期刊
ENERGIES
卷 14, 期 3, 页码 -出版社
MDPI
DOI: 10.3390/en14030752
关键词
fuzzy logic; fuzzy control systems; machine learning; decision trees; energy management; demand response; artificial intelligence
Modern energy automation solutions and demand response applications rely on load profiles to monitor and manage electricity consumption effectively. This study introduces a fuzzy control system that processes environmental data to recommend minimum energy consumption values for a residential building, utilizing the forward chaining Mamdani approach and decision tree linearization for rule generation. The hybrid feature selector based on XGBoost and decision tree metrics is implemented to determine feature importance and generate a small set of fuzzy rules for analyzing energy consumption behavior.
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.
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