Short-Term Load Forecasting Model Using Flower Pollination Algorithm

Abstract

Electricity is natural but not a storable resource and has a vital role in modern life. Balancing between consumption and production of the electricity is highly important for power plants and production facilities. Researches show that electricity load consumption characteristic is highly related to exogenous factors such as weather condition, day type (weekdays, weekends and holidays etc.), seasonal effects, economic and politic changes (crisis, elections etc.).  In this study, we propose a short-term load forecasting models using artificial intelligence based optimization technique. Proposed 5 different empirical models were optimized using flower pollination algorithm (FPA). Training and testing phase of the proposed models held with historical load and weather temperature dataset for the years between 2011-2014. Forecasting accuracy of the models was measured with Mean Absolute Percentage Error (MAPE) and monthly minimum approximately %1,79 for February 2013. Results showed that proposed load forecasting model is very competent for short-term load forecasting.

Keywords

• Artificial Intelligence,Flower Pollination Algorithm,Nature-Inspired Optimization,Short-Term Load Forecasting

References

1. Feinberg E.A. and Genethliou D., “Chapter 12 Load forecasting”, Applied Mathematics for Power Systems, pp.269-282. http://www.ams.sunysb.edu/~feinberg/public/lf.pdf
2. Fan S. and Hyndman R.J., “Short-Term Load Forecasting Based on a Semi-Parametric Additive Model,” IEEE Trans. Power Systems, vol.27, no.1, pp.134-141, 2012
3. Hippert H.S., Pedreira C.E. and Souza R.C. “Neural Networks for Short-Term Load Forecasting: A Review and Evaluation”, IEEE Trans. Power Systems, vol.16, no.1 pp. 44-55, 2001
4. Mbamalu G.A.N. and El-Hawary M.E., “Load forecasting via suboptimal seasonal autoregressive models and iteratively reweighted least squares estimation,” IEEE Trans. Power Systems, vol.8, no.1, pp. 343–348, 1993.
5. Yang H.T. and Huang C.M., “A new short-term load forecasting approach using self-organizing fuzzy ARMAX models,” IEEE Trans. Power Systems, vol.13, no.1, pp. 217–225, 1998.
6. Douglas A.P., Breipohl A.M., Lee F.N. and Adapa R.,“The impact of temperature forecast uncertainty on bayesian load forecasting,” IEEE Trans. Power Systems, vol.13, no.4, pp. 1507–1513, 1998.
7. Sadownik R. and E.P. Barbosa, “Short-term forecasting of industrial electricity consumption in Brazil,” J. Forecast., vol.18, pp. 215–224, 1999.
8. Charytoniuk W., Chen M.S. and Van Olinda P., “Nonparametric regression based short-term load forecasting,” IEEE Trans. Power Systems, vol.13, no.3, pp. 725–730, 1998.

9. Harvey A. and Koopman S.J., “Forecasting hourly electricity demand using time-varying splines,” J. American Stat. Assoc., vol.88, no.424, pp. 1228–1236, 1993.
10. Taylor J.W. and Majithia S., “Using combined forecasts with changing weights for electricity demand profiling”, J. Oper. Res. Soc., vol.51, no.1, pp. 72–82, 2000.
11. Mamlook R., Badran O. and Abdulhadi E., “A fuzzy inference model for short-term load forecasting”, Energy Policy vol. 37, no.4, pp.1239–1248, 2009
12. Pandian S.C., Duraiswamy K, Rajan C.C.R and Kanagaraj N, “Fuzzy approach for short term load forecasting”, Electr Power Syst Res vol.76 no.(6–7), pp.541–548, 2006
13. Aggarwal S, Kumar M, Saini LM and Kumar A, “Short-term load forecasting in deregulated electricity markets using fuzzy approach”, J Eng. Technol, vol.1, no.1, pp.24–31, 2011
14. Ying L.C., Pan M.C., “Using adaptive network based fuzzy inference system to forecast regional electricity loads”, Energy Convers Manag., vol.49, no.2, pp.205–211, 2008
15. Hassan S., Khosravi A., Jaafar J. and Khanesar M. A., “A systematic design of interval type-2 fuzzy logic system using extreme learning machine for electricity load demand forecasting”, Electrical Power and Energy Systems, vol.82, pp.1–10, 2016
16. Chaturvedi D.K., S.O. M. “Short-Term Load Forecasting Using Fuzzy Logic and Wavelet Transform Integrated Generalized Neural Network”, Electrical Power and Energy Systems, vol.67, pp.230-237, 2015
17. S. Li, P. L. G. “Short-term load forecasting by wavelet transform and evolutionary extreme learning machine”, Electric Power System Research, vol.122, pp. 96-103, 2015
18. Yukseltan E., Yucekaya A. and Bilge A.H., “Forecasting electricity demand for Turkey: Modeling periodic variations and demand segregation”, Applied Energy, vol.193, pp. 287–296, 2017
19. Çevik H.H. and Çunkaş M., “Short-term load forecasting using fuzzy logic and ANFIS”, Neural Computing and Applications, vol. 26, no.6, pp.1355–1367, 2015
20. Esener I., Yuksel T. and Kurban M., “Short-term load forecasting without meteorological data using AI-based structures”, Turkish Journal of Electrical Engineering & Computer Sciences, vol.23, pp.370-380, 2015
21. Yang X.-S., Nature-Inspired Optimization Algorithms, Londra: Elsevier, 2014.