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Coincident Peak Prediction Using a Feed-Forward Neural Network

Citation Author(s):
Daniel Kirschen, Baosen Zhang
Submitted by:
Chase Dowling
Last updated:
28 November 2018 - 2:08pm
Document Type:
Presentation Slides
Document Year:
2018
Event:
Presenters:
Chase Dowling
Paper Code:
1298
 

A significant portion of a business' annual electrical payments can be made up of coincident peak charges: a transmission surcharge for power consumed when the entire system is at peak demand. This charge occurs only a few times annually, but with per-MW prices orders of magnitudes higher than non-peak times. A business is incentivized to reduce its power consumption, but accurately predicting the timing of peak demand charges is nontrivial. In this paper we present a decision framework based on predicting the day-ahead likelihood of peak demand charges. We train a feed-forward neural network to estimate the probability of system demand peaks and show it outperforms conventional forecasting methods using historical load. Using ERCOT demand and weather data from 2010-2017, we show the effectiveness of our framework.

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