A novel optimized hybrid machine learning model to enhance the prediction accuracy of hourly building energy consumption

ABSTRACT

Building energy consumption (BEC) prediction is crucial in efficient energy management. This paper proposes an optimized hybrid prediction model that combines a support vector regression (SVR), a newly evolved coati optimization algorithm (COA), and a recursive feature elimination with cross-validation (RFECV) implemented on an hourly new dataset. The SVR is selected based on the experimentations conducted in this work that outperform other models. The COA is used for optimizing the hyperparameters of SVR, and RFECV is used to optimize the dataset. The SVR COA performs better than the Harris hawk optimization and Gray wolf optimization. Later, the optimized SVR is implemented on the optimized dataset, showing better accuracy and faster prediction compared with the default SVR, SVR with feature elimination, and optimized SVR models. The error metrics MAE, MAPE, RMSE, and R² are used for the model evaluation. The testing accuracy improved by 12.34%, 10.52%, 17.02%, and 0.09%, respectively, compared to the default model.

KEYWORDS:

• Building energy consumption
• hybrid prediction model
• support vector regression
• coati optimization algorithm
• recursive feature elimination cross-validation
• hyperparameter optimization

Disclosure statement

No potential conflict of interest was reported by the author(s).

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/15567036.2024.2374750.

Additional information

Notes on contributors

Rajasekar Thota

Rajasekar Thota received the B.Tech. degree in Electrical & Electronics Engineering from Sri Vidyanikethan Engineering College Tirupati, in 2010, and the M.Tech. degree in Electrical Power Systems from the Quba College of Engineering and Technology Nellore, in 2015. He has completed his Ph.D. degree in the Department of Electrical Engineering at the National Institute of Technology Silchar, Assam, India. His research interests include renewable energy sources in the building sector, the application of soft computing techniques in the green energy building sector, load forecasting, machine learning, energy efficiency, and energy management. He is an active reviewer of the energy journal Elsevier.

Nidul Sinha

Nidul Sinha was born in Tripura, India, in 1962. He received his B.E. degree in Electrical Engineering from Calcutta University, Kolkata, India, in 1984, the M.Tech. degree in Power Apparatus and Systems from IIT Delhi, New Delhi, in 1989, and a Ph.D. degree in Electrical Engineering from Jadavpur University, Kolkata. His Ph.D. thesis was on the application of intelligent techniques in the optimal operation of a power system. Since then, he has been engaged in active research in different areas like automatic generation control, optimal operation of the power system under conventional and non-conventional environments, control of renewable energy sources and micro-grid, image denoising, and video motion estimation, EEG-based emotion detection, and silent speech reading. He has more than 90 national and international publications in diverse fields. He has completed four sponsored Research and Development Projects. He has also been a reviewer of several international journals, such as IEEE, IET, Elsevier, Taylor and Francis, and Springer.