Title :
A hybrid approach to very small scale electrical demand forecasting
Author :
Marinescu, Andrei ; Harris, Colin ; Dusparic, Ivana ; Cahill, Vinny ; Clarke, Steven
Author_Institution :
Distrib. Syst. Group, Trinity Coll. Dublin, Dublin, Ireland
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.
Keywords :
distributed power generation; load forecasting; NRMSE; artificial neural networks; autoregressive moving average; day-ahead demand forecasting; fuzzy logic; hybrid method; large-scale systems; microgrid management; microgrid scheduling; municipal level grids; national level grids; small virtual power plant; smart-meter field trial; very small scale electrical demand forecasting; wavelet smoothing; Artificial neural networks; Demand forecasting; Electricity; Load forecasting; Microgrids; Training; VPP; demand forecasting; hybrid; microgrid;
Conference_Titel :
Innovative Smart Grid Technologies Conference (ISGT), 2014 IEEE PES
Conference_Location :
Washington, DC
DOI :
10.1109/ISGT.2014.6816426