Short-Term Solar Irradiance Forecasting Model Based on Artificial Neural Network Using Statistical Feature Parameters

Energies

Fei Wang²  Zengqiang Mi²  Shi Su¹ 

[1] Yunnan Electric Power Test and Research Institute (Group) Co., Ltd., Electric Power Research Institute, Kunming 650217, Yunnan, China; E-Mail:;School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, Hebei, China; E-Mails:

关键词: artificial neural network (ANN);    forecasting;    statistical feature;    solar irradiance;   

DOI  :  10.3390/en5051355

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2012 by the authors; licensee MDPI, Basel, Switzerland.