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A hybrid approach to very small scale electrical demand forecasting
Marinescu, Andrei; Harris, Colin; Dusparic, Ivana; Cahill, Vinny; Clarke, Siobhán
Microgrid management and scheduling can considerably benefit from day-ahead demand forecasting. Until now, most of the research in the field of electrical demand forecasting has been done on large-scale systems, such as national or municipal level grids. This paper examines a hybrid method that attempts to accurately estimate day-ahead electrical demand of a small community of houses resembling the load of a single transformer, the equivalent sizing of a small virtual power plant or microgrid. We have combined the advantages of several forecasting methods into a novel hybrid approach: artificial neural networks, fuzzy logic, auto-regressive moving average and wavelet smoothing. The combined system has been tested over two different scenarios, comprising communities of 90 houses and 230 houses, sampled from a smart-meter field trial in Ireland. Our hybrid approach achieves results of 3.22% NRMSE and 2.39% NRMSE respectively, leading to general improvements of 11%- 28% when compared to the individual methods.
Keyword(s): demand forecasting; hybrid; microgrid; VPP
Publication Date:
2014
Type: Journal article
Peer-Reviewed: Yes
Language(s): English
Institution: University of Limerick
Funder(s): Science Foundation Ireland
Citation(s): IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT);pp. 1-5
http://dx.doi.org/10.1109/ISGT.2014.6816426
10/CE/I1855
Publisher(s): IEEE Computer Society
First Indexed: 2014-09-25 05:37:49 Last Updated: 2019-09-19 06:26:26