Energy Consumption Prediction of Residential Buildings using Linguistic Fuzzy Rules Learning

The building sector is one of the most developed areas within countries due to its contribution to economic growth. On the other hand, the building sector is increasingly depleting energy resources, making it necessary and urgent to reduce its consumption. In this context, we deal with the issue of energy consumption of residential buildings, by studying and discussing energy consumption forecasting models for residential buildings to improve building designs and enhance energy efficiency. To achieve these objectives, it is necessary to explain the behavior of forecasting systems. This will enable stakeholders to make informed decisions. In the context of explainable artificial intelligence, which provides an interactive interface for stakeholders to interact with intelligent systems, check their results, and trust their behavior; we present a fuzzy prediction model based on subtractive clustering and linguistic hedges to forecast energy consumption for residential buildings. The model is tested on a dataset containing 768 residential building information with eight input and two output variables. The proposed model is compared with Elastic Net, Random Forest, MLP, Linear Regression, Gaussian Process Regressor, ExtraTrees, Decision Tree, KNeighbors, SVR, Adaboost, XGBoost, and Gradient Boosting Regressor. The simulation findings show the efficacy of linguistic fuzzy rules in the forecasting of residential energy consumption. In parallel with prediction, the model provides a good balance between interpretability and accuracy.