Hybrid Machine Learning System to Forecast Electricity Consumption of Smart Grid-Based Air Conditioners

This study develops a hybrid prediction system to forecast 1-day-ahead electricity consumption of air conditioners in office spaces. The hybrid system combines a linear autoregressive integrated moving average model and a nonlinear nature-inspired metaheuristic optimization-based prediction model. To evaluate the efficacy of the proposed system, a smart grid-based monitoring device was installed in an office space, which consists of smart meters, environmental monitoring sensors, infrared sensors, and fan adjustment systems. Data were retrieved to train and test the proposed system. Sensitivity analyses were performed to identify the optimal parameters of the model and inputs for future use. Evaluation results confirmed that the proposed hybrid system outperformed the conventional linear and nonlinear models, showing good agreement between predicted and actual electricity consumption of air conditioners. Particularly, the proposed system obtained the correlation coefficient R of 0.71 and total error rate of 4.8%. The hybrid system can facilitate facility managers in forecasting electricity consumption of air conditioners.