Four-Stage Space-Time Hybrid Model For Distributed Photovoltaic Power Forecasting

Abstract

The precision and reliability of distributed photovoltaic (PV) power forecasting play a crucial role in commercial PV plants for power integration and operation. However, the stochastic and intermittent nature of solar radiation affects forecasting accuracy. What's more, the diverse location and various meteorological information have brought challenges to real-time distributed forecasting. Therefore, it is more difficult for forecasting distributed power than centralized power. To solve this problem, we propose a four-stage space-time hybrid method for power forecasting of distributed PV plants. Firstly, Global and Local Feature Fusion Network (GLFFNet) is proposed to forecast the power output of centralized PV plants. Secondly, the weather clustering type is obtained to get ready for studying the relationship between centralized and distributed PV plants. Then, uncovered coefficients are figured out to show the distribution features of PV power both in the time and space domain. Finally, the correlation models are established based on Copula analysis. According to the above steps, the centralized forecasting results are transformed to obtain the distributed forecasting model. The case study shows that the proposed GLFFNet is more suitable for 1-hour ahead forecasting. Compared to 9 different neural models, MAPE reductions of GLFFNet model (time steps of 1 hour and 15 minutes) are 12.17%–23.23% and 3.18%–18.22%, respectively. Thus, MAPE of the case distributed PV power forecasting models are 0.62% (Type A), 0.96% (Type B) and 3.21% (Type C), respectively.