Short-term load forecasting using mixed lazy learning method

Seyed-Masoud BARAKATI; Ali Akbar GHARAVEISI; Seyed Mohammad Reza RAFIEI

Makale detayları

Short-term load forecasting using mixed lazy learning method

Yazarlar:

Seyed-Masoud BARAKATI | Ali Akbar GHARAVEISI | Seyed Mohammad Reza RAFIEI

Yayıncı:

TUBITAK

Dergi:

Turkish Journal of Electrical Engineering and Computer Science 2015 Vol.23 Issue 1, pp.201-211

Belge Tipi:

Makale

Yayın Tarihi:

3/2015

Belge Dili:

Türkçe

Document ID (Org No.):

2000742754

Sıklık:

İki Aylık

eISSN:

1303-6203

pISSN:

1300-0632

Özet:

 

A novel short-term load forecasting method based on the lazy learning (LL) algorithm is proposed. The LL algorithm's input data are electrical load information, daily electricity consumption patterns, and temperatures in a specified region. In order to verify the ability of the proposed method, a load forecasting problem, using the Pennsylvania-New Jersey-Maryland Interconnection electrical load data, is carried out. Three LL models are proposed: constant, linear, and mixed models. First, the performances of the 3 developed models are compared using the root mean square error technique. The best technique is then selected to compete with the state-of-the-art neural network (NN) load forecasting models. A comparison is made between the performances of the proposed mixed-model LL as the superior LL model and the radial basis function and multilayer perceptron NN models. The results reveal significant improvements in the precision and efficiency of the proposed forecasting model when compared with the NN techniques.

 

A novel short-term load forecasting method based on the lazy learning (LL) algorithm is proposed. The LL algorithm's input data are electrical load information, daily electricity consumption patterns, and temperatures in a specified region. In order to verify the ability of the proposed method, a load forecasting problem, using the Pennsylvania-New Jersey-Maryland Interconnection electrical load data, is carried out. Three LL models are proposed: constant, linear, and mixed models. First, the performances of the 3 developed models are compared using the root mean square error technique. The best technique is then selected to compete with the state-of-the-art neural network (NN) load forecasting models. A comparison is made between the performances of the proposed mixed-model LL as the superior LL model and the radial basis function and multilayer perceptron NN models. The results reveal significant improvements in the precision and efficiency of the proposed forecasting model when compared with the NN techniques.

Makale detayları

Short-term load forecasting using mixed lazy learning method

Yazarlar: Seyed-Masoud BARAKATI | Ali Akbar GHARAVEISI | Seyed Mohammad Reza RAFIEI

Yayıncı: TUBITAK

Dergi: Turkish Journal of Electrical Engineering and Computer Science 2015 Vol.23 Issue 1, pp.201-211

Belge Tipi: Makale

Yayın Tarihi: 3/2015

Belge Dili: Türkçe

Document ID (Org No.): 2000742754

Sıklık: İki Aylık

eISSN: 1303-6203

pISSN: 1300-0632

Sayı

Yazarlar:

Seyed-Masoud BARAKATI | Ali Akbar GHARAVEISI | Seyed Mohammad Reza RAFIEI

Yayıncı:

TUBITAK

Dergi:

Turkish Journal of Electrical Engineering and Computer Science 2015 Vol.23 Issue 1, pp.201-211

Belge Tipi:

Makale

Yayın Tarihi:

3/2015

Belge Dili:

Türkçe

Document ID (Org No.):

2000742754

Sıklık:

İki Aylık

eISSN:

1303-6203

pISSN:

1300-0632