Skip to main content

Stochastic Short-term High-resolution Prediction of Solar Irradiance and Photovoltaic Power Output

by Mohammed M Olama, Alexander Melin, Jin Dong, Seddik M Djouadi, Yichen Zhang
Publication Type
Conference Paper
Book Title
Proceedings of the 49th North American Power Symposium (NAPS)
Publication Date
Page Numbers
1 to 6
Publisher Location
New York, New York, United States of America
Conference Name
The 49th IEEE North American Power Symposium (NAPS 2017)
Conference Location
Morgantown, West Virginia, United States of America
Conference Sponsor
Conference Date

The increased penetration of solar photovoltaic (PV) energy sources into electric grids has increased the need for accurate modeling and prediction of solar irradiance and power production. Existing modeling and prediction techniques focus on long-term low-resolution prediction over minutes to years. This paper examines the stochastic modeling and short-term high-resolution prediction of solar irradiance and PV power output. We propose a stochastic state-space model to characterize the behaviors of solar irradiance and PV power output. This prediction model is suitable for the development of optimal power controllers for PV sources. A filter-based expectation-maximization and Kalman filtering mechanism is employed to estimate the parameters and states in the state-space model. The mechanism results in a finite dimensional filter which only uses the first and second order statistics. The structure of the scheme contributes to a direct prediction of the solar irradiance and PV power output without any linearization process or simplifying assumptions of the signal’s model. This enables the system to accurately predict small as well as large fluctuations of the solar signals. The mechanism is recursive allowing the solar irradiance and PV power to be predicted online from measurements. The mechanism is tested using solar irradiance and PV power measurement data collected locally in our laboratory.