Heliyon (Sep 2024)
Based on the improved fuzzy analytic hierarchy and the TSE-MLR model energy consumption prediction of university: A case study
Abstract
The accurate prediction of building energy consumption on university campuses is a significant research area. Current studies often focus on predicting the energy consumption of specific building areas or individual equipment, and typically consider only one factor, limiting the accuracy and applicability of the predictions. This study introduces the Time Segmented Energy-Multiple Linear Regression (TSE-MLR) prediction model, which integrates the improved fuzzy analytic hierarchy and the multiple linear regression algorithm. The model is compared with traditional (MLR, BP) and advanced (RNN) models, and their various indexes are discussed and analyzed. By collecting meteorological and energy consumption data from the study site over the past 12 years, the key factors affecting energy consumption on the university campus were identified using the improved fuzzy analytic hierarchy. Subsequently, the TSE-MLR model was trained using energy consumption data from 2010 to 2016 and validated using data from 2017 to 2019. The prediction results of the TSE-MLR model were compared with those obtained through Multiple linear regression, BP neural networks, and RNN. The results demonstrated that the TSE-MLR model significantly reduced the prediction error by 13.8 % and exhibited higher accuracy compared to the other models. Therefore, the TSE-MLR model introduced in this study offers a new and effective approach to predicting university energy consumption and supporting energy management using existing data from university building operations across different periods.
