Short, Medium and Long Term Load Forecasting Model and Virtual Load

Topics: Electric power transmission, Forecasting, Electricity generation Pages: 21 (6764 words) Published: October 15, 2012
Electrical Power and Energy Systems 32 (2010) 743–750

Contents lists available at ScienceDirect

Electrical Power and Energy Systems
journal homepage: www.elsevier.com/locate/ijepes

Short, medium and long term load forecasting model and virtual load forecaster based on radial basis function neural networks Changhao Xia a,b,*, Jian Wang b,*, Karen McMenemy c
a

College of Electrical Engineering and Information Technology, China Three Gorges University, Yichang Hubei 443002, China School of Mechanical and Aerospace Engineering, Queen’s University, Belfast, Northern Ireland BT9 5AH, UK c School of Electronics, Electrical Engineering and Computer Science, Queen’s University, Belfast, Northern Ireland BT9 5AH, UK b

a r t i c l e

i n f o

a b s t r a c t
Artificial neural networks (ANNs) can be easily applied to short-term load forecasting (STLF) models for electric power distribution applications. However, they are not typically used in medium and long term load forecasting (MLTLF) electric power models because of the difficulties associated with collecting and processing the necessary data. Virtual instrument (VI) techniques can be applied to electric power load forecasting but this is rarely reported in the literature. In this paper, we investigate the modelling and design of a VI for short, medium and long term load forecasting using ANNs. Three ANN models were built for STLF of electric power. These networks were trained using historical load data and also considering weather data which is known to have a significant affect of the use of electric power (such as wind speed, precipitation, atmospheric pressure, temperature and humidity). In order to do this a V-shape temperature processing model is proposed. With regards MLTLF, a model was developed using radial basis function neural networks (RBFNN). Results indicate that the forecasting model based on the RBFNN has a high accuracy and stability. Finally, a virtual load forecaster which integrates the VI and the RBFNN is presented. Ó 2010 Elsevier Ltd. All rights reserved.

Article history: Received 26 March 2008 Received in revised form 30 September 2009 Accepted 28 January 2010

Keywords: Electric load forecasting Radial basis function Neural network Virtual instrument

1. Introduction Short-term, medium and long-term forecasting of load demand is necessary for the correct operation of electric utilities. Forecasts are required for proper scheduling activities, such as generation scheduling, fuel purchasing scheduling, maintenance scheduling, investment scheduling, and for security analysis [1]. Generally the forecasting methods found in the literature are typically based on the following forms of mathematical analysis: regressive analysis, exponential smoothing, time series, grey box systems, Kalman filtering, expert systems, wavelet analysis, fuzzy system modelling, neural network modelling, etc. Many models for STLF [2–16,21–24] and MLTLF [1,17–20] have been proposed in the literature. In some cases researchers have combined several methods to develop their own hybrid method. For example in [2] a fuzzy linear regression method is used for load forecasting weekend power usage whereas weekday loads are forecast using a general exponential smoothing method. The latest developments in general load forecasting cited in the literature use artificial intelli-

* Corresponding authors. Address: School of Mechanical and Aerospace Engineering, Queen’s University, Belfast, Northern Ireland BT9 5AH, UK. Tel.: +44 0 28 90974181; fax: +44 0 28 90975576. E-mail addresses: c.xia@qub.ac.uk (C. Xia), j.wang@qub.ac.uk (J. Wang), k.mcmenemy@ee.qub.ac.uk (K. McMenemy). 0142-0615/$ - see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.ijepes.2010.01.009

gent-based forecasting (AIBF) techniques, such as ANN [1– 4,6,8,13,14,16–19], fuzzy logic [2,12,14,15], and genetic algorithms [9], all of which show promising results for STLF. Less literature...

References: [1] Ranaweera D, Karady G, Farmer R. Economic impact analysis of load forecasting. IEEE Trans Power Syst 1997;12(3):1388–92. [2] Song KB, Ha SK, Park JW, Kweon DJ, Kim KH. Hybrid load forecasting method with analysis of temperature sensitivities. IEEE Trans Power Syst 2006;21(2):869–76. [3] Aguirre LA, Rodrigues DD, Lima ST, Martinez CB. Dynamical prediction and pattern mapping in short-term load forecasting. Int J Electr Power Energy Syst 2008;30:73–82. [4] Truong NV, Wang L, Wong PKC. Modelling and short-term forecasting of daily peak power demand in Victoria using two-dimensional wavelet based SDP models. Int J Electr Power Energy Syst 2008;30:511–8. [5] Bhattacharyya SC. Short-term electric load forecasting using an artificial neural network: case of northern Vietnam. Int J Energy Res 2004;28:463–72. [6] Taylor JW, Buizza R. Neural network load forecasting with weather ensemble predictions. IEEE Trans Power Syst 2002;17(3):626–32. [7] Nengling T, Stenzel J, Wu HX. Techniques of applying wavelet transform into combined model for short-term load forecasting. Electr Power Syst Res 2006;76:525–33. [8] Senjyu T, Takara H, Uezato K, Funabashi T. One-hour-ahead load forecasting using neural network. IEEE Trans Power Syst 2002;17(1):113–8. [9] Ling SH, Leung FHF, Lam HK, Tam PKS. Short-term electric load forecasting based on a neural fuzzy network. IEEE Trans Ind Electron 2003;50(6):1305–16. [10] Papalexopoulos AD, Hesterberg TC. A regression-based approach to short-term system load forecasting. IEEE Trans Power Syst 1990;5(4):1535–50.
Train
Specimen Set
ANN
Fig. 4. Flowchart for a multi-purpose virtual load forecaster.
be possible to acquire a model with higher accuracy. Of course, this should be a very fruitful area for future research. 7. Conclusion This paper introduces a method that combines neural network models of load forecasting with virtual instrument technology in order to build a virtual forecaster. It also presents the scheme of a multi-purpose virtual load forecaster, to establish a united database for load forecasting and to combine short-term load forecasting with medium and long term load forecasting. When establishing a short-term load forecasting model, we should take
750
C. Xia et al. / Electrical Power and Energy Systems 32 (2010) 743–750 [19] Carpinteiro OAS, Lemeb RC, Souza ACZ, Pinheiro CAM, Moreira EM. Long-term load forecasting via a hierarchical neural model with time integrators. Electr Power Syst Res 2007;77:371–8. [20] Al-Hamadi HM, Soliman SA. Long-term/mid-term electric load forecasting based on short-term correlation and annual growth. Electr Power Syst Res 2005;74:353–61. [21] Dillon TS, Sestito S. Neural networks applications in power systems. Leicester (UK): CRL Publishing Ltd.; 1996. p. 1–105. [22] Dillon TS, Sestito S, Leung S. Short term load forecasting using an adaptive neural network. J Electr Power Energy Syst 1991;13(4):186–92. [23] Dillon TS, Phua KS. Power system load models suitable for use in generation planning, maintenance scheduling and stochastic control. In: Proceedings IEEE control power systems conference 1977, Texas A&M University; 1977. [24] Dillon TS, Morsztyn K, Phua K. Short term load forecasting using adaptive pattern recognition and self-organizing techniques. In: Proceedings of the fifth world power system computation conference (PSCC-5), September 1975, Cambridge, paper 2.4/3, p. 1–15.
[11] Yang JF, Stenzel J. Short-term load forecasting with increment regression tree. Electr Power Syst Res 2006;76:880–8. [12] Pandian SC, Duraiswamy K, Rajan CCA, Kanagaraj N. Fuzzy approach for short term load forecasting. Electr Power Syst Res 2006;76:541–8. [13] Zhang T, Zhao DF, Zhou L, Wang XF, Xia DZ. Short-term load forecasting using redial basis function networks and expert system. J Xi’an Jiaotong Univ 2001;35(4):331–4. [14] Tao WQ, Shi WQ. Short-term electric load forecasting based on radial basis function networks and fuzzy logic. J Hefei Univ Technol 2005;28(6):631–4. [15] Al-Kandari AM, Soliman SA, El-Hawary ME. Fuzzy short-term electric load forecasting. Int J Electr Power Energy Syst 2004;26:111–22. [16] Kandil N, Wamkeue R, Saad M, Georges S. An efficient approach for short term load forecasting using artificial neural networks. Int J Electr Power Energy Syst 2006;28:525–30. [17] Kermanshahi B, Iwamiya H. Up to year 2020 load forecasting using neural nets. Int J Electr Power Energy Syst 2002;24:789–97. [18] Ghiassi M, Zimbra DK, Saidane H. Medium term system load forecasting with a dynamic artificial neural network model. Electr Power Syst Res 2006;76:302–16.
Continue Reading

Please join StudyMode to read the full document

You May Also Find These Documents Helpful

  • load Essay
  • Long Term and Short Term Budget Essay
  • Short Term and Long Term Financing Essay
  • Short Term and Long Term Memory Essay
  • Long-Term & Short-Term Budgetting Essay
  • medium term forecasting of inflation rate in Bangladesh Essay
  • Balancing the Load Essay
  • Load Shedding Essay

Become a StudyMode Member

Sign Up - It's Free