2011 IEEE PES Innovative Smart Grid Technologies 2011
DOI: 10.1109/isgt-asia.2011.6167098
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Load demand forecasting: Model inputs selection

Abstract: Abstract-Developing a good demand forecasting model is the art of identifying the best modelling parameters. Improving the forecasting performance needs to study the input/output parameters of the system to identify the effective forecasting variables. In this paper, the energy demand of Joondalup Campus of Edith Cowan University (ECU) in Western Australia has been selected as a case study for the design and verification of a suitable forecasting model. Fuzzy Subtractive Clustering Method (FSCM) based Adaptive… Show more

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Cited by 5 publications
(2 citation statements)
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“…The number of hours per stage ( h ) determines the granularity of a stage. Its demand for resources, denoted by D , is known (or estimated using the mechanisms described in [ 25 ]) at every stage of execution of the application. The predicted values of the demand vector are available at each stage t , .…”
Section: System Model and Problem Formulationmentioning
confidence: 99%
“…The number of hours per stage ( h ) determines the granularity of a stage. Its demand for resources, denoted by D , is known (or estimated using the mechanisms described in [ 25 ]) at every stage of execution of the application. The predicted values of the demand vector are available at each stage t , .…”
Section: System Model and Problem Formulationmentioning
confidence: 99%
“…Multi-objective optimization based on modified game theory is applied in [117] to the environmental and economic problems of the MG. T.S. Mahmoud introduces in [119] T. Niknam et al propose in [122] a probabilistic approach for economic/emission management of microgrids from a probabilistic optimization method, including uncertainties covering and a modified multi-objective algorithm based on the MGSA to find Pareto-optimal front of the operation management problem.…”
Section: Operations Scheduling: the Economic Load Dispatch Problemmentioning
confidence: 99%