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Abstract

Penetration of distributed energy resources in distribution networks is predicted to increase dramatically in thenext seven years, bringing with it the opportunity for utilitiesto have a greater presence at low levels of the network. Toachieve this effectively, utilities will require accurate short termload forecasts. This paper presents a novel neural network-basedload forecasting system that applies recent advances in neuralattention mechanisms. The forecasting system is trained andassessed on ten years of historical half-hourly load, weather,and calendar data to produce a 24-hour horizon half-hourlyonline forecast. When forecasting during anomalous peak holidayperiods on a feeder that has a typical load of less than 1000kVAthe forecasting system achieves a MAPE of 7.4% and a meanerror of -15kVA. The forecasting system is implemented in aresidential battery trial and is able to successfully forecast majorpeaks with sufficient lead time and accuracy to enable the fleet ofbatteries to charge ahead of time and provide network support

Item Type:	Conference or Workshop Item (Paper)
Authors/Creators:	Jurasovic, M and Franklin, E and Negnevitsky, M and Scott, P
Keywords:	load forecasting, machine learning, DER
Journal or Publication Title:	Proceedings of The Australasian Universities Power Engineering Conference (AUPEC 2018)
Publisher:	IEEE
Copyright Information:	Copyright 2018 IEEE
Related URLs:	Google Scholar: Franklin, E Google Scholar: Negnevitsky, M UTAS Profile: Negnevitsky, M
Item Statistics:	View statistics for this item

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