Indian Journal of Science and Technology
DOI: 10.17485/ijst/2015/v8iS1/60527
Year: 2015, Volume: 8, Issue: Supplementary 1, Pages: 1-10
Original Article
Yoon-Su Jeong1 , Seung-Hee Lee2*, Kun-Hee Han3 , Duchwan Ryu4 , Yoonsung Jung5
1 Department Information & Communication Engineering, Mokwon University, South Korea; [email protected]
2 Department of Business Administration, Kumoh National Institute of Technology, South Korea; [email protected]
3 Department of Information Communication & Engineering, Baeseok University, South Korea; [email protected]
4 Division of Statistics, College of Liberal Arts and Sciences, Northern Illinois University, United States; [email protected]
5 Cooperative Agricultural Research Center, Prairie View A&M University, United States; [email protected]
Background/Objectives: For efficient PhotoVoltaic (PV) power generation, computing and information technologies are increasingly used in irradiance forecasting and correction. Methods/Statistical Analysis: Today the majority of PV modules are used for grid-connected power generation, so solar generation forecasting that predicts available PV output ahead is essential for integrating PV resources into electricity grids. This paper proposes a short-term solar power forecasting system that employs Neural Network (NN) models to forecast irradiance and PV power. Results: The proposed system uses the weather observations of a ground weather station, the medium-term weather forecasts of a physical model, and the short-term weather forecasts of the Weather Research and Forecasting (WRF) model as input. To increase prediction accuracy, the proposed system performs forecast corrections and determines the correction coefficients based on the characteristics and temperature of PV modules. The proposed system also analyzes the inclination angle of PV modules to predict PV power outputs. Conclusion/Application: In the future, the proposed forecasting system for solar power generation resources will be further refined and run in real environments.
Keywords: Distributed Power Generation, Forecasting System, Photovoltaic Power Forecasts, Solar Energy, Wind Power
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