Machine Learning Techniques for Decarbonizing and Managing Renewable Energy Grids

• Published in: Sustainability Vol. 14; no. 21; p. 13939
• Main Authors: Wu, Muqing, He, Qingsu, Liu, Yuping, Zhang, Ziqiang, Shi, Zhongwen, He, Yifan
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.11.2022
• Subjects: Algorithms; Alternative energy; Analysis; Classification; Clean energy; Clustering; Decarbonization; Distribution (Probability theory); Electric power distribution; Electric power grids; Electric vehicles; Energy industry; Error reduction; Forecasts and trends; Green technology; Machine learning; Methods; Optimization; Optimization models; Power supply; Probability distribution; Renewable resources; Support vector machines; Wind power
• ISSN: 2071-1050; 2071-1050
• DOI: 10.3390/su142113939

Given the vitality of the renewable-energy grid market, the optimal allocation of clean energy is crucial. An optimal dispatching method for source–load coordination of renewable-energy grid is proposed. An improved K-means clustering algorithm is used to preprocess the source data and historical load data. A support vector machine is used to predict the cluster of renewable-energy grid resources and load data, and typical scenarios are selected from the prediction results. Taking typical scenarios as a representative, the probability distribution of wind power output is accurately obtained. An optimization model of the total operation cost of the renewable-energy grid is established. The experimental results show that the algorithm reduces the error between the predicted value and the actual value. Our method can improve the real-time prediction accuracy of the renewable-energy grid system and increase the economic benefits of the renewable energy grid.