Regional photovoltaic installed capacity forecasting based on granger causality test and grey support vector machine

Forecasting results of regional photovoltaic (PV) installed capacity can provide important references for electric utilities and energy authorities. This paper proposes a three-step forecasting methodology of regional PV installed capacity. The first step is to study the relationship between regional PV installed capacity and a series of potential factors using co-integration analysis and Granger causality test; second, dimensionality reduction of influential factors is carried out by principal component analysis; in the last step, a grey support vector machine forecasting model is constructed based on the outcome of influential factors reduction. The example of Shanghai is given to illustrate the proposed methodology. Results show that factors such as electricity consumption, and generating cost of PV are closely related to PV installed capacity, nevertheless, sunshine duration and average temperature are not the Granger reasons for it; these findings are of great importance to the forecasting of 2016~2030 PV installed capacity in Shanghai.