A load forecasting method for HEMS applications

Author

Hong-Tzer Yang ; Jian-Tang Liao ; Che-I Lin

Author_Institution

Dept. of Electr. Eng., Nat. Cheng Kung Univ., Tainan, Taiwan

fYear

2013

fDate

16-20 June 2013

Firstpage

1

Lastpage

6

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.

Keywords

demand side management; load forecasting; neural nets; power system economics; wavelet transforms; ANN-based forecasting; HEMS applications; data selection; day-ahead short-term artificial neural network; error-correcting functions; home energy management system; load demands; load forecasting method; power expenditure minimization; wavelet transform; Artificial neural networks; Forecasting; Home appliances; Load forecasting; Load modeling; Predictive models; Wavelet transforms; HEMS; error-correcting; household load forecasting; neural network; wavelet transform;

fLanguage

English

Publisher

ieee

Conference_Titel

PowerTech (POWERTECH), 2013 IEEE Grenoble

Conference_Location

Grenoble

Type

conf

DOI

10.1109/PTC.2013.6652195

Filename

6652195