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Short term energy forecasting techniques for virtual power plants
Published in Institute of Electrical and Electronics Engineers Inc.
2016
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. © 2016 IEEE.
About the journal
JournalData powered by Typeset2016 IEEE 6th International Conference on Power Systems, ICPS 2016
PublisherData powered by TypesetInstitute of Electrical and Electronics Engineers Inc.
Open AccessYes
Concepts (11)
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    Energy utilization
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    Neural networks
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    ELECTRICITY GRIDS
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    ENERGY FORECASTING
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    HISTORICAL WEATHER DATUM
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    Load forecasting
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    NEW SOUTH WALES
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    PERIODIC MONITORING
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    VIRTUAL POWER PLANTS
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    VIRTUAL POWER PLANTS (VPP)
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    Forecasting