An adaptive hybrid model for day-ahead photovoltaic output power prediction

Abstract

Abstract Accurate and stable photovoltaic (PV) output power prediction is important for the secure, stable and economic operation of power gird. However, due to the indirectness, randomness and volatility of solar energy, accurate and stable PV output power prediction has become a very challenging issue. To obtain a more accurate and stable prediction results, an adaptive hybrid model combined with improved variational mode decomposition (IVMD), autoregressive integrated moving average (ARIMA) and improved deep belief network (IDBN) is developed to predict day-ahead PV output power. First, the original PV output power is decomposed into some regular and irregular components by IVMD. Second, the regular components are predicted by ARIMA, while irregular components are predicted by IDBN. Third, the final forecasting results is obtained by summing the prediction results of each component. The prediction performance is validated by comparing with some other models. Experimental results illustrate that the presented model can improve the prediction performance of PV output power than other models.