Photovoltaic power forecasting based on artificial neural network and meteorological data

Due to the intermittency and randomness of solar Photovoltaic (PV) power outputs, it is necessary to find a precise method for PV power forecasting. However, conventional methods, using only temperature, humidity and wind speed data, failed to obtain high accuracy when used to predict PV power outpu...

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Bibliographic Details
Published in2013 IEEE International Conference of IEEE Region 10 (TENCON 2013) pp. 1 - 4
Main Authors Jiahao Kou, Jun Liu, Qifan Li, Wanliang Fang, Zhenhuan Chen, Linlin Liu, Tieying Guan
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.01.2013
Subjects
Aerosol Index
Aerosols
Artificial Neural Network Method
BP Network
Data models
Forecasting
Maximum Absolute Prediction Error
Meteorology
Photovoltaic Power Forecasting
Power generation
Predictive models
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Summary:Due to the intermittency and randomness of solar Photovoltaic (PV) power outputs, it is necessary to find a precise method for PV power forecasting. However, conventional methods, using only temperature, humidity and wind speed data, failed to obtain high accuracy when used to predict PV power outputs under extreme weather conditions. Aerosol index which indicates particulate matter in the atmosphere has a strong correlation with PV generated energy. This paper proposes a novel photovoltaic power forecasting model considering aerosol index data as an additional input. Based on weather classification and back propagation artificial neural network approaches, the estimated results of the forecasting model show good coincidence with the measurement data. And the proposed model is able to improve the prediction accuracy of conventional methods using artificial neural network.
ISBN:9781479928255
1479928259
ISSN:2159-3442
2159-3450
DOI:10.1109/TENCON.2013.6718512