Multi-scale regional photovoltaic power generation forecasting method based on sequence coding reconstruction
Diyang Gong, Nan chen, Qingfeng Ji, Yajie Tang, Yizhi Zhou
Abstract
This paper proposes a solution for the challenges faced in predicting the output of distributed solar power stations due to difficulties in obtaining and utilizing data. The proposed method is a multi-scale regional photovoltaic power generation forecasting approach that uses sequence coding reconstruction. Firstly, the reference sites were selected to enhance the data in time domain and frequency domain for the limited site dataset. Then, the forecasting model of sequence coding reconstruction is constructed based on convLSTM, and the attention mechanism is added in the decoding process, so that it can make full use of the spatio-temporal correlation characteristics of regional photovoltaic power and realize the rolling forecasting of regional photovoltaic power. The result shows that the proposed method can maintain a good multi-scale regional photovoltaic power forecasting effect under the condition of limited data amount and low data collection cost, the accuracy of the proposed model is increased compared with the naive forecasting method.