Design and Application of an Energy Management System Based on Artificial Intelligence Technology

In response to the increasingly severe energy consumption problem and to promote energy saving and emission reduction, this study aims to design and apply an energy management system platform based on artificial intelligence (AI) technology. The system adopts sensor technology and data acquisition equipment to monitor various types of energy consumption in buildings in real time, efficiently process and predict these data through machine learning algorithms, and finally visualize the results. The system is functionally complete, completing the process from data collection to visualization, the cloud platform’s construction, and finally a full energy management platform. Various machine learning methods are applied to energy management by predicting the chilled water energy meter return temperature of the central air-conditioning system and comparing its performance. Among the various types of regression algorithms, the mean-square error (MSE) of decision tree regression is 0.36, the MSE of support vector regression (SVR) is 0.09, the MSE of K-nearest neighbor (KNN) regression is 0.57, and the MSE of extreme gradient boosting (XGBoost) regression is 0.32. The SVR, the XGBoost regression, and the decision tree regression perform better in various indices.