A Review of Multitemporal and Multispatial Scales Photovoltaic Forecasting Methods

Abstract

Reliable photovoltaic(PV) forecasting can provide important data support for power system operation, which is the key to realize the large-scale consumption of solar energy resources. PV forecasting task becomes crucial to ensure power system stability and economic operation. This paper reviews the existing research of PV forecasting methods from the perspective of multi-temporal scale and multi-spatial scale. Firstly, according to the forecasting process, demand, temporal and spatial scale, the forecasting methods are classified and the evaluation indicators involved in the research are listed. Secondly, based on the temporal scale of PV power generation, the results are combed through the three kind of scale of ultra-short-term, short-term and medium and long-term prediction. Thirdly, on each kind of temporal scale, the results are subdivided into single-site prediction and regional prediction to sort out in detail. Finally, the results are analyzed on the basis of the predicted temporal scale, spatial scale and input data. It has been observed that most recent papers highlight the importance of short-term predictions. The machine learning method shows excellent nonlinear description ability in short-term prediction, the prediction results are satisfactory. The spatial average effect of regional prediction reduces the variability of solar energy, the prediction results are reliable.

Keywords

• Solar energy
• photovoltaic forecasting
• machine learning
• multi-temporal scale
• multi-spatial scale