Publications and Talks

@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}
}

@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}
}

@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}
}

@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}
}

@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}
}

@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}
}

@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}
}

@article{Wicaksono2010,
title = {Wissensbasierte Energieanalyse – Verbesserte Energieeffizienz in Gebäuden des Privat- und Geschäftsbereichs},
author = {Hendro Wicaksono and Sven Rogalski},
url = {http://www.hanser-elibrary.com/doi/10.3139/104.110342},
doi = {10.3139/104.110342},
year = {2010},
date = {2010-06-30},
journal = {ZWF Zeitschrift für wirtschaftlichen Fabrikbetrieb},
volume = {105},
number = {6},
pages = {551-555},
abstract = {Vor dem Hintergrund einer wesentlich verbesserten Energieeffizienz in Gebäuden des Privat- und Geschäftsbereichs – trotz bestehender Gebäudeautomationssysteme – startete im Mai 2009 das Projekt „KEHL – Kontrollierte-Energie-Haushalts-Lösungen“ im Rahmen des Programms „KMU-innovativ“. Die wesentliche Forschungsherausforderung ist dabei, es dem Nutzer auf Basis einer einzigartigen Raum-, Zeit-, Ereignis-Relation zu ermöglichen, gebäudebezogene Gesamtverbräuche in Gebäuden bis auf die Geräteebene aufzusplitten und energetische Auswertungen in Abhängigkeit zum Nutzungsverhalten durchzuführen. Hierdurch werden nutzungsabhängige Energieverbrauchsanalysen ermöglicht, die eine automatisierte energieeffizientere Ansteuerung der vorhandenen Automationssysteme bewirken. Im nachstehenden Beitrag sollen unter anderem auf bisher erzielte Projektergebnisse eingegangen sowie weiterführende Forschungsansätze herausgestellt werden. Schwerpunkt der Ausführungen bilden insbesondere die KEHL-Wissensbasis und die darauf aufbauenden Energieanalysen.},
keywords = {data mining, Energy efficient building, knowledge management, machine learning},
pubstate = {published},
tppubtype = {article}
}