Investigating the impact of data normalization methods on predicting electricity consumption in a building using different artificial neural network models
Kim, Yang-Seon Kim, Moonkeun Fu, Nuodi Liu, Jiying Wang, Junqi Srebric, Jelena
Kim, Yang-Seon
Kim, Moonkeun
Fu, Nuodi
Liu, Jiying
Wang, Junqi
Srebric, Jelena
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Issue Date
2024-06-03
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Article
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Keywords
Data normalization,Artificial neural networks,Electricity prediction,Occupancy rates
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Citation
Kim, Y.-S., Kim, M. K., Fu, N., Liu, J., & Srebric, J. Investigating the impact of data normalization methods on predicting electricity consumption in a building using different artificial neural network models. Sustainable Cities and Society, 118. https://doi.org/10.1016/j.scs.2024.105570
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.
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Publisher
Elsevier Ltd.
Journal
Sustainable Cities and Society
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Archival Collection
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ISSN
2210-6715
2210-6707
2210-6707