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Analyzing the impact of electricity price forecasting on energy cost-aware scheduling
Date Issued
2014-12
Date Available
2017-05-12T09:44:34Z
Abstract
Energy cost-aware scheduling, i.e., scheduling that adapts to real-time energy price volatility, can save large energy consumers millions of dollars every year in electricity costs. Energy price forecasting coupled with energy price-aware scheduling, is a step toward this goal. In this work, we study cost-aware schedules and the effect of various price forecasting schemes on the end schedule-cost. We show that simply optimizing price forecasts based on classical regression error metrics (e.g., Mean Squared Error), does not work well for scheduling. Price forecasts that do result in significantly better schedules, optimize a combination of metrics, each having a different impact on the end-schedule-cost. For example, both price estimation and price ranking are important for scheduling, but they carry different weight. We consider day-ahead energy price forecasting using the Irish Single Electricity Market as a case-study, and test our price forecasts for two real-world scheduling applications: animal feed manufacturing and home energy management systems. We show that price forecasts that co-optimize price estimation and price ranking, result in significant energy-cost savings. We believe our results are relevant for many real-life scheduling applications that are currently plagued with very large energy bills.
Sponsorship
Irish Research Council
Science Foundation Ireland
Other Sponsorship
Intel Labs Europe
Type of Material
Journal Article
Publisher
Elsevier
Journal
Sustainable Computing: Informatics and Systems
Volume
4
Issue
4
Start Page
276
End Page
291
Copyright (Published Version)
2014 Elsevier
Language
English
Status of Item
Peer reviewed
This item is made available under a Creative Commons License
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Analyzing the impact of electricity price forecasting on energy cost aware scheduling.pdf
Size
1.42 MB
Format
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