An integrated ultra short term power forecasting method for regional wind–pv–hydro

Abstract

Renewable-based multi energy power system is the main trend for power system in the future. However, the randomness and fluctuation of wind and photovoltaic power, as well as the seasonality of hydropower, have an increasingly prominent impact on the stability of power system. Accurate power forecasting technology is the key to solve the above problems. At the same time, the output characteristics of heterogeneous energy sources are very different, and the existing forecasting methods are difficult to fully exploit their spatio-temporal correlation characteristics, which limits the improvement of prediction accuracy. In this paper, an integrated ultra short term power forecasting method for regional wind–pv–hydro is proposed, Firstly, it quantifies the differences and similarities among wind, pv and hydro in different scenarios based on empirical mode decomposition, and achieves the extraction of homogeneous features, on this basis, the integrated power forecasting models for wind, pv and hydro based on long short-term memory neural networks is constructed , and achieves regional-level integrated forecasting of wind, pv and hydro. The results show that the forecasting accuracy and modeling efficiency of the proposed integrated forecasting method are significantly improved compared with the traditional independent forecasting method, the forecasting accuracy is increased by 1%–3%, the modeling efficiency is increased by 6 times.