An adaptive hybrid model for day-ahead photovoltaic output power prediction. (20th January 2020)

• Record Type: Journal Article
• Authors: Zhang, Jinliang; Tan, Zhongfu; Wei, Yiming
• Is Part Of: Journal of cleaner production. Volume 244(2020)
• Publication Date: 2020-01-20
• Subjects: PV output power prediction -- IVMD -- ARIMA -- IDBN; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2019.118858
• Languages: English
• ISSNs: 0959-6526

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. Highlights: Adaptive computational model aims at developing efficient solution technique. Complex features of PV output power can be well captured by the presented model. Presented model can obtain more accurate and stable forecasting results. Validation and verification of different models through the real cases.