废物管理
地平线
环境科学
光伏系统
工程类
工艺工程
环境工程
物理
电气工程
天文
作者
Chao Zhang,Yunfeng Ma,Zengqiang Mi,Fan Yang,Long Zhang
出处
期刊:Applied Energy
[Elsevier]
日期:2024-01-01
卷期号:353: 122168-122168
被引量:1
标识
DOI:10.1016/j.apenergy.2023.122168
摘要
Periodic cleaning of photovoltaic (PV) panels, such as every three months, is a common industry practice. However, this fixed period may not be optimal for maximizing the profit of a PV power generation system, due to numerous time-variant influencing factors, such as weather, temperature, etc. To increase the overall profit of a solar farm, it is highly desirable to have a flexible cleaning schedule that considers time-variant influencing factors. For this requirement, a rolling-horizon cleaning recommendation system is presented in this paper. Within this cleaning recommendation system, a prediction model and profit model are proposed. The prediction model, called the ensemble long-term and nonlinear autoregressive, can provide a time-variant future horizon by analyzing and compressing the time-variant characteristics in historical information. The profit model based on mathematical constraints, can process time-variant future horizon output from prediction model to generate a flexible optimized recommendation for cleaning schedule. The effectiveness of the proposed system is validated in real farms and all data used in this paper is collected from real world. The two case studies in experiments show that the profit improvement can reach up to 6% and 30%, respectively.
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