TY - JOUR
T1 - Forecasting power output of photovoltaic systems based on weather classification and support vector machines
AU - Shi, Jie
AU - Lee, Wei Jen
AU - Liu, Yongqian
AU - Yang, Yongping
AU - Wang, Peng
N1 - Funding Information:
Manuscript received June 30, 2011; revised November 21, 2011; accepted January 5, 2012. Date of publication March 13, 2012; date of current version May 15, 2012. Paper 2011-ESC-303.R1, presented at the 2011 IEEE Industry Applications Society Annual Meeting, Orlando, FL, October 9–13, and approved for publication in the IEEE TRANSACTIONS ON INDUSTRY APPLI-CATIONS by the Energy Systems Committee of the IEEE Industry Applications Society. This work was supported by a grant from National Natural Science Foundation of China (50976034) and the Fundamental Research Funds for the Central Universities (09MG17). The work of J. Shi and Y. Liu was supported by National High Technology Research and Development Program 863: Wind Power Prediction Method Study and System Development (2007AA05Z428) to Yongqian Liu and Jie Shi.
PY - 2012
Y1 - 2012
N2 - Due to the growing demand on renewable energy, photovoltaic (PV) generation systems have increased considerably in recent years. However, the power output of PV systems is affected by different weather conditions. Accurate forecasting of PV power output is important for system reliability and promoting large-scale PV deployment. This paper proposes algorithms to forecast power output of PV systems based upon weather classification and support vector machines (SVM). In the process, the weather conditions are divided into four types which are clear sky, cloudy day, foggy day, and rainy day. In this paper, a one-day-ahead PV power output forecasting model for a single station is derived based on the weather forecasting data, actual historical power output data, and the principle of SVM. After applying it into a PV station in China (the capability is 20 kW), results show the proposed forecasting model for grid-connected PV systems is effective and promising.
AB - Due to the growing demand on renewable energy, photovoltaic (PV) generation systems have increased considerably in recent years. However, the power output of PV systems is affected by different weather conditions. Accurate forecasting of PV power output is important for system reliability and promoting large-scale PV deployment. This paper proposes algorithms to forecast power output of PV systems based upon weather classification and support vector machines (SVM). In the process, the weather conditions are divided into four types which are clear sky, cloudy day, foggy day, and rainy day. In this paper, a one-day-ahead PV power output forecasting model for a single station is derived based on the weather forecasting data, actual historical power output data, and the principle of SVM. After applying it into a PV station in China (the capability is 20 kW), results show the proposed forecasting model for grid-connected PV systems is effective and promising.
KW - Forecasting
KW - photovoltaic cell radiation effects
KW - photovoltaic systems
KW - support vector machine (SVM)
KW - weather classification
UR - http://www.scopus.com/inward/record.url?scp=84861374533&partnerID=8YFLogxK
U2 - 10.1109/TIA.2012.2190816
DO - 10.1109/TIA.2012.2190816
M3 - Article
AN - SCOPUS:84861374533
SN - 0093-9994
VL - 48
SP - 1064
EP - 1069
JO - IEEE Transactions on Industry Applications
JF - IEEE Transactions on Industry Applications
IS - 3
M1 - 6168891
ER -