A Weather-Based Hybrid Method for 1-Day Ahead Hourly Forecasting of PV Power Output

To improve real-time control performance and reduce possible negative impacts of photovoltaic (PV) systems, an accurate forecasting of PV output is required, which is an important function in the operation of an energy management system (EMS) for distributed energy resources. In this paper, a weathe...

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Bibliographic Details
Published in:IEEE transactions on sustainable energy 2014-07, Vol.5 (3), p.917-926
Main Authors: Yang, Hong-Tzer, Huang, Chao-Ming, Huang, Yann-Chang, Pai, Yi-Shiang
Format: Article
Language:English
Subjects:
Clouds
Forecasting
Fuzzy inference
learning vector quantization (LVQ)
Meteorology
photovoltaic (PV) output forecasting
Power generation
Predictive models
self-organizing map (SOM)
Support vector machine classification
support vector regression (SVR)
Training
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Summary:To improve real-time control performance and reduce possible negative impacts of photovoltaic (PV) systems, an accurate forecasting of PV output is required, which is an important function in the operation of an energy management system (EMS) for distributed energy resources. In this paper, a weather-based hybrid method for 1-day ahead hourly forecasting of PV power output is presented. The proposed approach comprises classification, training, and forecasting stages. In the classification stage, the self-organizing map (SOM) and learning vector quantization (LVQ) networks are used to classify the collected historical data of PV power output. The training stage employs the support vector regression (SVR) to train the input/output data sets for temperature, probability of precipitation, and solar irradiance of defined similar hours. In the forecasting stage, the fuzzy inference method is used to select an adequate trained model for accurate forecast, according to the weather information collected from Taiwan Central Weather Bureau (TCWB). The proposed approach is applied to a practical PV power generation system. Numerical results show that the proposed approach achieves better prediction accuracy than the simple SVR and traditional ANN methods.
ISSN:1949-3029
1949-3037
DOI:10.1109/TSTE.2014.2313600