关键词: 深度强化学习, 光伏功率预测, 深度学习, 深度确定性策略梯度, 循环确定性策略梯度, 注意力机制

Abstract: To address the problems that traditional models were affected by random power volatility and tend to ignore important information resulting in low prediction accuracy, two models of Deep Deterministic Policy Gradient (DDPG) and Recurrent Deterministic Policy Gradient (RDPG) based on Attention mechanism, ADDPG and ARDPG, were proposed and a PV power prediction framework was proposed based on the proposed models. Firstly, the original PV power data and meteorological data were normalized. Then, the PV power prediction problem was modeled as a Markov Decision Process (MDP), where historical power data and current weather data were used as state. Secondly, attention mechanism was added to the Actor network of DDPG and RDPG. The important information in each important component was given prominence. Irrelevant information was ignored and the instability of the original PV power was reduced to make the optimal action, which was the power prediction value. Thirdly, the DRL agents interacted with the historical data was used to learn the critical information in the data. Finally, the MDP problem was solved to obtain the optimal strategy to make an accurate prediction. The proposed models are experimented on DKASC, Alice Springs PV system. The experimental results show that under the evaluation index of Root Mean Square Error(RMSE), compared with DDPG and RDPG, the prediction errors of ADDPG and ARDPG are reduced by 24.24% and 31.34%. And compared with Long Short-Term Memory (LSTM) model with attention mechanism, the prediction errors of ADDPG and ARDPG are reduced by 26.89% and 18.76%. The proposed models can effectively improve the prediction accuracy of PV power, and can be extended to other prediction fields such as grid prediction, wind power prediction, etc.

Key words: deep reinforcement learning, photovoltaic power prediction, deep learning, Deep Deterministic Policy Gradient(DDPG), Recurrent Deterministic Policy Gradient(RDPG), attention mechanism