NDLI: A pareto optimization approach of a Gaussian process ensemble for short-term load forecasting

Content Provider	IEEE Xplore Digital Library
Author	Alamaniotis, M. Ikonomopoulos, A. Tsoukalas, L.H.
Copyright Year	2011
Description	Author affiliation: Applied Intelligent Systems Laboratory, School of Nuclear Engineering, Purdue University, West Lafayette IN 47907 USA (Alamaniotis, M.; Tsoukalas, L.H.) \|\| Institute of Nuclear Technology - Radiation Protection, National Center of Scientific Research “DEMOKRITOS”, Agia Paraskevi, Athens, 15310 Greece (Ikonomopoulos, A.)
Abstract	Accurate prediction of load demand remains a challenge for efficient power distribution and becomes critical in the context of smart grid management when the presence of stochastic sources adds to the stochasticity of demand. Short-term load forecasting involving demand prediction in the range of hours or days is of special interest to generators and power customers. A number of methods has been developed for fast and accurate electric power forecasting. Among others, Gaussian process (GP) regression has been used for prediction in the nonlinear problems with promising results. On that direction, an ensemble of Gaussian process regressors modeled as kernel machines is proposed for load forecasting. The use of different kernels accommodates the construction of a group composed of different predictors and its evolution using genetic algorithms. The proposed approach takes the form of a multiobjective problem in which the objectives consist of a set of criteria. In order to optimize all the criteria it needs to use Pareto optimality to identify an accepted solution. The results obtained show that the ensemble of GP predictors outperforms each individual forecaster.
Starting Page	1
Ending Page	6
File Size	245764
Page Count	6
File Format	PDF
ISBN	9781457708077
e-ISBN	9781457708091
e-ISBN	9781457708084
DOI	10.1109/ISAP.2011.6082231
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-09-25
Publisher Place	Greece
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Load forecasting Gaussian processes Pareto optimization Short-term Load Forecasting Kernel Forecasting Genetic Algorithms Genetic algorithms
Content Type	Text
Resource Type	Article

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