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.
关键词
太阳辐射强度预测/深度学习/可解释性/LRP算法/LSTM
Key words
solar irradiation prediction/deep learning/interpretability/LRP algorithm/LSTM
维普
万方数据