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A hybrid approach to very small scale electrical demand forecasting
Date
2014
Abstract
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.
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Description
peer-reviewed
Publisher
IEEE Computer Society
Citation
IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT);pp. 1-5
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Funding Information
Science Foundation Ireland (SFI)