A paradigm shift in solar energy forecasting: A novel two-phase model for monthly residential consumption

Accurately predicting residential solar energy consumption is crucial for efficient electricity production, supply, and power dispatch. However, conventional forecasting methods often struggle to handle complex energy consumption data. In response to this challenge, this study develops a pioneering two-stage error-corrected combined forecasting model that integrates traditional linear methods, seasonal processing techniques, deep learning models, and intelligent optimization algorithms to outperform other combined forecasting methods in terms of performance. This research analyzes the combined weight values, shedding light on why the proposed model consistently outperforms its counterparts. To confirm its superiority, the proposed model and five benchmark models are rigorously tested in this paper using four evaluation metrics and a hypothesis testing method. The empirical results show that the proposed combined model performs well in terms of accuracy and stability. Notably, the average absolute percentage error of its 24-step ahead prediction is 2.9053%, which outperforms all comparative models, both single and combined model. These results fully illustrate the advantages of the combined model and reaffirm the excellence of its prediction performance in predicting energy consumption.