, Hervé; Weichhart, Georg; Bollen, Peter; Ciuciu, Ioana; Vidal, Maria-Esther; Meersman, Robert (Ed.): Springer, 2018, ISBN: 978-3-319-73805-5. Abstract | Links | BibTeX | Tags: digitization, Ontology, product lifecycle management @inbook{Haas2018, title = {Digital Assistance Based on an Ontology Driven Model of the IT-Systems Along the Product Lifecycle}, author = {Klemens Haas and Simon Kappe and Martin Siebert and Hendro Wicaksono and Jivka Ovtcharova}, editor = {Christophe Debruyne and Hervé Panetto and Georg Weichhart and Peter Bollen and Ioana Ciuciu and Maria-Esther Vidal and Robert Meersman}, url = {https://www.springer.com/us/book/9783319738048}, doi = {10.1007/978-3-319-73805-5}, isbn = {978-3-319-73805-5}, year = {2018}, date = {2018-03-04}, publisher = {Springer}, abstract = {The market of Product Lifecycle Management (PLM) applications has changed into a complex landscape of heterogeneous systems in recent years. Consequently, it has become increasingly challenging for enterprises to identify a PLM application that meets their requirements and that can be successfully integrated into their existing IT systems. The approach presented in this paper aims at developing a decision supporting model of the IT system landscape that provides different analysis tools based on existing IT systems. The model which is expressed by an ontology is intended to represent data flows between the different IT applications in order to provide relevant information through requests and rules in further proceedings.}, keywords = {digitization, Ontology, product lifecycle management}, pubstate = {published}, tppubtype = {inbook} } The market of Product Lifecycle Management (PLM) applications has changed into a complex landscape of heterogeneous systems in recent years. Consequently, it has become increasingly challenging for enterprises to identify a PLM application that meets their requirements and that can be successfully integrated into their existing IT systems. The approach presented in this paper aims at developing a decision supporting model of the IT system landscape that provides different analysis tools based on existing IT systems. The model which is expressed by an ontology is intended to represent data flows between the different IT applications in order to provide relevant information through requests and rules in further proceedings. |
2017 |
McGlinn, Kris; Yuce, Baris; Wicaksono, Hendro; Howell, Shaun; Rezgui, Yacine Usability evaluation of a web-based tool for supporting holistic building energy management Journal Article Automation in Construction, 84 , pp. 154 - 165, 2017. Abstract | Links | BibTeX | Tags: Artificial neural network, BEMS, Fuzzy logic, Genetic algorithm, IFC, Information visualisation, Ontology @article{MCGLINN2017154, title = {Usability evaluation of a web-based tool for supporting holistic building energy management}, author = {Kris McGlinn and Baris Yuce and Hendro Wicaksono and Shaun Howell and Yacine Rezgui}, url = {https://www.sciencedirect.com/science/article/pii/S0926580516303545}, doi = {https://doi.org/10.1016/j.autcon.2017.08.033}, year = {2017}, date = {2017-03-31}, journal = {Automation in Construction}, volume = {84}, pages = {154 - 165}, abstract = {This paper presents the evaluation of the level of usability of an intelligent monitoring and control interface for energy efficient management of public buildings, called BuildVis, which forms part of a Building Energy Management System (BEMS.) The BEMS ‘intelligence’ is derived from an intelligent algorithm component which brings together ANN-GA rule generation, a fuzzy rule selection engine, and a semantic knowledge base. The knowledge base makes use of linked data and an integrated ontology to uplift heterogeneous data sources relevant to building energy consumption. The developed ontology is based upon the Industry Foundation Classes (IFC), which is a Building Information Modelling (BIM) standard and consists of two different types of rule model to control and manage the buildings adaptively. The populated rules are a mix of an intelligent rule generation approach using Artificial Neural Network (ANN) and Genetic Algorithms (GA), and also data mining rules using Decision Tree techniques on historical data. The resulting rules are triggered by the intelligent controller, which processes available sensor measurements in the building. This generates ‘suggestions’ which are presented to the Facility Manager (FM) on the BuildVis web-based interface. BuildVis uses HTML5 innovations to visualise a 3D interactive model of the building that is accessible over a wide range of desktop and mobile platforms. The suggestions are presented on a zone by zone basis, alerting them to potential energy saving actions. As the usability of the system is seen as a key determinate to success, the paper evaluates the level of usability for both a set of technical users and also the FMs for five European buildings, providing analysis and lessons learned from the approach taken.}, keywords = {Artificial neural network, BEMS, Fuzzy logic, Genetic algorithm, IFC, Information visualisation, Ontology}, pubstate = {published}, tppubtype = {article} } This paper presents the evaluation of the level of usability of an intelligent monitoring and control interface for energy efficient management of public buildings, called BuildVis, which forms part of a Building Energy Management System (BEMS.) The BEMS ‘intelligence’ is derived from an intelligent algorithm component which brings together ANN-GA rule generation, a fuzzy rule selection engine, and a semantic knowledge base. The knowledge base makes use of linked data and an integrated ontology to uplift heterogeneous data sources relevant to building energy consumption. The developed ontology is based upon the Industry Foundation Classes (IFC), which is a Building Information Modelling (BIM) standard and consists of two different types of rule model to control and manage the buildings adaptively. The populated rules are a mix of an intelligent rule generation approach using Artificial Neural Network (ANN) and Genetic Algorithms (GA), and also data mining rules using Decision Tree techniques on historical data. The resulting rules are triggered by the intelligent controller, which processes available sensor measurements in the building. This generates ‘suggestions’ which are presented to the Facility Manager (FM) on the BuildVis web-based interface. BuildVis uses HTML5 innovations to visualise a 3D interactive model of the building that is accessible over a wide range of desktop and mobile platforms. The suggestions are presented on a zone by zone basis, alerting them to potential energy saving actions. As the usability of the system is seen as a key determinate to success, the paper evaluates the level of usability for both a set of technical users and also the FMs for five European buildings, providing analysis and lessons learned from the approach taken. |
2016 |
McGlinn, Kris; Wiese, Matthias; Wicaksono, Hendro Towards a shared use case repository – the SWIMing initiative started in the framework of the EU H2020 R&DI programme Inproceedings Proceedings of the 33rd International Conference of CIB W78, Digital library of construction informatics and information technology in civil engineering and construction, 2016. Abstract | Links | BibTeX | Tags: building information modelling, Energy efficient building, linked data, Ontology @inproceedings{McGlinn2016, title = {Towards a shared use case repository – the SWIMing initiative started in the framework of the EU H2020 R&DI programme}, author = {Kris McGlinn and Matthias Wiese and Hendro Wicaksono}, url = {http://itc.scix.net/data/works/att/w78-2016-paper-010.pdf}, year = {2016}, date = {2016-11-02}, booktitle = {Proceedings of the 33rd International Conference of CIB W78}, publisher = {Digital library of construction informatics and information technology in civil engineering and construction}, abstract = {Data exchange and data sharing are one of the big challenges in the Architecture, Engineering, and Construction (AEC) industry and energy efficient building (EeB) domain. BIM open standards and lately the use of Semantic Web technologies provide a sound basis to implement exchange requirements derived from typical EeB use cases. However, the challenge remains to identify what models are available and how to align these with a particular use cases data requirements. This paper focuses on the application of an established methodology (Information Delivery Manual) adapted for the EeB domain and the application of the BIM*Q tool, which applies this methodology. The paper proposes to build-up a shared use case repository that collects detailed data Exchange Requirements as well as alignments to existing models to support projects when developing new use cases in the difficult task of aligning data requirements with models and standards. }, keywords = {building information modelling, Energy efficient building, linked data, Ontology}, pubstate = {published}, tppubtype = {inproceedings} } Data exchange and data sharing are one of the big challenges in the Architecture, Engineering, and Construction (AEC) industry and energy efficient building (EeB) domain. BIM open standards and lately the use of Semantic Web technologies provide a sound basis to implement exchange requirements derived from typical EeB use cases. However, the challenge remains to identify what models are available and how to align these with a particular use cases data requirements. This paper focuses on the application of an established methodology (Information Delivery Manual) adapted for the EeB domain and the application of the BIM*Q tool, which applies this methodology. The paper proposes to build-up a shared use case repository that collects detailed data Exchange Requirements as well as alignments to existing models to support projects when developing new use cases in the difficult task of aligning data requirements with models and standards. |
2015 |
Wicaksono, Hendro Intelligent Information model for improving energy efficiency in building operational phase Conference Jahrestagung des KIT-Zentrums Energie, EST Energy Science Technology, 2015. BibTeX | Tags: building information modelling, Energy efficient building, Ontology, ontology engineering, ontology population @conference{Wicaksono2015, title = {Intelligent Information model for improving energy efficiency in building operational phase}, author = {Hendro Wicaksono}, year = {2015}, date = {2015-01-20}, booktitle = {Jahrestagung des KIT-Zentrums Energie, EST Energy Science Technology}, keywords = {building information modelling, Energy efficient building, Ontology, ontology engineering, ontology population}, pubstate = {published}, tppubtype = {conference} } |
2014 |
Wicaksono, Hendro; Jost, Fabian; Rogalski, Sven; Ovtcharova, Jivka Energy efficiency evaluation in manufacturing through an ontology-represented knowledge base Journal Article Intelligent Systems in Accounting, Finance and Management, 21 (1), pp. 59-69, 2014. Abstract | Links | BibTeX | Tags: energy efficiency, knowledge base, knowledge management, manufacturing, Ontology @article{Wicaksono2014, title = {Energy efficiency evaluation in manufacturing through an ontology-represented knowledge base}, author = {Hendro Wicaksono and Fabian Jost and Sven Rogalski and Jivka Ovtcharova}, url = {http://onlinelibrary.wiley.com/doi/10.1002/isaf.1347/abstract}, doi = {10.1002/isaf.1347}, year = {2014}, date = {2014-04-01}, journal = {Intelligent Systems in Accounting, Finance and Management}, volume = {21}, number = {1}, pages = {59-69}, abstract = {Improving energy efficiency in a manufacturing company through an energy management system requires active participation of different stakeholders and involvement of different organizational entities and technical processes. Interoperability of stakeholders and entities is the key factor to achieve a successful implementation of an energy management system. Researchers have been developing approaches in applying ontologies to address interoperability issues among humans as well as machines. Ontologies have also been used for knowledge representation in different domains, such as energy management and manufacturing. In recent years, researchers have developed knowledge-based intelligent energy management systems in buildings, especially households, which use ontologies for knowledge representation. In the manufacturing domain, ontologies have been used for knowledge management in order to provide a common formal understanding between the stakeholders, who have different background knowledge. This paper proposes an approach to apply ontology to allow knowledge-based energy efficiency evaluation in manufacturing companies. The ontology provides a formal knowledge representation that addresses the interoperability issues due to different human stakeholders as well as machines involved in the energy management system of the company. This paper also describes the methods used to construct and to process the ontology. }, keywords = {energy efficiency, knowledge base, knowledge management, manufacturing, Ontology}, pubstate = {published}, tppubtype = {article} } Improving energy efficiency in a manufacturing company through an energy management system requires active participation of different stakeholders and involvement of different organizational entities and technical processes. Interoperability of stakeholders and entities is the key factor to achieve a successful implementation of an energy management system. Researchers have been developing approaches in applying ontologies to address interoperability issues among humans as well as machines. Ontologies have also been used for knowledge representation in different domains, such as energy management and manufacturing. In recent years, researchers have developed knowledge-based intelligent energy management systems in buildings, especially households, which use ontologies for knowledge representation. In the manufacturing domain, ontologies have been used for knowledge management in order to provide a common formal understanding between the stakeholders, who have different background knowledge. This paper proposes an approach to apply ontology to allow knowledge-based energy efficiency evaluation in manufacturing companies. The ontology provides a formal knowledge representation that addresses the interoperability issues due to different human stakeholders as well as machines involved in the energy management system of the company. This paper also describes the methods used to construct and to process the ontology. |
Anzaldi, Gabriel; Corchero, Aitor; Wicaksono, Hendro; McGlinn, Kris; Gardelan, Anton; Dibley, Michael J Knoholem: Knowledge-Based Energy Management for Public Buildings Through Holistic Information Modeling and 3D Visualization Inproceedings Gonzalez, Ignacio (Ed.): International Technology Robotics Applications, pp. 47-56, Springer International Publishing Switzerland Springer International Publishing, Switzerland, 2014, ISBN: 978-3-319-02332-8. Abstract | Links | BibTeX | Tags: building energy management, building information modelling, knowledge based energy management, Ontology @inproceedings{Anzaldi2014, title = {Knoholem: Knowledge-Based Energy Management for Public Buildings Through Holistic Information Modeling and 3D Visualization}, author = {Gabriel Anzaldi and Aitor Corchero and Hendro Wicaksono and Kris McGlinn and Anton Gardelan and Michael J. Dibley}, editor = {Ignacio Gonzalez}, url = {http://www.springer.com/de/book/9783319023311}, doi = {10.1007/978-3-319-02332-8}, isbn = {978-3-319-02332-8}, year = {2014}, date = {2014-01-09}, booktitle = {International Technology Robotics Applications}, volume = {70}, number = {1}, pages = {47-56}, publisher = {Springer International Publishing}, address = {Switzerland}, organization = {Springer International Publishing Switzerland}, abstract = {The mismanagement of energy in public buildings is related with the continuous growing up of energy consumption. Energy efficiency concept is one of the main awareness in the current society. The main contribution of the paper is focused on describing intelligent energy management architecture capable of intelligent generation of recommendations taking into account infrastructure behavior (building), user behavior (occupants of the building) and holistic techniques (recommendations and knowledge from other buildings). This article describes an approach developed under European Union project called KnoHolEM. Project solution is characterized by:(1) Support buildings in (near-) real-time energy monitoring through smart metering;(2) Knowledge-based development of intelligent energy management solutions, supporting interoperability between heterogeneous systems and assisted by algorithms for energy consumption.}, keywords = {building energy management, building information modelling, knowledge based energy management, Ontology}, pubstate = {published}, tppubtype = {inproceedings} } The mismanagement of energy in public buildings is related with the continuous growing up of energy consumption. Energy efficiency concept is one of the main awareness in the current society. The main contribution of the paper is focused on describing intelligent energy management architecture capable of intelligent generation of recommendations taking into account infrastructure behavior (building), user behavior (occupants of the building) and holistic techniques (recommendations and knowledge from other buildings). This article describes an approach developed under European Union project called KnoHolEM. Project solution is characterized by:(1) Support buildings in (near-) real-time energy monitoring through smart metering;(2) Knowledge-based development of intelligent energy management solutions, supporting interoperability between heterogeneous systems and assisted by algorithms for energy consumption. |
2013 |
Häfner, Polina; Häfner, Victor; Wicaksono, Hendro; Ovtcharova, Jivka Semi-automated ontology population from building construction drawings Inproceedings 5th International Conference on Knowledge Engineering and Ontology Development, KEOD 2013; Vilamoura, Algarve; Portugal; 19 September 2013 - 22 September 2013, pp. 379-386, INSTICC, Lissabon, Lissabon, 2013, ISBN: 978-989856581-5. Abstract | Links | BibTeX | Tags: Ontology, ontology population, pattern recognition @inproceedings{Häfner2013, title = {Semi-automated ontology population from building construction drawings}, author = {Polina Häfner and Victor Häfner and Hendro Wicaksono and Jivka Ovtcharova}, url = {https://publikationen.bibliothek.kit.edu/1000040883}, isbn = {978-989856581-5}, year = {2013}, date = {2013-09-06}, booktitle = {5th International Conference on Knowledge Engineering and Ontology Development, KEOD 2013; Vilamoura, Algarve; Portugal; 19 September 2013 - 22 September 2013}, pages = {379-386}, publisher = {INSTICC, Lissabon}, address = {Lissabon}, abstract = {Ontologies have been applied as knowledge representation in different domains including intelligent building management. One of the challenges in using ontologies is the population with building specific information, such as the building elements and the energy consuming devices.The population usually has to be done manually, thus it requires an extensive work. This is due to the lack of semantic information in the existing building drawings. The building drawings only contain geometrical information. However, it is possible that we understand the semantics of the drawings, if we have the knowledge in interpreting the semantics of the symbols, shapes and other geometric information. In this paper we introduce a tool to extract the semantic information from CAD drawings and populate the ontology using the extracted semantic information in a semi-automatic way. We extract the drawing primitives from CAD files, perform the pattern matching and classification algorithms to extract the semantic information and enrich them with geometric data like object world positions and bounding boxes. The resulting semantic information is then mapped to the corresponding ontology classes of a T-Box ontology. Finally individuals of the corresponding classes are created to populate the ontology.}, keywords = {Ontology, ontology population, pattern recognition}, pubstate = {published}, tppubtype = {inproceedings} } Ontologies have been applied as knowledge representation in different domains including intelligent building management. One of the challenges in using ontologies is the population with building specific information, such as the building elements and the energy consuming devices.The population usually has to be done manually, thus it requires an extensive work. This is due to the lack of semantic information in the existing building drawings. The building drawings only contain geometrical information. However, it is possible that we understand the semantics of the drawings, if we have the knowledge in interpreting the semantics of the symbols, shapes and other geometric information. In this paper we introduce a tool to extract the semantic information from CAD drawings and populate the ontology using the extracted semantic information in a semi-automatic way. We extract the drawing primitives from CAD files, perform the pattern matching and classification algorithms to extract the semantic information and enrich them with geometric data like object world positions and bounding boxes. The resulting semantic information is then mapped to the corresponding ontology classes of a T-Box ontology. Finally individuals of the corresponding classes are created to populate the ontology. |
Wicaksono, Hendro; Dobreva, Preslava Intelligent knowledge generation for energy management in buildings Inproceedings Breh, Wolfgang (Ed.): Impulse für die Zukunft der Energie : Wissenschaftliche Beiträge des KIT zur 2. Jahrestagung des KIT-Zentrums Energie. Doktorandensymposium, 13.06.2013, pp. 73-78, Zentrum Energie (Karlsruhe) KIT Scientific Publishing, Karlsruhe, 2013, ISBN: 978-3-7315-0097-1. Abstract | Links | BibTeX | Tags: building energy management, energy efficiency, energy performance indicator, knowledge management, Ontology, ontology engineering, ontology population @inproceedings{Wicaksono2013e, title = {Intelligent knowledge generation for energy management in buildings }, author = {Hendro Wicaksono and Preslava Dobreva}, editor = {Wolfgang Breh}, url = {https://publikationen.bibliothek.kit.edu/1000036425/2888062}, doi = {10.5445/KSP/1000036425}, isbn = {978-3-7315-0097-1}, year = {2013}, date = {2013-06-13}, booktitle = {Impulse für die Zukunft der Energie : Wissenschaftliche Beiträge des KIT zur 2. Jahrestagung des KIT-Zentrums Energie. Doktorandensymposium, 13.06.2013}, pages = {73-78}, publisher = {KIT Scientific Publishing}, address = {Karlsruhe}, organization = {Zentrum Energie (Karlsruhe)}, abstract = {Energy consumption in buildings is currently representing a significant percentage of the whole energy consumption on earth. The EU responds to this trend by dedicated policy making and financially supporting research activities in the field of efficiency improvement without decreasing inhabitants comfort. This paper describes a method for intelligent energy management in public buildings, going behind classical data-driven approaches commonly used by BMS solutions. An ontology based approach for energy analysis offering extended concept for automated population, self-learning mechanisms and integration to other systems. Furthermore we demonstrate how the energy performance analysis is improved using the ontology based approach. }, keywords = {building energy management, energy efficiency, energy performance indicator, knowledge management, Ontology, ontology engineering, ontology population}, pubstate = {published}, tppubtype = {inproceedings} } Energy consumption in buildings is currently representing a significant percentage of the whole energy consumption on earth. The EU responds to this trend by dedicated policy making and financially supporting research activities in the field of efficiency improvement without decreasing inhabitants comfort. This paper describes a method for intelligent energy management in public buildings, going behind classical data-driven approaches commonly used by BMS solutions. An ontology based approach for energy analysis offering extended concept for automated population, self-learning mechanisms and integration to other systems. Furthermore we demonstrate how the energy performance analysis is improved using the ontology based approach. |
Wicaksono, Hendro An Integrated Method for ICT Supported Energy Efficiency Improvement in Manufacturing Inproceedings Breh, Wolfgang (Ed.): Impulse für die Zukunft der Energie : Wissenschaftliche Beiträge des KIT zur 2. Jahrestagung des KIT-Zentrums Energie. Doktorandensymposium, 13.06.2013, pp. 67-72, Zentrum Energie (Karlsruhe) KIT Scientific Publishing, Karlsruhe, 2013, ISBN: 978-3-7315-0097-1. Abstract | Links | BibTeX | Tags: energy efficiency, energy management, energy performance indicator, knowledge based energy management, knowledge management, Ontology @inproceedings{Wicaksono2013f, title = {An Integrated Method for ICT Supported Energy Efficiency Improvement in Manufacturing }, author = {Hendro Wicaksono}, editor = {Wolfgang Breh}, url = {http://digbib.ubka.uni-karlsruhe.de/volltexte/1000036425}, doi = {10.5445/KSP/1000036425}, isbn = {978-3-7315-0097-1}, year = {2013}, date = {2013-06-13}, booktitle = {Impulse für die Zukunft der Energie : Wissenschaftliche Beiträge des KIT zur 2. Jahrestagung des KIT-Zentrums Energie. Doktorandensymposium, 13.06.2013}, pages = {67-72}, publisher = {KIT Scientific Publishing}, address = {Karlsruhe}, organization = {Zentrum Energie (Karlsruhe)}, abstract = {Energy and resource efficiency have been developing into one of the most crucial issues of the 21st century. Manufacturers are demanded to improve their energy efficiency by regulating their energy consumption. Energy management system helps the manufacturers to improve their energy efficiency. Most of the manufacturing companies face problems in implementing the energy management standards mostly due to the lack of ICT support, especially to help to evaluate the current energy performance. This paper presents an ICT based holistic approach to help manufacturing companies in the implementation of energy management system. The approach uses an ontological knowledge base containing the structures and rules representing best practices as reference of energy efficiency to support the qualitative evaluation. In the approach, we also develop measurement figures called Energy Performance Indicators (EPI) to determine the energy efficiency degrees in different resource units and organizational parts of the company. The knowledge management approach and EPI support quantitative and qualitative energy efficiency evaluation of manufacturing operations. Furthermore this paper introduces the method to improve the energy efficiency in production process planning.}, keywords = {energy efficiency, energy management, energy performance indicator, knowledge based energy management, knowledge management, Ontology}, pubstate = {published}, tppubtype = {inproceedings} } Energy and resource efficiency have been developing into one of the most crucial issues of the 21st century. Manufacturers are demanded to improve their energy efficiency by regulating their energy consumption. Energy management system helps the manufacturers to improve their energy efficiency. Most of the manufacturing companies face problems in implementing the energy management standards mostly due to the lack of ICT support, especially to help to evaluate the current energy performance. This paper presents an ICT based holistic approach to help manufacturing companies in the implementation of energy management system. The approach uses an ontological knowledge base containing the structures and rules representing best practices as reference of energy efficiency to support the qualitative evaluation. In the approach, we also develop measurement figures called Energy Performance Indicators (EPI) to determine the energy efficiency degrees in different resource units and organizational parts of the company. The knowledge management approach and EPI support quantitative and qualitative energy efficiency evaluation of manufacturing operations. Furthermore this paper introduces the method to improve the energy efficiency in production process planning. |
2012 |
Wicaksono, Hendro; Rogalski, Sven; Ovtcharova, Jivka Ontology Driven Approach for Intelligent Energy Management in Discrete Manufacturing Inproceedings Proceedings of the International Conference on Knowledge Engineering and Ontology Development, pp. 108-114, INSTICC SciTePress, 2012, ISBN: 978-989-8565-30-3. Abstract | Links | BibTeX | Tags: energy efficiency, energy management, knowledge acquisition, knowledge capturing, machine learning, manufacturing, Ontology @inproceedings{Wicaksono2012c, title = {Ontology Driven Approach for Intelligent Energy Management in Discrete Manufacturing}, author = {Hendro Wicaksono and Sven Rogalski and Jivka Ovtcharova}, url = {http://www.scitepress.org/PublicationsDetail.aspx?ID=VdNAwL50fGw=&t=1}, doi = {10.5220/0004141601080114}, isbn = {978-989-8565-30-3}, year = {2012}, date = {2012-10-07}, booktitle = {Proceedings of the International Conference on Knowledge Engineering and Ontology Development}, volume = {1}, pages = {108-114}, publisher = {SciTePress}, organization = {INSTICC}, abstract = {In recent years ontologies have been used for knowledge representation in different domains, such as energy management and manufacturing. Researchers have developed approaches in applying ontologies for intelligent energy management in households. In the manufacturing domain, ontologies have been used for knowledge management in order to provide a common formal understanding between the stakeholders, who have different background knowledge. Energy management in a manufacturing company involves different organizational entities and technical processes. This paper proposes an approach to applying ontology for intelligent energy management in discrete manufacturing companies. The ontology provides a formal knowledge representation that is accessible by different human stakeholders as well as machines in the company. This paper also demonstrates the methods used to construct and to process the ontology.}, keywords = {energy efficiency, energy management, knowledge acquisition, knowledge capturing, machine learning, manufacturing, Ontology}, pubstate = {published}, tppubtype = {inproceedings} } In recent years ontologies have been used for knowledge representation in different domains, such as energy management and manufacturing. Researchers have developed approaches in applying ontologies for intelligent energy management in households. In the manufacturing domain, ontologies have been used for knowledge management in order to provide a common formal understanding between the stakeholders, who have different background knowledge. Energy management in a manufacturing company involves different organizational entities and technical processes. This paper proposes an approach to applying ontology for intelligent energy management in discrete manufacturing companies. The ontology provides a formal knowledge representation that is accessible by different human stakeholders as well as machines in the company. This paper also demonstrates the methods used to construct and to process the ontology. |
Wicaksono, Hendro; Rogalski, Sven; Ovtcharova, Jivka Knowledge Management Approach to improve Energy Efficiency in Small Medium Enterprises Journal Article 2012. Abstract | BibTeX | Tags: data mining, energy efficiency, energy management, knowledge management, machine learning, Ontology @article{Wicaksono2012d, title = {Knowledge Management Approach to improve Energy Efficiency in Small Medium Enterprises}, author = {Hendro Wicaksono and Sven Rogalski and Jivka Ovtcharova}, year = {2012}, date = {2012-09-13}, abstract = {Energy efficiency in accordance with the economization of production costs is an important com-petitive factor in the energy-intensive industry. Energy management is a way to achieve this, but most of the manufacturing companies face problems in implementing it due to the lack of standard-izations in their operation portfolio. Energy related information are managed separately and in an unstructured manner. Managements have low visibility to the usage of energy in the operation due to the knowledge gap between managers and operators. Operators are often not aware whether their activities and decisions create excessive energy usage because of the different knowledge among them. This paper introduces a novel method based on knowledge management approach to address the problem using an ontology knowledge base and semi-automatic knowledge acquisition using data mining technique. In this paper the application of the approach in a small medium sized stain-less steel manufacturer will be presented. }, keywords = {data mining, energy efficiency, energy management, knowledge management, machine learning, Ontology}, pubstate = {published}, tppubtype = {article} } Energy efficiency in accordance with the economization of production costs is an important com-petitive factor in the energy-intensive industry. Energy management is a way to achieve this, but most of the manufacturing companies face problems in implementing it due to the lack of standard-izations in their operation portfolio. Energy related information are managed separately and in an unstructured manner. Managements have low visibility to the usage of energy in the operation due to the knowledge gap between managers and operators. Operators are often not aware whether their activities and decisions create excessive energy usage because of the different knowledge among them. This paper introduces a novel method based on knowledge management approach to address the problem using an ontology knowledge base and semi-automatic knowledge acquisition using data mining technique. In this paper the application of the approach in a small medium sized stain-less steel manufacturer will be presented. |
Wicaksono, Hendro; Aleksandrov, Kiril; Rogalski, Sven An Intelligent System for Improving Energy Efficiency in Building Using Ontology and Building Automation Systems Book Chapter Kongoli, Florian (Ed.): IntechOpen, 2012, ISBN: 978-953-51-0685-2. Links | BibTeX | Tags: building energy management, data mining, energy efficiency, machine learning, Ontology @inbook{Wicaksono2012, title = {An Intelligent System for Improving Energy Efficiency in Building Using Ontology and Building Automation Systems}, author = {Hendro Wicaksono and Kiril Aleksandrov and Sven Rogalski }, editor = {Florian Kongoli}, url = {https://www.intechopen.com/books/automation/an-intelligent-system-for-improving-energy-efficiency-in-building-using-ontology-and-building-automa}, doi = {10.5772/48006}, isbn = {978-953-51-0685-2}, year = {2012}, date = {2012-07-25}, publisher = {IntechOpen}, keywords = {building energy management, data mining, energy efficiency, machine learning, Ontology}, pubstate = {published}, tppubtype = {inbook} } |
2011 |
Schubert, Viktor; Aleksandrov, Kiril; Wicaksono, Hendro Von der Anforderung zum Design-Element - Modellierung der Produktstruktur mit dem Business Editor Book Chapter Franke, H J (Ed.): pp. 74-82, Shaker, Aachen, 2011. Links | BibTeX | Tags: Ontology, product configuration, product lifecycle management, requirement engineering @inbook{Schubert2011, title = {Von der Anforderung zum Design-Element - Modellierung der Produktstruktur mit dem Business Editor}, author = {Viktor Schubert and Kiril Aleksandrov and Hendro Wicaksono}, editor = {H. J. Franke}, url = {https://www.amazon.de/Anforderungsmanagement-kundenindividuelle-Produkte-L%C3%B6sungsans%C3%A4tze-Praxisorientierte/dp/384400517X}, doi = {978-3-8440-0517-2}, year = {2011}, date = {2011-11-01}, pages = {74-82}, publisher = {Shaker}, address = {Aachen}, keywords = {Ontology, product configuration, product lifecycle management, requirement engineering}, pubstate = {published}, tppubtype = {inbook} } |
Wicaksono, Hendro; Schubert, Viktor; Rogalski, Sven; Laydi, Youssef Ait; Ovtcharova, Jivka Ontology-driven Requirements Elicitation in Product Configuration Systems Book Chapter ElMaraghy, Hoda A (Ed.): pp. 63-67, Springer Berlin Heidelberg, Berlin, Heidelberg, 2011, ISBN: 978-3-642-23860-4. Abstract | Links | BibTeX | Tags: Ontology, product configuration, requirement engineering @inbook{Wicaksono2011, title = {Ontology-driven Requirements Elicitation in Product Configuration Systems}, author = {Hendro Wicaksono and Viktor Schubert and Sven Rogalski and Youssef Ait Laydi and Jivka Ovtcharova}, editor = {Hoda A. ElMaraghy}, url = {https://link.springer.com/chapter/10.1007/978-3-642-23860-4_10}, doi = {10.1007/978-3-642-23860-4_10}, isbn = {978-3-642-23860-4}, year = {2011}, date = {2011-09-15}, pages = {63-67}, publisher = {Springer Berlin Heidelberg}, address = {Berlin, Heidelberg}, abstract = {Producing timely and customer-oriented products is a key factor for manufacturers` success in competing in the recent global economic era. Customer needs are becoming more complex as a result of rapid changes in social, technical, and economic conditions. Manufacturers must provide more flexibility and individual customizations of the products. This is not an easy task due to the different perspectives between manufacturer and customer during the pre-contract phase. Product configuration systems are generally used to improve the quotation process. However, it is still difficult to provide customer satisfying products because customers often cannot clearly articulate their requirements. This paper introduces an approach that assists the customer in articulating their requirements and supports the manufacturer to capture the customer needs completely. We propose an intelligent product configuration system that harmonizes both customer and manufacturer perspectives, based on an ontology-based integrated knowledge model. We utilize ontology-based methods and techniques to develop a recommender system in the product configuration system to improve the requirements elicitation and to avoid requirement inconsistencies.}, keywords = {Ontology, product configuration, requirement engineering}, pubstate = {published}, tppubtype = {inbook} } Producing timely and customer-oriented products is a key factor for manufacturers` success in competing in the recent global economic era. Customer needs are becoming more complex as a result of rapid changes in social, technical, and economic conditions. Manufacturers must provide more flexibility and individual customizations of the products. This is not an easy task due to the different perspectives between manufacturer and customer during the pre-contract phase. Product configuration systems are generally used to improve the quotation process. However, it is still difficult to provide customer satisfying products because customers often cannot clearly articulate their requirements. This paper introduces an approach that assists the customer in articulating their requirements and supports the manufacturer to capture the customer needs completely. We propose an intelligent product configuration system that harmonizes both customer and manufacturer perspectives, based on an ontology-based integrated knowledge model. We utilize ontology-based methods and techniques to develop a recommender system in the product configuration system to improve the requirements elicitation and to avoid requirement inconsistencies. |
2010 |
Wicaksono, Hendro; Rogalski, Sven; Kusnady, Enrico Knowledge-based intelligent energy management using building automation system Inproceedings 2010 Conference Proceedings IPEC, IEEE, Singapore, Singapore, 2010, ISSN: 1947-1262. Abstract | Links | BibTeX | Tags: building automation system, data mining, energy management, knowledge-based system, machine learning, Ontology @inproceedings{Wicaksono2010b, title = {Knowledge-based intelligent energy management using building automation system}, author = {Hendro Wicaksono and Sven Rogalski and Enrico Kusnady}, url = {http://ieeexplore.ieee.org/document/5696994/}, doi = {10.1109/IPECON.2010.5696994}, issn = {1947-1262}, year = {2010}, date = {2010-10-29}, booktitle = {2010 Conference Proceedings IPEC}, publisher = {IEEE}, address = {Singapore, Singapore}, abstract = {The rise of energy needs and energy prices due to shortage of energy resources force companies and households to consume energy more efficiently. In recent years building automation systems are commonly used to allow better comfort for the occupants, and also to improve security in the building. This paper describes an approach for improving energy efficiency in private and business buildings by utilizing building automation systems. The goal of the approach is to provide an intelligent system that can help the occupants to monitor and control their energy consumption and also to notice their energy saving potentials. This paper also introduces method of knowledge-driven energy analysis that offers more intelligent mechanism as improvement of classical data-driven analysis, which is commonly used by existing energy analysis solutions.}, keywords = {building automation system, data mining, energy management, knowledge-based system, machine learning, Ontology}, pubstate = {published}, tppubtype = {inproceedings} } The rise of energy needs and energy prices due to shortage of energy resources force companies and households to consume energy more efficiently. In recent years building automation systems are commonly used to allow better comfort for the occupants, and also to improve security in the building. This paper describes an approach for improving energy efficiency in private and business buildings by utilizing building automation systems. The goal of the approach is to provide an intelligent system that can help the occupants to monitor and control their energy consumption and also to notice their energy saving potentials. This paper also introduces method of knowledge-driven energy analysis that offers more intelligent mechanism as improvement of classical data-driven analysis, which is commonly used by existing energy analysis solutions. |
Wicaksono, Hendro; Rogalski, Sven Ontology Supported Intelligent Energy Management System in Buildings Inproceedings Proceeding IEEE International Conference on Industrial Engineering and Business Management (ICIEBM), IEEE 2010. BibTeX | Tags: building automation system, data mining, machine learning, Ontology @inproceedings{Wicaksono2010c, title = {Ontology Supported Intelligent Energy Management System in Buildings}, author = {Hendro Wicaksono and Sven Rogalski}, year = {2010}, date = {2010-10-13}, booktitle = {Proceeding IEEE International Conference on Industrial Engineering and Business Management (ICIEBM)}, organization = {IEEE}, keywords = {building automation system, data mining, machine learning, Ontology}, pubstate = {published}, tppubtype = {inproceedings} } |
Schubert, Viktor; Wicaksono, Hendro; Rogalski, Sven Knowledge-based Product Configuration through Product Life Cycle Oriented Feedback-Driven Requirements Engineering Inproceedings Proceeding 20th International Conference Flexible Automation and Intelligent Manufacturing (FAIM), San Francisco, USA, 2010. BibTeX | Tags: case-based reasoning, data mining, machine learning, Ontology, product configuration, product lifecycle management @inproceedings{Schubert2010, title = {Knowledge-based Product Configuration through Product Life Cycle Oriented Feedback-Driven Requirements Engineering}, author = {Viktor Schubert and Hendro Wicaksono and Sven Rogalski}, year = {2010}, date = {2010-07-14}, booktitle = {Proceeding 20th International Conference Flexible Automation and Intelligent Manufacturing (FAIM), San Francisco, USA}, keywords = {case-based reasoning, data mining, machine learning, Ontology, product configuration, product lifecycle management}, pubstate = {published}, tppubtype = {inproceedings} } |