Abstract Details
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Activity Number:
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28
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Type:
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Topic Contributed
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Date/Time:
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Sunday, August 4, 2013 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistical Learning and Data Mining
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| Abstract - #310296 |
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Title:
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Electricity Load Forecasting on Smart Grid
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Author(s):
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Sining Chen*+
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Companies:
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Bell Labs, Alcatel-Lucent
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Keywords:
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Abstract:
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The smart grid is the future of electricity distribution, monitoring and management. In terms of management, it is important for the power supplier to be able to predict the consumption in the immediate future, e.g. 1 hour ahead of time, 24 hours ahead. We describe the characteristics of the data from smart grid, and the problem of load forecasting using these datasets. We discuss the use of SARIMA models and spline-based state space model before and after taking into account of the heating-cooling effect. We then compare the performance of each model.
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Authors who are presenting talks have a * after their name.
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