Abstract
The uncertainty and randomness of large-scale photovoltaic (PV) generation will seriously affect the safety and economy of the power systems. In order to mitigate the negative impact of grid-connected solar PV, it is important to accurately predict PV power. In view of this, this paper first proposes a novel hybrid method based on non-pooling convolutional neural network and deep deterministic policy gradient (DDPG) for ultra-short-term PV power forecasting. The DDPG model is used to implement the feature training task, and the NPCNN model is introduced into DDPG’s actor network to learn action strategies for partially observed problems in complex environments. The proposed method is extensively evaluated on real PV data in Limburg, Belgium. Numerical results show that the proposed method can provide good forecasting performance in different seasons and forecasting horizons.
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