研究目的
To analyze the errors on estimation and forecast of regional PV power generation with upscaling method by using monitoring data obtained from 2219 small PV plants in Kyushu, Japan.
研究成果
The study found that random sampling is sufficient for upscaling forecasts when utilizing a large number of reference plants. The minimum RMSEs and uncertainty of estimations decreased with an increasing number of reference plants, while for forecasts, the uncertainty decreased but the minimum RMSEs remained flat at sufficient numbers of reference plants.
研究不足
The study focused on time-averaged evaluation without considering the temporary impact of clouds traversing the region. Future studies could investigate the dependence of error on spatial distribution and include monitored data from higher voltage levels.
1：Experimental Design and Method Selection:
The study used an upscaling algorithm for evaluating and forecasting regional PV power generation. The methodology involved random sampling of PV plants and calculating estimation and forecast errors.
2：Sample Selection and Data Sources:
Monitoring data from 2219 PV plants in Kyushu, Japan, were used. The data included power output measurements with a time resolution of 30 minutes.
3：List of Experimental Equipment and Materials:
The study utilized monitoring data from PV plants and numerical weather prediction models for forecasting.
4：Experimental Procedures and Operational Workflow:
Random selections of PV plants were made, and estimation and forecast errors were calculated over the analysis period. The study also involved blending models for short-term forecasts.
5：Data Analysis Methods:
The errors were analyzed using root mean square error (RMSE) calculations, and the results were categorized by the number of reference plants.
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