Deep-Ensemble Learning Method for Solar Resource Assessment of Complex Terrain Landscapes

作     者：Lifeng Li Zaimin Yang Xiongping Yang Jiaming Li Qianyufan Zhou Ping Yang 

作者机构：Energy Development Research InstituteChina Southern Power GridGuangzhou510000China Corporate HeadquartersChina Southern Power GridGuangzhou510000China Guangdong Green Energy Key LaboratorySouth China University of TechnologyGuangzhou510000China 

出 版 物：《Energy Engineering》 (能源工程（英文）)

年 卷 期：2024年第121卷第5期

页      面：1329-1346页

核心收录：

学科分类：080703[工学-动力机械及工程] 08[工学] 0807[工学-动力工程及工程热物理] 

基　　金：funded by Key-Area Research and Development Program Project of Guangdong Province (2021B0101230003) China Southern Power Grid Science and Technology Project (ZBKJXM20220004) 

主　　题：Photovoltaic resource assessment deep learning ensemble learning random forest gated recurrent unit long short-term memory 

摘      要：As the global demand for renewable energy grows,solar energy is gaining attention as a clean,sustainable energy *** assessment of solar energy resources is crucial for the siting and design of photovoltaic power *** study proposes an integrated deep learning-based photovoltaic resource assessment *** learning and deep learning methods are fused for photovoltaic resource assessment for the first *** proposed method combines the random forest,gated recurrent unit,and long short-term memory to effectively improve the accuracy and reliability of photovoltaic resource *** proposed method has strong adaptability and high accuracy even in the photovoltaic resource assessment of complex terrain and *** experimental results show that the proposed method outperforms the comparison algorithm in all evaluation indexes,indicating that the proposed method has higher accuracy and reliability in photovoltaic resource assessment with improved generalization performance traditional single algorithm.