Premium
Regional forecasts of photovoltaic power generation according to different data availability scenarios: a study of four methods
Author(s) -
Fonseca Junior Joao Gari da Silva,
Oozeki Takashi,
Ohtake Hideaki,
Takashima Takumi,
Ogimoto Kazuhiko
Publication year - 2015
Publication title -
progress in photovoltaics: research and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.286
H-Index - 131
eISSN - 1099-159X
pISSN - 1062-7995
DOI - 10.1002/pip.2528
Subject(s) - mean squared error , photovoltaic system , meteorology , forecast error , environmental science , forecast verification , regression analysis , statistics , forecast skill , computer science , mathematics , econometrics , engineering , geography , electrical engineering
Abstract The development of methods to forecast photovoltaic (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 1 day ahead of time and to compare their performances. Four forecast methods were regarded, of which two 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 performed for 1 year of hourly forecasts using data of 273 PV systems installed in two 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 root mean square error (RMSE) and mean absolute error (MAE) were 0.25 and 0.15 kWh/kWh avg , respectively. For Kanto, with homogeneous weather conditions, the four methods performed similarly. In this case, the lowest annual forecast errors were 0.33 kWh/kWh avg for the normalized RMSE and 0.202 kWh/kWh avg for the normalized MAE. Copyright © 2014 John Wiley & Sons, Ltd.