Temperature is a key factor in modeling electricity demand, making the selection of optimal weather stations essential for accurate predictions. However, current methods for selecting weather stations often rely on heuristic approaches that explore only a limited subset of potential combinations, potentially missing better solutions. In this paper, we propose an innovative approach that integrates the Simulated Annealing (SA) algorithm with local search techniques to improve forecast accuracy and reduce implementation time. Our method demonstrates superior performance in both quality and efficiency compared to existing approaches, as validated across three datasets, including data from a major distribution company in Iran and the Global Energy Forecasting Competitions of 2012 and 2014. Our results show that incorporating local search techniques reduces the Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) by 3.48% and 2.82%, respectively. Furthermore, the average implementation time of our SA algorithm is 36.52% lower than that of the existing metaheuristic algorithm.

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