A novel non-iterative correction method for short-term photovoltaic power forecasting. (October 2020)

• Record Type: Journal Article
• Authors: Yin, Wansi; Han, Yutong; Zhou, Hai; Ma, Ming; Li, Li; Zhu, Honglu
• Is Part Of: Renewable energy. Volume 159(2020)
• Page Start: 23
• Page End: 32
• Publication Date: 2020-10
• Subjects: Non-iterative correction -- Photovoltaic power -- Short-term forecasting -- Multi-model -- Error analysis; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.renene.2020.05.134
• Languages: English
• ISSNs: 0960-1481

Abstract

Abstract: Short-term photovoltaic (PV) power forecasting is of great significance for the real-time dispatching of power systems, but the accuracy of short-term forecasting of PV power is not satisfactory. Mastering the distribution characteristics of forecasting error and correcting the forecasting results are effective ways to improve the short-term forecasting accuracy. In this paper, the error distribution characteristics of short-term prediction results of PV power are studied, and then a non-iterative correction method for PV power short-term forecasting is proposed. The statistical result show that the error distribution is different in different seasons, the power forecasted error is strongly similar to the irradiance error distribution in numerical weather prediction (NWP). Therefore, this paper calculates the short-term forecasting results through seasonal models and uses non-iterative method to correct forecasting results, which can effective avoids the influence of accumulation errors. Compared with other methods, the root mean square error (RMSE) of this method is reduced by about 4.5%, and the mean absolute error (MAE) is reduced by about 2.6%, it shows the method can effectively improve the short-term forecasting accuracy of PV power. Highlights: NWP is the main source of power forecasting error. The real data are used to correct the forecasted results. The non-iterative method can improve the forecasting performance without cumulative error.