The building sector is a major energy consumer, and its share of energy consumption is increasing because of urbanization. Improving building energy efficiency is imperative for reducing energy costs and the environmental impact. This study developed a novel time-series sliding window metaheuristic optimization-based machine learning model for predicting building energy consumption. Real-time data retrieved from a smart grid installed in an experimental building were used to evaluate the efficacy and effectiveness of the proposed model. The proposed model integrates a seasonal autoregressive integrated moving average (SARIMA) model and metaheuristic firefly algorithm-based least squares support vector regression (MetaFA-LSSVR) model. Specifically, the proposed novel time-series forecasting model fits the SARIMA model to linear data components in the first stage, and the MetaFA-LSSVR model captures nonlinear data components in the second stage. The MetaFA is proposed to optimize prediction accuracy of the proposed model. A k-week sliding window approach is used for employing historical data as inputs for the novel time-series forecasting model. The prediction model yielded high and reliable accuracy in 1-day-ahead predictions of building energy consumption, with a total error rate of 1.181% and mean absolute error of 0.026 kWh. Notably, the model demonstrates an improved accuracy rate in the range of 36.8–113.2% relative to those of the linear forecasting model (i.e., SARIMA) and nonlinear forecasting models (i.e., LSSVR and MetaFA-LSSVR). Therefore, end users can further apply the forecasted information to enhance efficiency of energy usage in their buildings, especially during peak times.

The building sector is a major energy consumer, and its share of energy consumption is increasing because of urbanization. Improving building energy efficiency is imperative for reducing energy costs and the environmental impact. This study developed a novel time-series sliding window metaheuristic optimization-based machine learning model for predicting building energy consumption. Real-time data retrieved from a smart grid installed in an experimental building were used to evaluate the efficacy and effectiveness of the proposed model. The proposed model integrates a seasonal autoregressive integrated moving average (SARIMA) model and metaheuristic firefly algorithm-based least squares support vector regression (MetaFA-LSSVR) model. Specifically, the proposed novel time-series forecasting model fits the SARIMA model to linear data components in the first stage, and the MetaFA-LSSVR model captures nonlinear data components in the second stage. The MetaFA is proposed to optimize prediction accuracy of the proposed model. A k-week sliding window approach is used for employing historical data as inputs for the novel time-series forecasting model. The prediction model yielded high and reliable accuracy in 1-day-ahead predictions of building energy consumption, with a total error rate of 1.181% and mean absolute error of 0.026 kWh. Notably, the model demonstrates an improved accuracy rate in the range of 36.8–113.2% relative to those of the linear forecasting model (i.e., SARIMA) and nonlinear forecasting models (i.e., LSSVR and MetaFA-LSSVR). Therefore, end users can further apply the forecasted information to enhance efficiency of energy usage in their buildings, especially during peak times.

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