Open Access

ARTICLE

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

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

1 Energy Development Research Institute, China Southern Power Grid, Guangzhou, 510000, China
2 Corporate Headquarters, China Southern Power Grid, Guangzhou, 510000, China
3 Guangdong Green Energy Key Laboratory, South China University of Technology, Guangzhou, 510000, China

* Corresponding Author: Qianyufan Zhou. Email:

Energy Engineering 2024, 121(5), 1329-1346. https://doi.org/10.32604/ee.2023.046447

Received 01 October 2023; Accepted 04 December 2023; Issue published 30 April 2024

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 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.

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Keywords

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