Assessment of recent metaheuristic algorithms for support vector regression-based building energy consumption prediction in net-zero energy buildings

The rapid rise in building construction creates the energy demand for nearly half of the world's energy demand. To minimize energy consumption in buildings, a concept called net-zero energy building (NZEB) is gaining popularity in developing countries and is being implemented in India as well. The NZEB aims to match the on-site renewable energy generation available at the building location with the building energy consumption (BEC) without relying on grid energy. To attain this concept in a real-time scenario, it requires information about the energy generation at the building and the energy consumption at each instant. It is necessary to predict the dynamically varying building loads to easily manage the available sources without the involvement of the grid. This can be achieved by designing an accurate prediction model. This article presents a comparative assessment of recent metaheuristic algorithms for hyperparameter optimization of a support vector regression (SVR) model to enhance the prediction performance of BEC. The analysis was conducted using hourly campus-scale energy consumption data collected from the National Institute of Technology Silchar, Assam, India, from 1 March 2018 to 29 February 2020, comprising 17,544 samples. The Polar Fox Optimization algorithm, Flood Algorithm, and Hiking Optimization Algorithm (HOA) were comparatively evaluated for SVR hyperparameter tuning in this application. The mean absolute error (MAE), mean absolute percentage error (MAPE), root mean squared error (RMSE), R 2 , percentage BIAS (PBIAS), and Willmott's Index (WI) error metrics are used to evaluate the performance of the optimized SVR models. The recently developed HOA algorithm exhibits better prediction accuracy with an MAE of 8.3099 kWh, RMSE of 11.1283 kWh, R 2 of 0.9986, MAPE of 2.7820%, PBIAS of −0.0759%, and WI of 0.9996 when compared to other models. The comparative results for different models show that the recent metaheuristic optimization methods can improve the performance of SVR model for accurate BEC prediction in NZEB applications.