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Fuzzy Control System for Smart Energy Management in Residential Buildings Based on Environmental Data

Author

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  • Dimitrios Kontogiannis

    (Department of Electrical and Computer Engineering, University of Thessaly, 38221 Volos, Greece)

  • Dimitrios Bargiotas

    (Department of Electrical and Computer Engineering, University of Thessaly, 38221 Volos, Greece)

  • Aspassia Daskalopulu

    (Department of Electrical and Computer Engineering, University of Thessaly, 38221 Volos, Greece)

Abstract

Modern energy automation solutions and demand response applications rely on load profiles to monitor and manage electricity consumption effectively. The introduction of smart control systems capable of handling additional fuzzy parameters, such as weather data, through machine learning methods, offers valuable insights in an attempt to adjust consumer behavior optimally. Following recent advances in the field of fuzzy control, this study presents the design and implementation of a fuzzy control system that processes environmental data in order to recommend minimum energy consumption values for a residential building. This system follows the forward chaining Mamdani approach and uses decision tree linearization for rule generation. Additionally, a hybrid feature selector is implemented based on XGBoost and decision tree metrics for feature importance. The proposed structure discovers and generates a small set of fuzzy rules that highlights the energy consumption behavior of the building based on time-series data of past operation. The response of the fuzzy system based on sample input data is presented, and the evaluation of its performance shows that the rule base generation is derived with improved accuracy. In addition, an overall smaller set of rules is generated, and the computation is faster compared to the baseline decision tree configuration.

Suggested Citation

  • Dimitrios Kontogiannis & Dimitrios Bargiotas & Aspassia Daskalopulu, 2021. "Fuzzy Control System for Smart Energy Management in Residential Buildings Based on Environmental Data," Energies, MDPI, vol. 14(3), pages 1-18, February.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:3:p:752-:d:490715
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    References listed on IDEAS

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    Cited by:

    1. Jonas Sievers & Thomas Blank, 2023. "A Systematic Literature Review on Data-Driven Residential and Industrial Energy Management Systems," Energies, MDPI, vol. 16(4), pages 1-21, February.
    2. Isaías Gomes & Karol Bot & Maria Graça Ruano & António Ruano, 2022. "Recent Techniques Used in Home Energy Management Systems: A Review," Energies, MDPI, vol. 15(8), pages 1-41, April.
    3. Alvaro Llaria & Jessye Dos Santos & Guillaume Terrasson & Zina Boussaada & Christophe Merlo & Octavian Curea, 2021. "Intelligent Buildings in Smart Grids: A Survey on Security and Privacy Issues Related to Energy Management," Energies, MDPI, vol. 14(9), pages 1-37, May.
    4. Anastasios I. Dounis, 2022. "Machine Intelligence in Smart Buildings," Energies, MDPI, vol. 16(1), pages 1-5, December.
    5. Jie Yao & Feng Wu, 2022. "Cloud automatic mechanical equipment based on D–T fuzzy control and internet of things," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(4), pages 1696-1704, August.
    6. Ayman A. Aly & Bassem F. Felemban & Ardashir Mohammadzadeh & Oscar Castillo & Andrzej Bartoszewicz, 2021. "Frequency Regulation System: A Deep Learning Identification, Type-3 Fuzzy Control and LMI Stability Analysis," Energies, MDPI, vol. 14(22), pages 1-21, November.
    7. Dimitrios Kontogiannis & Dimitrios Bargiotas & Aspassia Daskalopulu & Lefteri H. Tsoukalas, 2021. "A Meta-Modeling Power Consumption Forecasting Approach Combining Client Similarity and Causality," Energies, MDPI, vol. 14(19), pages 1-19, September.
    8. Athanasios Ioannis Arvanitidis & Dimitrios Bargiotas & Aspassia Daskalopulu & Dimitrios Kontogiannis & Ioannis P. Panapakidis & Lefteri H. Tsoukalas, 2022. "Clustering Informed MLP Models for Fast and Accurate Short-Term Load Forecasting," Energies, MDPI, vol. 15(4), pages 1-14, February.
    9. Dimitrios K. Panagiotou & Anastasios I. Dounis, 2022. "Comparison of Hospital Building’s Energy Consumption Prediction Using Artificial Neural Networks, ANFIS, and LSTM Network," Energies, MDPI, vol. 15(17), pages 1-25, September.
    10. Athanasios Ioannis Arvanitidis & Dimitrios Bargiotas & Aspassia Daskalopulu & Vasileios M. Laitsos & Lefteri H. Tsoukalas, 2021. "Enhanced Short-Term Load Forecasting Using Artificial Neural Networks," Energies, MDPI, vol. 14(22), pages 1-14, November.

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