Ultra‐short‐term photovoltaic power forecasting of multifeature based on hybrid deep learning

Abstract

The output power of photovoltaic power has randomness and volatility, which poses new challenges to the peak shaving and dispatching of the power system. Therefore, the accurate prediction of photovoltaic power output is an effective way to maintain the security and stability of the power grid. In this article, a hybrid prediction model based on improved convolutional neural network and bidirectional gated recurrent unit is proposed to predict PV output power. Firstly, the concept of time relevance is introduced to fuse this feature with weather characteristics as the input of the prediction model. Secondly, the time series generated adversarial neural network is applied to the field of photovoltaic power generation prediction for the first time to enhance the dataset obtained. Thirdly, the model proposed in this article is trained and compared with other models under different characteristics, different weather conditions, and different forecast time steps. The experimental results show that the hybrid deep learning model proposed in this article has better prediction performance. Under different characteristics, the model proposed in this article has the highest prediction accuracy. After data enhancement, the prediction accuracy of each model has been improved by 1% to 3%. Among them, the prediction accuracy of the model proposed in this article is 0.977 and 0.960, respectively. Under different weather conditions and different time steps, the proposed model has the best stability. Under cloudy and rainy weather, the prediction accuracy of the proposed model only decreases by about 6%. In addition, the prediction accuracy of the proposed model only decreases by approximately 1.5% to 4% from 15 to 180 minutes.