TY  - EJOU
AU  - Li, feng 
AU  - Yang, Zaimin 
AU  - Yang, Xiongping 
AU  - Li, Jiaming 
AU  - Zhou, Qianyufan 
AU  - Yang, Ping 

TI  - Deep-Ensemble Learning Method for Solar Resource Assessment of Complex Terrain Landscapes
T2  - Energy Engineering

PY  - 2024
VL  - 121
IS  - 5
SN  - 1546-0118

AB  - As the global demand for renewable energy grows, solar energy is gaining attention as a clean, sustainable energy source. Accurate assessment of solar energy resources is crucial for the siting and design of photovoltaic power plants. This study proposes an integrated deep learning-based photovoltaic resource assessment method. Ensemble learning and deep learning methods are fused for photovoltaic resource assessment for the first time. The proposed method combines the random forest, gated recurrent unit, and long short-term memory to effectively improve the accuracy and reliability of photovoltaic resource assessment. The proposed method has strong adaptability and high accuracy even in the photovoltaic resource assessment of complex terrain and landscape. The 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.
KW  - Photovoltaic resource assessment; deep learning; ensemble learning; random forest; gated recurrent unit; long short-term memory

DO  - 10.32604/ee.2023.046447