Clustering Based Formulation for Short Term Load Forecasting
A clustering based technique has been developed and implemented for Short Term Load Forecasting, in this article. Formulation has been done using Mean Absolute Percentage Error (MAPE) as an objective function. Data Matrix and cluster size are optimization variables. Model designed, uses two temperature variables. This is compared with six input Radial Basis Function Neural Network (RBFNN) and Fuzzy Inference Neural Network (FINN) for the data of the same system, for same time period. The fuzzy inference system has the network structure and the training procedure of a neural network which initially creates a rule base from existing historical load data. It is observed that the proposed clustering based model is giving better forecasting accuracy as compared to the other two methods. Test results also indicate that the RBFNN can forecast future loads with accuracy comparable to that of proposed method, where as the training time required in the case of FINN is much less.
[1] M.T. Hagen and S.M.Behr, "The time series approach to Short Term
Load Forecasting", IEEE Trans. On Power Systems., PRWS-2(3), pp.
785-791, 1990.
[2] W.R.Christiaanse, "Short Term Load Forecasting using general
exponential smoothing", IEEE Trans. On Power Appar. Syst. PAS-90
pp.900-910, 1971.
[3] A.D.Papalexopoulos, T.Hasterberg, "A Regression based Approach to
Short Term System Load Forecast", IEEE Trans. On Power Systems.
Vol.5, No.4, , pp 1535-1544, Nov. 1990.
[4] I. Mogram and S. Rahman , "Analysis and evaluation of five short term
load forecast techniques", IEEE Trans. On Power Systems. Vol.4, No.4,
pp 1484-1491, 1989.
[5] T. S. Dillon, S. Sestito, and S. Leung, "Short term load forecasting using
an adaptive neural network," Elect. Power Energy Syst., vol. 13, Aug.
1991.
[6] D.C.Park M.A.,El-Sharkawi, R.J.Marks, L.E.Atlas and M.J.Damborg, "
Electric Load Forecasting using an Artificial Neural Networks", IEEE
Trans. on Power Systems, vol.6,No.2, , pp. 442-449, May 1991.
[7] T.M.Peng N.F.Hubele and G.Karady, " Advancement in the application
of Neural Networks for short term load forecasting", IEEE Trans. on
Power Systems, vol.7,No.1, pp. 250-257, Feb.1992.
[8] D.K.Ranaweera,N.F.Hubele and A.D.Papalexopoulos, " Application of
Radial Basis Function Neural Network Model for Short Term Load
Forecasting", IEE Proc. Gener. Trans. Distrib., vol. 142,No.1, Jan.1995.
[9] S.P. Singh and O.P. Malik, "Single ANN architecture for short term load
forecasting for all seasons", Int. Jour of Engineering Intelligent Systems,
vol. 3, no. 4 249-254,Dec.1995.
[10] D. Singh and S.P. Singh, "Self selecting neural network for short-term
load forecasting", Jour. Of Electric Power Component and Systems, vol.
29, pp. 117-130,2001
[11] K.L.Ho, Y.Y.Hsu, C.F.Chen, T.E.Lee, C.C.Liang, T.S.Lai and K.K.Chen
, "Short Term Load Forecasting of Taiwan Power System using a
Knowledge Based Expert System", IEEE Trans.on Power Systems,
vol.5, pp. 1214-1221, 1990.
[12] A. G. Bakirtzis, J. B. Theocharis, S. J. Kiartzis, and K. J. Satsios, "Shortterm
load forecasting using fuzzy neural networks," IEEE Trans. Power
Syst., vol. 10, pp. 1518-1524, Aug. 1995.
[13] A. Khotanzad, E. Zhou and H.Elragal, "A Neuro-Fuzzy approach to
Short-term load forecasting in a price sensitive environment," IEEE
Trans. Power Syst., vol. 17 no. 4, pp. 1273-1282, Nov. 2002.
[14] Hiroyuki Mori and Hidenori Kobayashi," Optimal fuzzy inference for
short term load forecasting ", IEEE Trans. on Power Systems, vol.11,
No.2, pp. 390-396. Feb. 1996.
[15] K.H. Kim, J.K. Park, K.J. Hwang, and S.H. Kim, "Implementation of
Hybrid Short-term Load Forecasting System Using Artificial Neural
Networks and Fuzzy Expert Systems," IEEE Trans. on Power Systems,
vol. 10, no. 3, pp. 1534-1539, Aug. 1995.
[16] Ranaweera D.K., Hubele N.F. and Karady G.G., "Fuzzy logic for shortterm
load forecasting", Electrical Power and Energy Systems," Vol. 18,
No. 4, pp. 215-222, 1996.
[17] Kwang-Ho Kim, Hyoung-Sun Youn, Yong-Cheol Kang, "Short-tem
Load Forecasting for Special Days in anomalous Load Conditions Using
Neural Network and Fuzzy Inference Method", IEEE Trans. On Power
Systems, Vol. 15, pp. 559-569, 2000.
[1] M.T. Hagen and S.M.Behr, "The time series approach to Short Term
Load Forecasting", IEEE Trans. On Power Systems., PRWS-2(3), pp.
785-791, 1990.
[2] W.R.Christiaanse, "Short Term Load Forecasting using general
exponential smoothing", IEEE Trans. On Power Appar. Syst. PAS-90
pp.900-910, 1971.
[3] A.D.Papalexopoulos, T.Hasterberg, "A Regression based Approach to
Short Term System Load Forecast", IEEE Trans. On Power Systems.
Vol.5, No.4, , pp 1535-1544, Nov. 1990.
[4] I. Mogram and S. Rahman , "Analysis and evaluation of five short term
load forecast techniques", IEEE Trans. On Power Systems. Vol.4, No.4,
pp 1484-1491, 1989.
[5] T. S. Dillon, S. Sestito, and S. Leung, "Short term load forecasting using
an adaptive neural network," Elect. Power Energy Syst., vol. 13, Aug.
1991.
[6] D.C.Park M.A.,El-Sharkawi, R.J.Marks, L.E.Atlas and M.J.Damborg, "
Electric Load Forecasting using an Artificial Neural Networks", IEEE
Trans. on Power Systems, vol.6,No.2, , pp. 442-449, May 1991.
[7] T.M.Peng N.F.Hubele and G.Karady, " Advancement in the application
of Neural Networks for short term load forecasting", IEEE Trans. on
Power Systems, vol.7,No.1, pp. 250-257, Feb.1992.
[8] D.K.Ranaweera,N.F.Hubele and A.D.Papalexopoulos, " Application of
Radial Basis Function Neural Network Model for Short Term Load
Forecasting", IEE Proc. Gener. Trans. Distrib., vol. 142,No.1, Jan.1995.
[9] S.P. Singh and O.P. Malik, "Single ANN architecture for short term load
forecasting for all seasons", Int. Jour of Engineering Intelligent Systems,
vol. 3, no. 4 249-254,Dec.1995.
[10] D. Singh and S.P. Singh, "Self selecting neural network for short-term
load forecasting", Jour. Of Electric Power Component and Systems, vol.
29, pp. 117-130,2001
[11] K.L.Ho, Y.Y.Hsu, C.F.Chen, T.E.Lee, C.C.Liang, T.S.Lai and K.K.Chen
, "Short Term Load Forecasting of Taiwan Power System using a
Knowledge Based Expert System", IEEE Trans.on Power Systems,
vol.5, pp. 1214-1221, 1990.
[12] A. G. Bakirtzis, J. B. Theocharis, S. J. Kiartzis, and K. J. Satsios, "Shortterm
load forecasting using fuzzy neural networks," IEEE Trans. Power
Syst., vol. 10, pp. 1518-1524, Aug. 1995.
[13] A. Khotanzad, E. Zhou and H.Elragal, "A Neuro-Fuzzy approach to
Short-term load forecasting in a price sensitive environment," IEEE
Trans. Power Syst., vol. 17 no. 4, pp. 1273-1282, Nov. 2002.
[14] Hiroyuki Mori and Hidenori Kobayashi," Optimal fuzzy inference for
short term load forecasting ", IEEE Trans. on Power Systems, vol.11,
No.2, pp. 390-396. Feb. 1996.
[15] K.H. Kim, J.K. Park, K.J. Hwang, and S.H. Kim, "Implementation of
Hybrid Short-term Load Forecasting System Using Artificial Neural
Networks and Fuzzy Expert Systems," IEEE Trans. on Power Systems,
vol. 10, no. 3, pp. 1534-1539, Aug. 1995.
[16] Ranaweera D.K., Hubele N.F. and Karady G.G., "Fuzzy logic for shortterm
load forecasting", Electrical Power and Energy Systems," Vol. 18,
No. 4, pp. 215-222, 1996.
[17] Kwang-Ho Kim, Hyoung-Sun Youn, Yong-Cheol Kang, "Short-tem
Load Forecasting for Special Days in anomalous Load Conditions Using
Neural Network and Fuzzy Inference Method", IEEE Trans. On Power
Systems, Vol. 15, pp. 559-569, 2000.
@article{"International Journal of Electrical, Electronic and Communication Sciences:51960", author = "Ajay Shekhar Pandey and D. Singh and S. K. Sinha", title = "Clustering Based Formulation for Short Term Load Forecasting", abstract = "A clustering based technique has been developed and implemented for Short Term Load Forecasting, in this article. Formulation has been done using Mean Absolute Percentage Error (MAPE) as an objective function. Data Matrix and cluster size are optimization variables. Model designed, uses two temperature variables. This is compared with six input Radial Basis Function Neural Network (RBFNN) and Fuzzy Inference Neural Network (FINN) for the data of the same system, for same time period. The fuzzy inference system has the network structure and the training procedure of a neural network which initially creates a rule base from existing historical load data. It is observed that the proposed clustering based model is giving better forecasting accuracy as compared to the other two methods. Test results also indicate that the RBFNN can forecast future loads with accuracy comparable to that of proposed method, where as the training time required in the case of FINN is much less.
", keywords = "Load forecasting, clustering, fuzzy inference.", volume = "2", number = "10", pages = "2182-5", }
