ANN based Short-Term Load Curve Forecasting

  • Violeta Eugenia Chis Aurel Vlaicu University of Arad
  • Constantin Barbulescu Power Systems Department Politehnica University Timisoara Romania Timisoara, Romania
  • Stefan Kilyeni Power Systems Department Politehnica University Timisoara Romania Timisoara, Romania
  • Simona Dzitac Power Engineering Department University of Oradea Oradea, Romania

Abstract

A software tool developed in Matlab for short-term load forecasting (STLF) is presented. Different forecasting methods such as artificial neural networks, multiple linear regression, curve fitting have been integrated into a stand-alone application with a graphical user interface. Real power consumption data have been used. They have been provided by the branches of the distribution system operator from the Southern-Western part of the Romanian Power System. This paper is an extended variant of [4].

References

[1] Charytoniuk, W.; Chen, M.S.; Van Olinda, P. (1998). Nonparametric Regression Based Short-Team Load Forecasting, IEEE Transaction on Power Systems, 13(3), 735-730, 1998.
https://doi.org/10.1109/59.708572

[2] Chen, H.; Canizares, A.C.; Ajit, S. (2011). ANN based Short-Term Load Forecasting in Electricity markets, Proceedings of the IEEE Power Engineering Society Winter Meeting, 2, 411-415, 2011.

[3] Chen, J.F.; Wang, W.M.; Huang, C.M.(2005). Analysis of an adaptive time-series autoregressive moving-average (ARMA) model for short-term load forecasting, Electric Power Systems Research, 34, 187-196, 2005.
https://doi.org/10.1016/0378-7796(95)00977-1

[4] Chis, V.; Barbulescu, C., Kilyeni, S.; Dzitac S. (2018). Short-Term Load Forecasting Software Tool, Proceedings of the 7th International Conference on Computers Communications and Control (ICCCC), 111–118, 2018.
https://doi.org/10.1109/ICCCC.2018.8390446

[5] Cho, M.Y.; Hwang, J.C.; Chen, C.S. (1995). Customer short-term load forecasting by using ARIMA transfer function model, Proceedings of the International Conference on Energy Management and Power Delivery, 317-322, 1995.
https://doi.org/10.1109/EMPD.1995.500746

[6] Danladi, A.; Yohanna, M.; Puwu, M.I.; Garkida, B.M. (2016). Long-term load forecast modelling using a fuzzy logic approach, Pacific Science Review A: Natural Science and Engineering, 18(2), 123-127, 2016.

[7] Ho, K.l.; Hsu, Y.I.; Chen, C.F.; Lee, T.E.; Liang, C.C.; Lai, T.S.; Chen, K.K. (1990). Short Term Load Forecasting of Taiwan Power System Using a Knowledge Based Expert System, IEEE Transactions on Power Systems, 5(4), 1214-1221, 1990.
https://doi.org/10.1109/59.99372

[8] Hong, W.C.; Dong, Y.; Chen, L.Y.; Wei, S.Y. (2012). Seasonal Support vector Regression with Chaotic Genetic Algorithm in Electric Load, ICGEC 6th International Conference on Genetic and Evolutionary Computing, 124-127, 2012.

[9] Hyndman, R.J.; Koehler, A.B. (2016). Another look at measuring forecast accuracy, International Journal of Forecasting, 22(2), 679-688, 2016.

[10] Ismail, Z.; Efendy, R. (2011). Enrollment forecasting based on modified weight fuzzy time series, Journal of Artificial Intelligence, 4(1), 110-118, 2011.
https://doi.org/10.3923/jai.2011.110.118

[11] Jin, X.; Dong, Y.; Wu, J.; Wang, J. (2010). An Improved Combined Forecasting Method for Electric Power Load Based on Autoregressive Integrated Moving Average Model, International Conference of Information Science and Management Engineering (ISME), 2, 476-480, 2010.
https://doi.org/10.1109/ISME.2010.124

[12] Karapidakis, S. (2007). Machine learning for frequency estimation of power systems, Applied Soft Computing, 7(1), 105-114, 2007.
https://doi.org/10.1016/j.asoc.2005.04.002

[13] Mordjaoui, M.; Haddad, S.; Medoued, A.; Laouafi, A. (2017). Electric load forecasting by using dynamic neural network, Journal hydrogen Energy, 42, 17655-17663, 2017.

[14] Pandian, S.C.; Duraiswamy, K.; Rajan, C.C.A. (2006). Fuzzy approach for short term load forcasting, Electric Power Systems Research, 76, 541-548, 2006.
https://doi.org/10.1016/j.epsr.2005.09.018

[15] Park, D.C.; El-Sharkawi, M.A.; Marks, R.J.; Atlas, L.E.; Damborg, M.J. (1991). Electric load forecasting using an artificial neural network, IEEE Transactions on Power Systems, 6(2), 442-449, 1991.
https://doi.org/10.1109/59.76685

[16] Schellong, W. (2011). Energy Demand Analysis and Forecast, Energy Management Systems P. Giridhar Kini, IntechOpen, DOI: 10.5772/21022, 2011.
https://doi.org/10.5772/21022

[17] Sheikh, S.K.; Unde, M.G. (2012). Short-Term Load Forecasting Using ANN Technique, International Journal of Engineering Sciences & Emerging Technologies, 1(2), 97-107, 2012.
https://doi.org/10.7323/ijeset/v1_i2_12

[18] Shelke, M.; Thakare, P.D. (2014). Short Term Load Forecasting by Using Data Mining Techniques, International Journal of Science and Research (IJSR), 3(9), 1363-1367, 2014.

[19] Singh, P.; Dwivedi, P. (2018). Integration of new evolutionary approach with artificial neural network for solving short term load forecast problem, Journal of Applied Energy, 217, 537- 549, 2018.
https://doi.org/10.1016/j.apenergy.2018.02.131

[20] Srinivasan, D.S.; Tan, S.S.; Cheng, C.S.; Chan, E.K. (1999). Parallel neural network-fuzzy expert system strategy for short-term load forecasting: system implementation and performance evaluation, IEEE Transactions on Power Systems, 14(3), 1100-1106, 1999.
https://doi.org/10.1109/59.780934

[21] Yang, H.P.; Yan, F.F.; Wang, H.; Zhang, L.(2016). Short-term load forecasting based on data mining, IEEE 20th International Conference on Computer Supported Cooperative Work in Design, 170-173, 2016.

[22] Zhang, J.,; Yi-Ming, W.; Dezhi, L.; Zhongfu, T.; Jianhua, Z. (2018). Short term electricity load forecasting using a hybrid model, Journal Energy, 158(C), 774-781, 2018.
https://doi.org/10.1016/j.energy.2018.06.012

[23] [Online]. Available: www.mathworks.com Matlab Users guide, Accesed on 12 February 2018.
Published
2018-11-29
How to Cite
CHIS, Violeta Eugenia et al. ANN based Short-Term Load Curve Forecasting. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, [S.l.], v. 13, n. 6, p. 938-955, nov. 2018. ISSN 1841-9844. Available at: <http://univagora.ro/jour/index.php/ijccc/article/view/3385>. Date accessed: 10 aug. 2020. doi: https://doi.org/10.15837/ijccc.2018.6.3385.

Keywords

artificial neural networks; short-term load forecasting; articial intelligence; load curve