Adaptive sliding window load forecasting

Judith Foster, Xueqin Liu, Sean McLoone

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)
189 Downloads (Pure)

Abstract

Small-scale, renewable generation which is embedded in the distribution network is causing previously unseen fluctuations in demand. In Northern Ireland this new generation, which is not visible to, or controllable by, the system operator, is presenting major challenges for accurate load forecasting. Currently deployed load forecasting methods are struggling to cope due to the rapid growth in this new generation, and its weather dependent nature. In this paper linear load forecasting methods are investigated within a sliding window parameter updating framework, which is adopted to address the non-stationarity of the problem. Initially, models are built using historical load terms selected based on correlation analysis of recorded load data. Then, Forward Selection Regression is used to choose the most important variables from a candidate set, consisting of historical load variables and a range of weather related parameters. Model performance is evaluated on load data for the period 2015-2016. A 7-input model, with parameters updated on the basis of a 5-day sliding window of historical data, is shown to yield optimal results, with a mean absolution percentage error of 2.4%.

Original languageEnglish
Title of host publication2017 28th Irish Signals and Systems Conference, ISSC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781538610466
DOIs
Publication statusPublished - 18 Jul 2017
Event28th Irish Signals and Systems Conference, ISSC 2017 - Killarney, Ireland
Duration: 20 Jun 201721 Jun 2017

Conference

Conference28th Irish Signals and Systems Conference, ISSC 2017
CountryIreland
CityKillarney
Period20/06/201721/06/2017

Keywords

  • electric load forecasting
  • forward selection regression
  • linear methods
  • sliding window

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Signal Processing

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