Fuzzy Control System for Smart Energy Management in Residential Buildings Based on Environmental Data
Dimitrios Kontogiannis (),
Dimitrios Bargiotas () and
Aspassia Daskalopulu ()
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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
Energies, 2021, vol. 14, issue 3, 1-18
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.
Keywords: fuzzy logic; fuzzy control systems; machine learning; decision trees; energy management; demand response; artificial intelligence (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:14:y:2021:i:3:p:752-:d:490715
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