Algorithm Application of Machine Learning Algorithms to Predict Energy Demand

This article presents an analysis of the application of machine learning algorithms to predict energy demand based on temperature and humidity data. Extensive research has been conducted on the effectiveness and accuracy of various machine learning methods in the context of energy demand forecasting, including the support vector machine (SVM) method with various kernels (e.g., 'rbf' and 'poly') and RandomForestRegressor. The article also discusses in detail the selection of the optimal model and parameter optimization to achieve the best forecasting results for energy demand. The presented research holds significant practical importance for advancing the field of energy resource management, enhancing their utilization efficiency, and optimizing decision-making processes. This enables the identification of the most effective and accurate approaches to forecast energy demand across diverse conditions and scenarios.