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Short term energy forecasting techniques for virtual power plants
Ravichandran, S. Vijayalakshmi, A. Swarup, K.S. Rajamani, Haile S. Pillai, Prashant
Ravichandran, S.
Vijayalakshmi, A.
Swarup, K.S.
Rajamani, Haile S.
Pillai, Prashant
Publication Date
2016-10-06
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© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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Abstract
The advent of smart meter technology has enabled periodic monitoring of consumer energy consumption. Hence, short term energy forecasting is gaining more importance than conventional load forecasting. An Accurate forecasting of energy consumption is indispensable for the proper functioning of a virtual power plant (VPP). This paper focuses on short term energy forecasting in a VPP. The factors that influence energy forecasting in a VPP are identified and an artificial neural network based energy forecasting model is built. The model is tested on Sydney/ New South Wales (NSW) electricity grid. It considers the historical weather data and holidays in Sydney/ NSW and forecasts the energy consumption pattern with sufficient accuracy.
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Accepted manuscript
Citation
Ravichandran S, Vijayalakshmi A, Swarup KS et al (2016) Short term energy forecasting techniques for virtual power plants. In: 2016 IEEE 6th International Conference on Power Systems (ICPS). 4-6 Mar 2016, New Delhi, India.
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Conference paper