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Accurately predicting solar irradiation (SI) is crucial for power scheduling and photovoltaic site selection. With the development of high-performance computing and large-capacity storage devices, data-driven deep learning models have gained widespread attentions in the SI prediction domain. However, the lack of physical interpretability due to the “black-box” nature of deep learning models restricts their credibility in specific scenarios. To enhance the interpretability of the model on the premise of maintaining prediction accuracy and keeping the model structure unchanged, and without increasing computational complexity, a model based on long short-term memory (LSTM) neural network is constructed, demonstrating an 8.07% performance improvement over the conventional neural networks and showing superior outlier handling capabilities. By employing layer-wise relevance propagation (LRP) algorithm, factors influencing the model output are scored from both temporal and spatial dimensions, enhancing the model’s interpretability. The research results indicate that the model possesses good interpretability under the premise of ensuring performance, with historical solar irradiation, time-related features (such as hour, day, week, month), solar altitude information (such as sunrise and sunset times), cloud cover, radiation time, temperature, and dew point temperature being the main factors influencing SI prediction.
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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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