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 |
Aikenov Temirlan; Hidayat, Rahmat; Wicaksono Hendro Power consumption and process cost prediction of customized products using explainable AI Inproceedings Flexible Automation and Intelligent Manufacturing: Establishing Bridges for More Sustainable Manufacturing Systems., 2023. Abstract | Links | BibTeX | Tags: energy efficiency, explainable artificial intelligence, machine learning, sustainability, sustainable manuracturing, XAI @inproceedings{Aikenov2023, title = {Power consumption and process cost prediction of customized products using explainable AI}, author = {Aikenov, Temirlan; Hidayat, Rahmat; Wicaksono, Hendro}, url = {https://link.springer.com/chapter/10.1007/978-3-031-38165-2_135}, doi = {https://doi.org/10.1007/978-3-031-38165-2_135}, year = {2023}, date = {2023-08-25}, booktitle = {Flexible Automation and Intelligent Manufacturing: Establishing Bridges for More Sustainable Manufacturing Systems.}, abstract = {Production shifted from a product-centered perspective (mass production of one article) to a customer-centered perspective (mass customization of product variants). It also happens in energy-intensive industries, such as steel production. Mass customization companies face a challenge in accurately estimating the total costs of an individual product. Furthermore, 20% to 40% of the costs related to steel products come from energy. Increasing the product variety can cause an inevitable loss of sustainability. This paper presents machine-learning approaches to improve the sustainability of the steel production industry. It is done by finding the most accurate way to predict the power consumption and the costs of customized products. Moreover, this research also finds the most energy-efficient machine mix based on the predictions. The method is validated in a steel manufacturing Small Medium Enterprise (SME). In this research, experiments were conducted with different machine learning models, and it was found that the most accurate results were achieved using regularization-based and random forest regression models. Explainable AI (XAI) is also used to clarify how product properties influence process costs and power consumption. This paper also discusses scenarios on how the prediction of costs and power consumption can assist production planners in performing workstation selection. This research improves the production planning of customized products by providing a trustable decision support system for machine selection based on explainable machine learning models for process time and power consumption predictions.}, keywords = {energy efficiency, explainable artificial intelligence, machine learning, sustainability, sustainable manuracturing, XAI}, pubstate = {published}, tppubtype = {inproceedings} } Production shifted from a product-centered perspective (mass production of one article) to a customer-centered perspective (mass customization of product variants). It also happens in energy-intensive industries, such as steel production. Mass customization companies face a challenge in accurately estimating the total costs of an individual product. Furthermore, 20% to 40% of the costs related to steel products come from energy. Increasing the product variety can cause an inevitable loss of sustainability. This paper presents machine-learning approaches to improve the sustainability of the steel production industry. It is done by finding the most accurate way to predict the power consumption and the costs of customized products. Moreover, this research also finds the most energy-efficient machine mix based on the predictions. The method is validated in a steel manufacturing Small Medium Enterprise (SME). In this research, experiments were conducted with different machine learning models, and it was found that the most accurate results were achieved using regularization-based and random forest regression models. Explainable AI (XAI) is also used to clarify how product properties influence process costs and power consumption. This paper also discusses scenarios on how the prediction of costs and power consumption can assist production planners in performing workstation selection. This research improves the production planning of customized products by providing a trustable decision support system for machine selection based on explainable machine learning models for process time and power consumption predictions. |
Krstevski Stefan; Fatahi Valilai, Omid; Wicaksono Hendro In Proceedings of the 2023 10th International Conference on Industrial Engineering and Applications (ICIEAEU '23), pp. 98–106, Association for Computing Machinery, New York, NY, USA, 2023. Abstract | Links | BibTeX | Tags: energy efficiency, manufacturing, operation research, production planning and control, production scheduling, sustainability @inproceedings{Krstevski2023, title = {Integrating Real-Time Dynamic Electricity Price Forecast into Job Shop Production Scheduling Model with Multiple Machine Environments}, author = {Krstevski, Stefan; Fatahi Valilai, Omid; Wicaksono, Hendro }, url = {https://doi.org/10.1145/3587889.3587905}, doi = {10.1145/3587889.3587905}, year = {2023}, date = {2023-06-09}, booktitle = {In Proceedings of the 2023 10th International Conference on Industrial Engineering and Applications (ICIEAEU '23)}, pages = {98–106}, publisher = {Association for Computing Machinery, New York, NY, USA}, abstract = {One of the challenges in the transition towards green electricity is the intermittence of power generated by renewable sources. Thus, power consumers, including the manufacturing industry, must adapt their activities and processes to green electricity supply. Real-time dynamic pricing is an approach to encourage electricity consumers to change their consumption patterns by lowering prices when the availability of green electricity in the grid is high. Due to the introduction of real-time electricity pricing, manufacturing companies must adapt their production planning by integrating dynamic price information into their production scheduling. Our research focuses on extending the basic production scheduling mathematical model by introducing real-time power pricing in the model. The prices are built based on the current proportion of green electricity in the grid represented in the green electricity index (GEI) with one-hour intervals. This paper also illustrates a scenario of how to use the model. Our future research will further extend the model addressing the flexibility of manufacturing shop floors (e.g. adding buffer, retooling, and setup time) and validate the model in two small and medium manufacturing enterprises.}, keywords = {energy efficiency, manufacturing, operation research, production planning and control, production scheduling, sustainability}, pubstate = {published}, tppubtype = {inproceedings} } One of the challenges in the transition towards green electricity is the intermittence of power generated by renewable sources. Thus, power consumers, including the manufacturing industry, must adapt their activities and processes to green electricity supply. Real-time dynamic pricing is an approach to encourage electricity consumers to change their consumption patterns by lowering prices when the availability of green electricity in the grid is high. Due to the introduction of real-time electricity pricing, manufacturing companies must adapt their production planning by integrating dynamic price information into their production scheduling. Our research focuses on extending the basic production scheduling mathematical model by introducing real-time power pricing in the model. The prices are built based on the current proportion of green electricity in the grid represented in the green electricity index (GEI) with one-hour intervals. This paper also illustrates a scenario of how to use the model. Our future research will further extend the model addressing the flexibility of manufacturing shop floors (e.g. adding buffer, retooling, and setup time) and validate the model in two small and medium manufacturing enterprises. |
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. |
2019 |
Kusumawardana Arya; Habibi, Muhammad Afnan; Wibawanto Slamet; Wicaksono Hendro; Prasetya Yoga; Nurrahman Rizqi Coordination Power Control Of DC Water Pump System using Dual-loop Control and Consensus Algorithm Inproceedings 2019 International Conference on Electrical, Electronics and Information Engineering (ICEEIE), pp. 37-42, IEEE, 2019. Links | BibTeX | Tags: algorithm, artificial intelligence, energy efficiency, Energy efficient building @inproceedings{Kusumawardana2020, title = {Coordination Power Control Of DC Water Pump System using Dual-loop Control and Consensus Algorithm}, author = {Kusumawardana, Arya; Habibi, Muhammad Afnan; Wibawanto,Slamet; Wicaksono, Hendro; Prasetya, Yoga; Nurrahman, Rizqi }, url = {https://ieeexplore.ieee.org/document/8981473}, doi = {10.1109/ICEEIE47180.2019.8981473}, year = {2019}, date = {2019-02-01}, booktitle = {2019 International Conference on Electrical, Electronics and Information Engineering (ICEEIE)}, pages = {37-42}, publisher = {IEEE}, keywords = {algorithm, artificial intelligence, energy efficiency, Energy efficient building}, pubstate = {published}, tppubtype = {inproceedings} } |
2017 |
Wicaksono, Hendro DAREED: IT-Plattform für Energieeffizienz in Smart-City Workshop CEB Karlsruhe, 2017. Links | BibTeX | Tags: energy efficiency, IT integration, IT platform, semantic data integration, smart cities, smart energy @workshop{Wicaksono2017e, title = {DAREED: IT-Plattform für Energieeffizienz in Smart-City}, author = {Hendro Wicaksono}, url = {https://www.buildingsmart.de/kos/WNetz?art=File.download&id=6388&name=CEB17-Kongressprogramm.pdf http://presse.karlsruhe.de/db/meldungen/wirtschaft/best_practice_beispiele_fur_unternehmen.html}, year = {2017}, date = {2017-06-29}, address = {Karlsruhe}, organization = {CEB}, keywords = {energy efficiency, IT integration, IT platform, semantic data integration, smart cities, smart energy}, pubstate = {published}, tppubtype = {workshop} } |
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. |
2013 |
Wicaksono, Hendro; Belzner, Tim; Ovtcharova, Jivka Efficient Energy Performance Indicators for Different Level of Production Organizations in Manufacturing Companies Inproceedings Prabhu, Vittal; Taisch, Marco; Kiritsis, Dimitris (Ed.): Advances in Production Management Systems. Sustainable Production and Service Supply Chains, pp. 249-256, Springer Berlin Heidelberg, Berlin, Heidelberg, 2013, ISBN: 978-3-642-41266-0. Abstract | Links | BibTeX | Tags: energy efficiency, energy performance indicator, manufacturing @inproceedings{Wicaksono2013c, title = {Efficient Energy Performance Indicators for Different Level of Production Organizations in Manufacturing Companies}, author = {Hendro Wicaksono and Tim Belzner and Jivka Ovtcharova}, editor = {Vittal Prabhu and Marco Taisch and Dimitris Kiritsis}, url = {https://link.springer.com/book/10.1007/978-3-642-41266-0}, doi = {10.1007/978-3-642-41266-0_31}, isbn = {978-3-642-41266-0}, year = {2013}, date = {2013-09-12}, booktitle = {Advances in Production Management Systems. Sustainable Production and Service Supply Chains}, volume = {414}, pages = {249-256}, publisher = {Springer Berlin Heidelberg}, address = {Berlin, Heidelberg}, series = {IFIP}, abstract = {Demands for lower CO2 emissions due to the climate change and the rising of energy prices force manufacturing companies to deal with the energy issue. Energy management, where one of the tasks is energy efficiency evaluation, can help the companies to overcome the issue. This paper presents holistic metric to evaluate the energy efficiency in manufacturing companies, which considers the different organization level of production, such as machine or equipment level, production line level, and factory level. As the size of the scope and the number of observed factors vary, the metric provide flexible criteria to select relevant variables. The developed metric could be used to simulate and to compare energy efficiency of different production facilities, lines, and factories in a single company. The metric is an instrument to recognize how energy (in) efficient is a production system, so that adjustments may be made in the planning and management to achieve the energy savings.}, keywords = {energy efficiency, energy performance indicator, manufacturing}, pubstate = {published}, tppubtype = {inproceedings} } Demands for lower CO2 emissions due to the climate change and the rising of energy prices force manufacturing companies to deal with the energy issue. Energy management, where one of the tasks is energy efficiency evaluation, can help the companies to overcome the issue. This paper presents holistic metric to evaluate the energy efficiency in manufacturing companies, which considers the different organization level of production, such as machine or equipment level, production line level, and factory level. As the size of the scope and the number of observed factors vary, the metric provide flexible criteria to select relevant variables. The developed metric could be used to simulate and to compare energy efficiency of different production facilities, lines, and factories in a single company. The metric is an instrument to recognize how energy (in) efficient is a production system, so that adjustments may be made in the planning and management to achieve the energy savings. |
Wicaksono, Hendro; Prohl, Enrst Victor; Ovtcharova, Jivka Hyper heuristc based production process scheduling to improve productivity in sustainable manufacturing Inproceedings Proceeding the 22nd International Conference on Production Research, Brazil, 28 July – 1 August, 2013, 2013. Abstract | BibTeX | Tags: energy efficiency, hyper heuristics, manufacturing, production scheduling @inproceedings{Wicaksono2013d, title = {Hyper heuristc based production process scheduling to improve productivity in sustainable manufacturing}, author = {Hendro Wicaksono and Enrst Victor Prohl and Jivka Ovtcharova}, year = {2013}, date = {2013-08-01}, booktitle = {Proceeding the 22nd International Conference on Production Research, Brazil, 28 July – 1 August, 2013}, abstract = {In recent years, increased customer-demand in individualized and timely products has changed the playing field for manufacturing industries. The production process is perpetually gaining complexity whereas its life-cycle is shortening. Additionally, a growing ecological conscience enforces the consideration of energy effi-ciency. Intelligent production process scheduling is a core instrument to increase efficiency of the value-added chain whilst acknowledging existing constraints. This paper introduces a hyper-heuristic based framework for production scheduling that incorporates economic and ecological aspects. In contrast to meta heuristics that aim for easily reusable solution-methods to NP-Hard scheduling problems, hyper-heuristics try to automate the search for the best solution methods. Thus a hyper-heuristic does not seek for the best solution, but for a heuristic that solves the problem best. Other than conventional scheduling described in lit-erature, the proposed approach in this paper copes with many aspects (constraints and objectives) at once, such as retooling activities, energy efficiency, energy peak load avoidance, product lot size, operation multi-plicity, and shift work. Furthermore, this paper will introduce the incorporation possibilities of prior knowledge coming from both human and machine learning (data mining) into the hyper-heuristic framework. }, keywords = {energy efficiency, hyper heuristics, manufacturing, production scheduling}, pubstate = {published}, tppubtype = {inproceedings} } In recent years, increased customer-demand in individualized and timely products has changed the playing field for manufacturing industries. The production process is perpetually gaining complexity whereas its life-cycle is shortening. Additionally, a growing ecological conscience enforces the consideration of energy effi-ciency. Intelligent production process scheduling is a core instrument to increase efficiency of the value-added chain whilst acknowledging existing constraints. This paper introduces a hyper-heuristic based framework for production scheduling that incorporates economic and ecological aspects. In contrast to meta heuristics that aim for easily reusable solution-methods to NP-Hard scheduling problems, hyper-heuristics try to automate the search for the best solution methods. Thus a hyper-heuristic does not seek for the best solution, but for a heuristic that solves the problem best. Other than conventional scheduling described in lit-erature, the proposed approach in this paper copes with many aspects (constraints and objectives) at once, such as retooling activities, energy efficiency, energy peak load avoidance, product lot size, operation multi-plicity, and shift work. Furthermore, this paper will introduce the incorporation possibilities of prior knowledge coming from both human and machine learning (data mining) into the hyper-heuristic framework. |
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. |
Wicaksono, Hendro; Nusiaputra, Yodha; Rogalski, Sven; Budiarto, Rachmawan; Krahtov, Konstantin; Pamma, Adam; Suharto, Toto; Ovtcharova, Jivka Peningkatan Ketahanan Energi Nasional Melalui Platform Kerjasama Industri-Akademisi-Pemerintah Antara Jerman dan Indonesia Inproceedings Pertemuan Presiden RI dengan Indonesia di Jerman, Kedutaan Besar Republik Indonesia Berlin 2013. Abstract | BibTeX | Tags: energy efficiency, energy management, energy security, innovation network, renewable energy @inproceedings{Wicaksono2013g, title = {Peningkatan Ketahanan Energi Nasional Melalui Platform Kerjasama Industri-Akademisi-Pemerintah Antara Jerman dan Indonesia}, author = {Hendro Wicaksono and Yodha Nusiaputra and Sven Rogalski and Rachmawan Budiarto and Konstantin Krahtov and Adam Pamma and Toto Suharto and Jivka Ovtcharova }, year = {2013}, date = {2013-03-05}, booktitle = {Pertemuan Presiden RI dengan Indonesia di Jerman}, organization = {Kedutaan Besar Republik Indonesia Berlin}, abstract = {Peningkatan ketahanan energi adalah salah satu visi utama kedua negara, Jerman dan Indonesia, dalam rangka menjawab tantangan dalam negeri masing-masing serta menunaikan tanggung jawab untuk memberi kontribusi positif pada isu-isu global seperti lingkungan hidup. Hal ini dituangkan dalam deklarasi Jakarta yang ditandatangani oleh Presiden SBY dan Kanselir Angela Merkel dalam rangka peringatan 60 tahun kerjasama Indonesia-Jerman. Ketahanan energi di Jerman melibatkan peran aktif dan sinergi antara penyedia energi, lembaga penelitian dan pendidikan, industri terkait terutama UMKM, serta pemerintah. Sinergi seperti ini yang sekarang jarang dijumpai di Indonesia. Paper ini ditulis oleh para profesional dan akademisi dari Jerman dan Indonesia dan membahas konsep platform kerjasama Jerman-Indonesia dalam bidang ketahanan energi yang dinamakan PROIndo, yang telah diinisiasi sejak tahun 2010. Tujuan platform ini adalah meningkatkan inovasi teknologi dan bisnis kedua negara dalam bidang ketahanan energi dengan melibatkan kalangan industri, akademisi, dan pemerintah. Paper ini juga menyampaikan langkah-langkah yang ditempuh dalam mewujudkan kerjasama tersebut dalam bentuk workshop dan pertukaran personil. Selain itu dalam paper ini juga dijelaskan proyek-proyek kerjasama yang sedang dan akan berjalan, yaitu di bidang manajemen energi dan pengembangan teknologi energi terbarukan. Paper ini ditutup dengan rekomendasi kepada pemerintah Indonesia untuk meningkatkan ketahanan energi nasional Indonesia melalui platform kerjasama PROIndo.}, keywords = {energy efficiency, energy management, energy security, innovation network, renewable energy}, pubstate = {published}, tppubtype = {inproceedings} } Peningkatan ketahanan energi adalah salah satu visi utama kedua negara, Jerman dan Indonesia, dalam rangka menjawab tantangan dalam negeri masing-masing serta menunaikan tanggung jawab untuk memberi kontribusi positif pada isu-isu global seperti lingkungan hidup. Hal ini dituangkan dalam deklarasi Jakarta yang ditandatangani oleh Presiden SBY dan Kanselir Angela Merkel dalam rangka peringatan 60 tahun kerjasama Indonesia-Jerman. Ketahanan energi di Jerman melibatkan peran aktif dan sinergi antara penyedia energi, lembaga penelitian dan pendidikan, industri terkait terutama UMKM, serta pemerintah. Sinergi seperti ini yang sekarang jarang dijumpai di Indonesia. Paper ini ditulis oleh para profesional dan akademisi dari Jerman dan Indonesia dan membahas konsep platform kerjasama Jerman-Indonesia dalam bidang ketahanan energi yang dinamakan PROIndo, yang telah diinisiasi sejak tahun 2010. Tujuan platform ini adalah meningkatkan inovasi teknologi dan bisnis kedua negara dalam bidang ketahanan energi dengan melibatkan kalangan industri, akademisi, dan pemerintah. Paper ini juga menyampaikan langkah-langkah yang ditempuh dalam mewujudkan kerjasama tersebut dalam bentuk workshop dan pertukaran personil. Selain itu dalam paper ini juga dijelaskan proyek-proyek kerjasama yang sedang dan akan berjalan, yaitu di bidang manajemen energi dan pengembangan teknologi energi terbarukan. Paper ini ditutup dengan rekomendasi kepada pemerintah Indonesia untuk meningkatkan ketahanan energi nasional Indonesia melalui platform kerjasama PROIndo. |
2012 |
Wicaksono, Hendro; Ovtcharova, Jivka Energy Consumption Regulation and Optimization in Discrete Manufacturing through Semi-automatic Knowledge Generation using Data Mining Inproceedings Proceeding 10th Global Conference of Sustainable Manufacturing (GCSM), 2012. Abstract | BibTeX | Tags: data mining, discrete manufacturing, energy efficiency, knowledge capturing, knowledge management, machine learning @inproceedings{Wicaksono2012b, title = {Energy Consumption Regulation and Optimization in Discrete Manufacturing through Semi-automatic Knowledge Generation using Data Mining}, author = {Hendro Wicaksono and Jivka Ovtcharova }, year = {2012}, date = {2012-11-02}, booktitle = {Proceeding 10th Global Conference of Sustainable Manufacturing (GCSM)}, abstract = {The rapid growth of industrialization has led to a significant increase of energy demand that results in a constantly increasing of energy prices. Meanwhile, the changes of social, technical, and economic conditions in the market have challenged manufacturers to deal with the requirements for various and complex products. This has made production processes more sophisticated and energy intensive thus it leads to expensive production costs. This paper discusses a knowledge based approach to regulate the energy consumption in processing the customer orders in discrete manufacturing. The knowledge base consists of a rule set, which determines the choices of machines to process the products based on the given characteristics. Generally, the construction of such a knowledge base is a time-consuming task. This paper presents a semi-automatic rule generation using data mining. It extracts the energy consumption pattern based on relation of different parameter, such as product properties, machine profile, production processes, and surrounding variables.}, keywords = {data mining, discrete manufacturing, energy efficiency, knowledge capturing, knowledge management, machine learning}, pubstate = {published}, tppubtype = {inproceedings} } The rapid growth of industrialization has led to a significant increase of energy demand that results in a constantly increasing of energy prices. Meanwhile, the changes of social, technical, and economic conditions in the market have challenged manufacturers to deal with the requirements for various and complex products. This has made production processes more sophisticated and energy intensive thus it leads to expensive production costs. This paper discusses a knowledge based approach to regulate the energy consumption in processing the customer orders in discrete manufacturing. The knowledge base consists of a rule set, which determines the choices of machines to process the products based on the given characteristics. Generally, the construction of such a knowledge base is a time-consuming task. This paper presents a semi-automatic rule generation using data mining. It extracts the energy consumption pattern based on relation of different parameter, such as product properties, machine profile, production processes, and surrounding variables. |
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} } |