Title :
Forecasting the power consumption of a single domestic electric water heater for a direct load control program
Author :
Shaad, M. ; Momeni, A. ; Diduch, C.P. ; Kaye, M.E. ; Chang, L.
Author_Institution :
Fac. of Electr. & Comput. Eng, Univ. of New Brunswick, Fredericton, NB, Canada
Abstract :
Thermal storage units such as residential electric hot waters are feasible candidates to be used in a direct load control programs. They can store electric power in form of heat for later use. PowerShift Atlantic (PSA) is a leading project in developing a demand-side management program to provide up to 32MW reserve capacity through electric water heaters. To control water heaters properly, the controller needs to have an estimation of the on/off status of each individual water heaters in advance. This paper presents a monte-carlo statistical based method to create a short-term load forecast of the individual loads. This method was compared with a traditional neural network based load forecast. This paper also presents a model to estimate the relative error of the aggregated load forecast. The proposed methods were deployed on the PSA pilot and the experimental results are discussed in this paper.
Keywords :
Monte Carlo methods; demand side management; electric heating; load flow control; load forecasting; thermal energy storage; Monte Carlo statistical based method; PSA; PowerShift Atlantic; control water heaters; demand side management program; direct load control program; domestic electric water heater; load forecast; power 32 MW; power consumption forecasting; residential electric hot water; thermal storage unit; Artificial neural networks; Forecasting; Load forecasting; Mathematical model; Resistance heating; Water heating; Aggregation Error; Demand-Side Management; Load Forecast; Smart Grid;
Conference_Titel :
Electrical and Computer Engineering (CCECE), 2015 IEEE 28th Canadian Conference on
Conference_Location :
Halifax, NS
Print_ISBN :
978-1-4799-5827-6
DOI :
10.1109/CCECE.2015.7129511