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Investigating the impact of data normalization methods on predicting electricity consumption in a building using different artificial neural network models

Year: 2024

Type: article

Abstract: The study investigates the impact of data normalization on the prediction of electricity consumption in buildings using four multilayer Artiﬁcial Neural Networks (ANN) algorithms: Long Short-Term Memory Networks (LSTM), Levenberg–Marquardt Back-propagation (LMBP), Recurrent Neural Networks (RNN), and General Regression Neural Network (GRNN). Four data normalization approaches, Min-Max Scaling, Mean, Z-score, and Gaussian function were assessed on experimental datasets. The LSTM algorithm, when combined with Min-Max normal-ization, showed the most favorable predictive capabilities, with a low Coefﬁcient of Variation of the Root Mean Square Error (CVRMSE) of 10.3 and Normalized Mean Bias Error (NMBE) of 0.6. The remaining three normal-ization approaches showed satisfactory concordance with empirical data, but with slight disparities in precision. The LMBP model, when using Z-score normalization, had favorable performance in forecasting electricity con-sumption, but the discrepancies across the models were not signiﬁcant. The Recurrent Neural Network (RNN) model, when used with Gaussian normalization, exhibited the most favorable performance, with the lowest Coefﬁcient of Variation of Root Mean Square Error (CVRMSE) at 11.8 and Normalized Mean Biased Error (NMBE) at 0.6. The Generalized Regression Neural Network (GRNN) model, trained on unprocessed data, exhibited superior performance, with the lowest Coefﬁcient of Variation of Root Mean Square Error (CVRMSE) at 19.2 and NMBE at 1.0. In conclusion, the study highlights the signiﬁcant inﬂuence of data normalization on the predictive capabilities of various ANN models, suggesting that careful use of data normalization techniques can signiﬁcantly improve the accuracy of electricity consumption forecasting in buildings.

Source: Sustainable Cities and Society

DOI: 10.1016/j.scs.2024.105570

Authors: Yang-Seon Kim, Moon Keun Kim, Nuodi Fu, Jiying Liu, Junqi Wang, Jelena Srebric