A load forecasting method for HEMS applications

Hong-Tzer Yang, Jian Tang Liao, Che I. Lin

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

23 Citations (Scopus)

Abstract

In a home energy management system (HEMS), household load forecasting is difficult, due to its small number of loads and random nature of turning on/off. However, it is important to pre-schedule the load demands of home appliances in the HEMS for power expenditure minimization. This paper proposes a new day-ahead short-term artificial neural network (ANN) based forecasting method, which consists of the techniques of data selection, wavelet transform (WT), ANN-based forecasting, and error-correcting (EC) functions. To verify the effectiveness of the proposed forecasting method, the approach has been verified by using practical data for household load demands. Numerical forecasting results are presented and discussed in this paper.

Original languageEnglish
Title of host publication2013 IEEE Grenoble Conference PowerTech, POWERTECH 2013
DOIs
Publication statusPublished - 2013 Dec 27
Event2013 IEEE Grenoble Conference PowerTech, POWERTECH 2013 - Grenoble, France
Duration: 2013 Jun 162013 Jun 20

Publication series

Name2013 IEEE Grenoble Conference PowerTech, POWERTECH 2013

Other

Other2013 IEEE Grenoble Conference PowerTech, POWERTECH 2013
CountryFrance
CityGrenoble
Period13-06-1613-06-20

All Science Journal Classification (ASJC) codes

  • Energy Engineering and Power Technology
  • Fuel Technology

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