Analyzing the impact of electricity price forecasting on energy cost-aware scheduling

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Title: Analyzing the impact of electricity price forecasting on energy cost-aware scheduling
Authors: Grimes, Diarmuid
Ifrim, Georgiana
O'Sullivan, Barry
Simonis, Helmut
Permanent link: http://hdl.handle.net/10197/8508
Date: Dec-2014
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.
Funding Details: Irish Research Council
Science Foundation Ireland
Type of material: Journal Article
Publisher: Elsevier
Copyright (published version): 2014 Elsevier
Keywords: Machine learning;Statistics;Cost-aware-scheduling;Energy-price-forecasting;Smartgrid;Energy efficiency
DOI: 10.1016/j.suscom.2014.08.009
Language: en
Status of Item: Peer reviewed
Appears in Collections:Insight Research Collection

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