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To study the effect of dust on performance of photovoltaic power generation, a laboratory bench was built to collect daily power generation data of clean and polluted photovoltaic strings while monitoring meteorological data to analyze the influence of dust accumulation and weather on power generation performance of photovoltaic modules. The results indicate that, the increase in PM2.5 mass concentration in winter and the frequent occurrence of sandstorms in spring lead to a significant accumulation of dust on surface of the photovoltaic modules, resulting in a rapid increase in cumulative power generation losses. However, in summer, due to increased precipitation, dust is difficult to accumulate on photovoltaic modules, resulting in a slow increase in cumulative power generation losses. In addition, the DTW algorithm is employed to find similar days. Firstly, the entropy method is used to calculate the weights of each meteorological parameter. Then, the DTW values corresponding to each meteorological parameter on each historical day are calculated in reverse chronological order, multiplied by their weights, and added together to obtain the comprehensive DTW value for each historical day. By comparing the comprehensive DTW values of each historical day, the meteorological similar day that is closest to the current day is selected. In order to avoid extreme weather conditions, a portion of the dataset is selected as the validation set, and the criteria for finding similar days are optimized. The data from 9:00 to 15:00 each day is divided into three time periods for analysis, and the condition that the average solar irradiance is not less than 600 W/m2 is set. After optimization, the evaluation index determination coefficient of the prediction model is 0.83, and the root mean square error is 0.22, indicating a significant improvement in prediction performance. Finally, the algorithm is used to develop a cleaning strategy for the photovoltaic power plant. After comparing the cumulative power generation loss with the cleaning cost, it is determined that the power plant should be cleaned every 28 days under long-term non rainfall conditions.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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