Integrated Methods for Optimization (International Series in Operations Research & Management Science)
Models for Global Constraint Applications
Constraints
A Survey of Uncertain Data Algorithms and Applications
IEEE Transactions on Knowledge and Data Engineering
Cutting the electric bill for internet-scale systems
Proceedings of the ACM SIGCOMM 2009 conference on Data communication
Statistical machine learning makes automatic control practical for internet datacenters
HotCloud'09 Proceedings of the 2009 conference on Hot topics in cloud computing
Energy aware consolidation for cloud computing
HotPower'08 Proceedings of the 2008 conference on Power aware computing and systems
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Operating cost aware scheduling model for distributed servers based on global power pricing policies
COMPUTE '11 Proceedings of the Fourth Annual ACM Bangalore Conference
Online job-migration for reducing the electricity bill in the cloud
NETWORKING'11 Proceedings of the 10th international IFIP TC 6 conference on Networking - Volume Part I
Energy-cost-aware scheduling of HPC workloads
WOWMOM '11 Proceedings of the 2011 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks
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Wholesale electricity markets are becoming ubiquitous, offering consumers access to competitively-priced energy. The cost of energy is often correlated with its environmental impact; for example, environmentally sustainable forms of energy might benefit from subsidies, while the use of high-carbon sources might be discouraged through taxes or levies. Reacting to real-time electricity price fluctuations can lead to high cost savings, in particular for large energy consumers such as data centres or manufacturing plants. In this paper we focus on the challenge of day-ahead energy price prediction, using the Irish Single Electricity Market Operator (SEMO) as a case-study. We present techniques that significantly out-perform SEMO's own prediction. We evaluate the energy savings that are possible in a production scheduling context, but show that better prediction does not necessarily yield energy-cost savings. We explore this issue further and characterize, and evaluate, important properties that an energy price predictor must have in order to give rise to significant scheduling-cost savings in practice.