Publications and Talks

@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}
}

@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}
}

@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}
}

@inproceedings{Wicaksono2017,
title = {Einsatz von (Open)-Linked-Data für Gebäudeinformationsmodellierung},
author = {Hendro Wicaksono },
editor = {Open Experience GmbH},
url = {https://openexperience.de/Forum_Digitale_Transformation_des_Baubetriebs_in_der_Praxis.html#Tagungsband},
year = {2017},
date = {2017-01-19},
booktitle = {Forum Digitale Transformation des Baubetriebs in der Praxis},
volume = {1},
publisher = {openexperience.de},
keywords = {building energy management, linked data},
pubstate = {published},
tppubtype = {inproceedings}
}

@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}
}

@inproceedings{Tonev2016,
title = {Semantic Data Integration for Smart Cities using Linked Data},
author = {Kiril Tonev and Hendro Wicaksono},
year = {2016},
date = {2016-09-07},
booktitle = {11th European Conference on Product and Process Modelling
},
keywords = {linked data, smart cities},
pubstate = {published},
tppubtype = {inproceedings}
}

@inproceedings{McGlinn2016b,
title = {Identifying use cases and data requirements for BIM based energy management},
author = {Kris McGlinn and Hendro Wicaksono and Willie Lawton and Matthias Wiese and and Nikolaos Kaklanis and Ioanna Petri and Dimitrios Tzovaras},
url = {https://www.cibse.org/knowledge/knowledge-items/detail?id=a0q20000008I71X},
year = {2016},
date = {2016-04-15},
publisher = {CIBSE},
abstract = {Energy consumption over the whole Building Lifecycle (BLC) is difficult to monitor and predict due to the complexity of the processes involved. Building Information Modelling (BIM) addresses the management and interoperability of the data exchanged between different computer applications employed at different stages of the BLC. Industry Foundation Classes (IFC) is a leading standard of BIM implementation. As yet, IFC does not cover all data structures to meet the latest use case data requirements for energy management. Linking IFC with other available ontologies is one potential solution. In this paper 46 unique use cases are identified over 33 EU projects in the Energy Efficient Building domain and explored in order to identify the most relevant data domains across projects. These data domains will form the basis of a process of alignment of project data models with existing standards and ontologies. It will also provide the basis of guidelines for new projects wishing to improve interoperability.},
keywords = {building energy management, building information modelling, building lifecycle management, Energy efficient building, linked data, use cases},
pubstate = {published},
tppubtype = {inproceedings}
}