Lifeng Li¹, Zaimin Yang¹, Xiongping Yang¹, Jiaming Li², Qianyufan Zhou^3,*, Ping Yang³

Energy Engineering, Vol.121, No.5, pp. 1329-1346, 2024, DOI:10.32604/ee.2023.046447

Abstract 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… More >

Yanbo Shen^1,2, Yang Gao³, Yueming Hu^1,2, Xin Yao⁴, Wenzheng Yu^4,*, Yubing Zhang⁴

Energy Engineering, Vol.121, No.1, pp. 43-57, 2024, DOI:10.32604/ee.2023.041921

Abstract The Tibet Plateau is one of the regions with the richest solar energy resources in the world. In the process of achieving carbon neutrality in China, the development and utilization of solar energy resources in the region will play an important role. In this study, the gridded solar resource data with 1 km resolution in Tibet were obtained by spatial correction and downscaling of SMARTS model. On this basis, the spatial and temporal distribution characteristics of solar energy resources in the region in the past 30 years (1991–2020) are finely evaluated, and the annual global horizontal radiation resource is calculated.… More >