Linear Regression for Forecasting Photovoltaic Power Generation

Hua Ping Guo; Shuang Hui Wu; Zhao Qing Wang; Chang An Wu

doi:10.4028/www.scientific.net/AMM.494-495.1771

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 494-495Linear Regression for Forecasting Photovoltaic...

Linear Regression for Forecasting Photovoltaic Power Generation

Abstract:

One key issue for knowledge discovery is to build a model with simple structure, high performance and interpretability. Linear regression is simple and interpretable model comparing to other models such as neural network. This paper introduces linear regression into photovoltaic power forecasting. Experimental results on the data set collected by Zhongwei third photovoltaic power station of Ningxia Jinyang new energy Co., Ltd. show that, compared with neural network, linear regression performs better generated power forecasting.

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Periodical:

Applied Mechanics and Materials (Volumes 494-495)

Pages:

1771-1774

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.494-495.1771

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Online since:

February 2014

Authors:

Hua Ping Guo*, Shuang Hui Wu, Zhao Qing Wang, Chang An Wu

Keywords:

Generated Power Forecasting, Linear Regression, Neural Network (NN)

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* - Corresponding Author

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