National Institute of Advanced Industrial Science and Technology
Institute of Industrial Science (IIS), Collaborative Research Center for Energy Engineering (CEE), Tokyo University
抄録
The development of methods to forecast PV power generation regionally is of utmost importance to support the spread of such power systems in current power grids. The objective of this study is to propose and to evaluate methods to forecast regional PV power one-day ahead of time and to compare their performances. Four forecast methods were regarded of which 2 are new ones proposed in this study. Together they characterize a set of forecast methods that can be applied in different scenarios regarding availability of data and infrastructure to make the forecasts. The forecast methods were based on the use of support vector regression and weather prediction data. Evaluations were done for 1 year of hourly forecasts using data of 273 PV systems installed in 2 adjacent regions in Japan, Kanto and Chubu. The results show the importance of selecting the proper forecast method regarding the region characteristics. For Chubu, the region with a variety of weather conditions, the forecast methods based on single systems’ forecasts and the one based on stratified sampling provided the best results. In this case the best annual normalized RMSE and MAE were 0.25 kWh/kWhavg and 0.15 kWh/kWhavg. For Kanto, with homogeneous weather conditions, the 4 methods performed similarly. In this case, the lowest annual forecast errors were 0.33 kWh/kWhavg for the normalized RMSE and 0.202 kWh/kWhavg for the normalized MAE.
Regional Forecasts of Photovoltaic Power Generation According to Different Data Availability Scenarios: A Study of 4 Methods
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