Maximum information coefficient combined with <scp>IMPA</scp>‐<scp>ELM</scp> model for load regulation assessment

Abstract In smart grid and smart building environments, accurate forecasting of load demand in residential buildings is of critical importance. This helps to enhance the stability of the power system, facilitate the integration of distributed renewable energy sources, and develop efficient demand response strategies. In view of this, this paper proposes a day‐ahead power load regulation assessment model based on the maximum information coefficient (MIC) combined with an improved Marine Predators Algorithm (IMPA) to optimize the extreme learning machine (ELM). The daily lagged load is used to construct the initial feature set, and the MIC is used for feature selection to filter out the top five features with the largest values. The MPA improvement strategy includes self‐mapping to generate chaotic sequence initialization and boundary mutation operations. The experimental results show that the proposed model has the optimal performance in evaluating the load regulation amount compared to other models. Taking Mean Absolute Error (MAE) as an example, compared with ELM and MPA‐ELM, IMPA‐ELM improved by 13.85% and 3.04%, respectively.