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Analyzing the Impact of Electricity Price Forecasting on Energy Cost-Aware Scheduling

Publication Type: 
Refereed Original Article
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 towards this goal. In this work, we study cost-aware schedules and the e ect 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 signi cantly better schedules, optimize a combination of metrics, each having a di erent impact on the end-schedule- cost. For example, both price estimation and price ranking are important for scheduling, but they carry di erent 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 signi cant energy-cost savings. We believe our results are relevant for many real-life scheduling applications that are currently plagued with very large energy bills.
Digital Object Identifer (DOI): 
10.1016/j.suscom.2014.08.009
Publication Status: 
Published
Date Accepted for Publication: 
Tuesday, 12 August, 2014
Publication Date: 
03/09/2014
Journal: 
Elsevier Journal on Sustainable Computing, Informatics and Systems
Institution: 
National University of Ireland, Dublin (UCD)
Project Acknowledges: 
Open access repository: 
No