Short-Term Solar Irradiance Forecasting Model Based on Artificial Neural Network Using Statistical Feature ParametersReport as inadecuate




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1

School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, Hebei, China

2

State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Baoding 071003, Hebei, China

3

Yunnan Electric Power Test and Research Institute Group Co., Ltd., Electric Power Research Institute, Kunming 650217, Yunnan, China





*

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Abstract Short-term solar irradiance forecasting STSIF is of great significance for the optimal operation and power predication of grid-connected photovoltaic PV plants. However, STSIF is very complex to handle due to the random and nonlinear characteristics of solar irradiance under changeable weather conditions. Artificial Neural Network ANN is suitable for STSIF modeling and many research works on this topic are presented, but the conciseness and robustness of the existing models still need to be improved. After discussing the relation between weather variations and irradiance, the characteristics of the statistical feature parameters of irradiance under different weather conditions are figured out. A novel ANN model using statistical feature parameters ANN-SFP for STSIF is proposed in this paper. The input vector is reconstructed with several statistical feature parameters of irradiance and ambient temperature. Thus sufficient information can be effectively extracted from relatively few inputs and the model complexity is reduced. The model structure is determined by cross-validation CV, and the Levenberg-Marquardt algorithm LMA is used for the network training. Simulations are carried out to validate and compare the proposed model with the conventional ANN model using historical data series ANN-HDS, and the results indicated that the forecast accuracy is obviously improved under variable weather conditions. View Full-Text

Keywords: artificial neural network ANN; forecasting; statistical feature; solar irradiance artificial neural network ANN; forecasting; statistical feature; solar irradiance





Author: Fei Wang 1,2,* , Zengqiang Mi 1,2, Shi Su 3 and Hongshan Zhao 1,2

Source: http://mdpi.com/



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