Empowering Energy Consumption Forecasting in Smart Buildings: Towards a Hybrid Loss Function

Energy consumption forecasting is of paramount importance in achieving energy conservation goals. While numerous approaches have been developed to optimize building energy usage, predictive analytics stands out as a cornerstone tool for informed decision-making. Deep learning models have gained popularity for forecasting energy consumption in smart buildings. These models leverage a variety of techniques, including loss functions, activation functions, and optimizers, to enhance training effectiveness. However, the commonly used Mean Squared Error (MSE) as a loss function has a notable drawback as it treats overestimations and underestimations equally. In this study, we propose a novel Hybrid Loss Function (HLF) tailored to address this limitation. The HLF penalizes the model more for underestimating energy consumption during abnormal seasons while maintaining its ability to accurately predict actual consumption, particularly under normal operating conditions. Through extensive simulations, our findings demonstrate that our proposed approach outperforms existing methods in the literature, providing exceptionally accurate and robust forecasts of energy consumption.