Predicting short-term electric power demand using weather data

This study aims to utilize a random forest regression model to predict electricity consumption, helping power companies better manage demand fluctuations, optimize resource allocation, and improve system efficiency. A comprehensive dataset was collected for the study, which includes various features related to electricity consumption, such as temperature, humidity, wind speed, and holidays. During the model training process, cross-validation and grid search methods were used to optimize the hyperparameters of the random forest regression model. The evaluation of the model's performance demonstrated the effectiveness of the random forest regression model in practical applications. The results of the study show that the model can accurately predict short-term electricity consumption, providing power companies with a reliable decision-support tool. This not only helps power companies effectively manage demand fluctuations during peak usage periods but also optimizes resource allocation, thereby improving the overall operational efficiency of the power system.The methods and results of this study provide valuable references for further optimization of power systems. The research proves the potential of machine learning techniques in electricity demand forecasting and provides a solid foundation for future research and applications. Through further research and improvements, machine learning models will play an increasingly important role in power system management, driving the power industry towards more intelligent and efficient development.