Predicting Solar Irradiance using Time Series Neural Networks

Author

Ahmad Alzahrani
Jonathan W. Kimball, Missouri University of Science and TechnologyFollow
Cihan H. Dagli, Missouri University of Science and TechnologyFollow

Abstract

Increasing the accuracy of prediction improves the performance of photovoltaic systems and alleviates the effects of intermittence on the systems stability. A Nonlinear Autoregressive Network with Exogenous Inputs (NARX) approach was applied to the Vichy-Rolla National Airport's photovoltaic station. The proposed model uses several inputs (e.g. time, day of the year, sky cover, pressure, and wind speed) to predict hourly solar irradiance. Data obtained from the National Solar Radiation Database (NSRDB) was used to conduct simulation experiments. These simulations validate the use of the proposed model for short-term predictions. Results show that the NARX neural network notably outperformed the other models and is better than the linear regression model. The use of additional meteorological variables, particularly sky cover, can further improve the prediction performance.

Recommended Citation

A. Alzahrani et al., "Predicting Solar Irradiance using Time Series Neural Networks," Procedia Computer Science, vol. 36, pp. 623 - 628, Elsevier, Nov 2014.

The definitive version is available at https://doi.org/10.1016/j.procs.2014.09.065

Meeting Name

Complex Adaptive Systems (2014: Nov. 3-5, Philadelphia, PA)

Department(s)

Electrical and Computer Engineering

Second Department

Engineering Management and Systems Engineering

Keywords and Phrases

Balloons; Forecasting; Linear Regression; Photovoltaic Cells; Photovoltaic Effects; Regression Analysis; Solar Radiation; System Stability; Time Series; Wind; Adaptive Systems; Linear Regression Models; Meteorological Variables; NARX; Nonlinear Autoregressive Network With Exogenous Inputs; NSRDB; Photovoltaic Systems; Prediction Performance; Short Term Prediction; Complex Networks; Time Series Neural Network

International Standard Serial Number (ISSN)

1877-0509

Document Type

Article - Conference proceedings

Document Version

Final Version

File Type

text

Language(s)

English

Rights

© 2014 Elsevier, All rights reserved.

Creative Commons Licensing

This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License.

Publication Date

01 Nov 2014