Title :
Irradiance forecasting for the photovoltaic systems
Author :
Jiaming Li ; Ward, John K.
Author_Institution :
CCI, CSIRO, Canberra, ACT, Australia
Abstract :
Global warming has emerged as a key environmental issue, with one of the most exciting approaches to greenhouse gas reductions being the use renewable energy. Photovoltaic (PV) generation of electricity is an important renewable energy source, especially at small scale, such as in homes. To increase the value of small-scale renewable generators, one efficient way is to aggregate and control their output and group them in zones. One challenge of doing this is the precise forecasting of each PV output. This paper introduces our developed irradiance forecasting algorithm based on the data from a sky camera. A SVM regression technique is used to generate regress curves between future irradiance and other available information. A series of experimental results are presented to evaluate and demonstrate our forecasting accuracy.
Keywords :
air pollution control; global warming; photovoltaic power systems; power engineering computing; regression analysis; support vector machines; PV generation; PV output forecasting; SVM regression technique; curve regression; global warming; greenhouse gas reductions; irradiance forecasting algorithm; photovoltaic systems; renewable energy; renewable energy source; sky camera; small-scale renewable generators; Accuracy; Electricity; Green products; Microeconomics; Predictive models; Irradiance Forecasting; Photovoltaic System; SVM Regression; Sky Camera; Solar Prediction; Virtual Power System;
Conference_Titel :
Modelling, Identification & Control (ICMIC), 2014 Proceedings of the 6th International Conference on
Conference_Location :
Melbourne, VIC
DOI :
10.1109/ICMIC.2014.7020778