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2013, vol. 17, iss. 2, pp. 97-99
Comparative analysis of temperature and load time series influence on short-term load forecasting
University of Novi Sad, Faculty of Technical Science
Keywords: short-term load forecasting; load data series; power industry; temperature influence; multiple linear regression
This paper presents several Multiple Linear Regression models for short-term load forecasting, which are intended for use as benchmark tests. A comparative analysis based on models’ performance is presented, which resulted from helping a US power utility to develop a commercial product. The main goal of the analysis carried out is to determine which variables influence the electrical load the most. Models which take into account only weather variables are developed on one side, and the ones containing only load shape information on the other. Both models are of linear regression type, while the input variables are selected based on correlation analysis. The influence of the different variables is than compared, and the model that uses both weather and load shape information is developed. The proposed benchmark models are used as a tool in developing more sophisticated methods for electrical load prediction, such as artificial neural networks, or support vector machines. The analysis of proposed models could provide useful results in dealing with a real-life forecasting algorithm and re-evaluation of methods already presented.
Amjady, N., Keynia, F. (2009) Short-term load forecasting of power systems by combination of wavelet transform and neuro-evolutionary algorithm. Energy, 34(1): 46-57
Amjady, N. (2001) Short-term hourly load forecasting using time-series modeling with peak load estimation capability. IEEE Transactions on Power Systems, 16(4): 798-805
Amjady, N., Keynia, F., Zareipour, H. (2010) Short-Term Load Forecast of Microgrids by a New Bilevel Prediction Strategy. IEEE Transactions on Smart Grid, 1(3): 286-294
Bakirtzis, A.G., Theocharis, J.B., Kiartzis, S.J., Satsios, K.J. (1995) Short term load forecasting using fuzzy neural networks. IEEE Transactions on Power Systems, 10(3): 1518-1524
Christiaanse, W. (1971) Short-Term Load Forecasting Using General Exponential Smoothing. IEEE Transactions on Power Apparatus and Systems, PAS-90(2): 900-911
Fan, S., Chen, L. (2006) Short-Term Load Forecasting Based on an Adaptive Hybrid Method. IEEE Transactions on Power Systems, 21(1): 392-401
Hippert, H.S., Pedreira, C.E., Souza, R.C. (2001) Neural networks for short-term load forecasting: a review and evaluation. IEEE Transactions on Power Systems, 16(1): 44-55
Hong, T., Wang, P., Willis, H.L. (2011) A naïve multiple linear regression benchmark for short term load forecasting. in: Power and Energy Society General Meeting, 2011 IEEE, July 24-29, Raleigh, NC, str. 1-6
Ilic, S., Vukmirovic, S., Erdeljan, A., Kulic, F. (2012) Hybrid artificial neural network system for short-term load forecasting. Thermal Science, 16(suppl. 1): 215-224
Irisarri, G., Widergren, S., Yehsakul, P. (1982) On-Line Load Forecasting for Energy Control Center Application. IEEE Transactions on Power Apparatus and Systems, PAS-101(1): 71-78
Meslier, F. (1978) New advances in short term load forecasting using Box and Jenkins approach. in: IEEE/PES Winter Meeting, New York
Papalexopoulos, A.D., Hesterberg, T.C. (1989) A regression based approach to short term system load forecasting. in: Proceedings of PICA conference, Seattle, Washington
Peng, T.M., Hubele, N.F., Karady, G.G. (1990) Conceptual approach to the application of neural network for short-term load forecasting. in: IEEE International Symposium on Circuits and Systems
Shahidehpour, M., Li, Z., Yamin, H. (2002) Market operations in electric power systems. New York: John Wiley and sons Inc
Song, K.B., Ha, S.K., Park, J.W., Kweon, D.J., Kim, K.H. (2006) Hybrid Load Forecasting Method With Analysis of Temperature Sensitivities. IEEE Transactions on Power Systems, 21(2): 869-876
Srinivasan, D., Chang, C.S., Liew, A.C. (1995) Demand forecasting using fuzzy neural computation, with special emphasis on weekend and public holiday forecasting. IEEE Transactions on Power Systems, 10(4): 1897-1903


article language: English
document type: Original Scientific Paper
published in SCIndeks: 02/09/2013

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