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.
Highlights
Modern energy applications often use load profiles resulting from time-series data of electricity usage to monitor and manage the power consumption of customers efficiently and reliably [1]
Since the study of residential and industrial environments requires a more detailed definition of all the variables that contribute toward energy consumption, the energy datasets used in modern applications often contain many important measurements ranging from appliance consumption values to weather parameters
This paper explored a fuzzy system design approach for a residential building based on weather parameters in order to derive recommendations for the minimum energy consumption values based on environmental data
Summary
Modern energy applications often use load profiles resulting from time-series data of electricity usage to monitor and manage the power consumption of customers efficiently and reliably [1]. The insights extracted from the energy data convey more interpretable trends and patterns, which can be used by the energy provider as a management tool for the control of distribution and pricing. The output of such models can be useful to customers as a recommendation engine, helping them make more informed decisions and reschedule their daily tasks when opportunities arise for them to participate in more dynamic pricing plans [2]. Linear models sometimes fail to capture the trends that can be observed from the data, and the mathematical models used to describe them do not express the dynamic and complex nature of those environments as they evolve over time. Many input parameters used to define those environments often have a more imprecise and uncertain meaning that is often associated with human perception and expert knowledge
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