Abstract
This paper proposes a hybrid artificial neural networks–differential evolution (ANN–DE) and wavelet transforms (WTs)-based approach to forecast the short-term electrical load demand data. The input data ranging from 1 h to several days have a significant effect on the accuracy of short-term load forecasting (STLF). Different forecasting methods with good accuracy are developed for solving the STLF problem based on time series analysis and artificial intelligence system. However, there are several disadvantages of ANNs such as falling in trap of local minima during its parameter optimization process. Therefore, to avoid this problem, in this paper, a hybrid approach is developed by combining the ANNs, WTs and evolutionary-based DE algorithm. Here, the ANNs are used to model the nonlinear and complex behavior of electrical load demand. WTs are used to improve the forecasting ability by decreasing the ill-behaved load demand series into a more stable series. The chance of falling into local optimum can be overcome by using the evolutionary-based DE algorithm. In order to show the effectiveness and suitability of the proposed hybrid approach, the load demand data are taken from California Independent System Operator Web site.
Similar content being viewed by others
References
Kung C, Devaney MJ, Huang C, Kung C (1998) An adaptive power system load forecasting scheme using a genetic algorithm embedded neural network. In: IEEE instrumentation and measurement technology conference, St. Paul, MN, USA, 18–21 May 1998, pp 308–311
Maifeld T, Sheblé G (1994) Short-term load forecasting by a neural network and a refined genetic algorithm. Electr Power Syst Res 31(3):147–152
Mohapatra A, Mallick MK, Panigrahi BK, Cui Z, Hong S (2011) A hybrid approach for short term electricity price and load forecasting. In: International conference on energy, automation and signal, Bhubaneswar, Odisha, India, 28–30 Dec 2011, pp 1–5
Baliyan A, Gaurav K, Mishra SK (2015) A review of short term load forecasting using artificial neural network models. Proc Comput Sci 48:121–125
Schachter J, Mancarella P (2014) A short-term load forecasting model for demand response applications. In: 11th international conference on the European energy market, Krakow, Poland, 28–30 May 2014, pp 1–5
Yang Y, Wu J, Chen Y, Li C (2013) A new strategy for short-term load forecasting. Abstr Appl Anal 2013:1–9
Cheepati KR, Prasad TN (2016) Performance comparison of short term load forecasting techniques. Int J Grid Distrib Comput 9(4):287–302
Taylor JW (2008) An evaluation of methods for very short-term load forecasting using minute-by-minute British data. Int J Forecast 24(4):645–658
Taylor JW, McSharry PE (2007) Short-term load forecasting methods: an evaluation based on European data. IEEE Trans Power Syst 22(4):2213–2219
Annamareddi S, Gopinathan S, Dora B (2013) A simple hybrid model for short-term load forecasting. J Eng 2013:1–7
Sun W, Ye M (2015) Short-term load forecasting based on wavelet transform and least squares support vector machine optimized by fruit fly optimization algorithm. J Electr Comput Eng 2015:1–9
Zhang Z, Gong W (2016) Short-term load forecasting model based on quantum elman neural networks. Math Probl Eng 2016:1–8
Reddy SS, Momoh JA (2014) Short term electrical load forecasting using back propagation neural networks. In: North american power symposium, Pullman, WA, USA, 7–9 Sept 2014, pp 1–6
Reddy SS, Jung CM (2016) Short-term load forecasting using artificial neural networks and wavelet transform. Int J Appl Eng Res 11(19):9831–9836
Dudek G (2015) Pattern similarity-based methods for short-term load forecasting: part 1—principles. Appl Soft Comput 37:277–287
Dudek G (2015) Pattern similarity-based methods for short-term load forecasting: part 2—models. Appl Soft Comput 36:422–441
Panapakidis IP (2016) Application of hybrid computational intelligence models in short-term bus load forecasting. Expert Syst Appl 54:105–120
Ghofrani M, Ghayekhloo M, Arabali A, Ghayekhloo A (2015) A hybrid short-term load forecasting with a new input selection framework. Energy 81:777–789
Chen Y, Xu P, Chu Y, Li W, Wu Y, Ni L, Bao Y, Wang K (2017) Short-term electrical load forecasting using the support vector regression (SVR) model to calculate the demand response baseline for office buildings. Appl Energy 195:659–670
Zeng N, Zhang H, Liu W, Liang J, Alsaadi FE (2017) A switching delayed PSO optimized extreme learning machine for short-term load forecasting. Neurocomputing 240:175–182
Sadaei HJ, Guimarães FG, Silva CJ, Lee MH, Eslami T (2017) Short-term load forecasting method based on fuzzy time series, seasonality and long memory process. Int J Approx Reason 83:196–217
Hu R, Wen S, Zeng Z, Huang T (2017) A short-term power load forecasting model based on the generalized regression neural network with decreasing step fruit fly optimization algorithm. Neurocomputing 221:24–31
He Y, Liu R, Li H, Wang S, Lu X (2017) Short-term power load probability density forecasting method using kernel-based support vector quantile regression and Copula theory. Appl Energy 185(1):254–266
Jiang P, Liu F, Song Y (2017) A hybrid forecasting model based on date-framework strategy and improved feature selection technology for short-term load forecasting. Energy 119:694–709
Li S, Goel L, Wang P (2016) An ensemble approach for short-term load forecasting by extreme learning machine. Appl Energy 170:22–29
Meloa JD, Asanzab SZ, Feltrin AP (2017) A local search algorithm to allocate loads predicted by spatial load forecasting studies. Electr Power Syst Res 146:206–2017
Ren Y, Suganthan PN, Srikanth N, Amaratunga G (2016) Random vector functional link network for short-term electricity load demand forecasting. Inf Sci 367–368:1078–1093
Dudek G (2016) Pattern-based local linear regression models for short-term load forecasting. Electr Power Syst Res 130:139–147
Ray P, Arya SR, Nandkeolyar S (2017) Electric load forecasts by metaheuristic based back propagation approach. J Green Eng 7(1–2):61–82
Ray P, Sen S, Barisal AK (2014) Hybrid methodology for short-term load forecasting. In: IEEE international conference on power electronics, drives and energy systems, Mumbai, India, 16–19 Dec 2014, pp 1–6
Ray P, Panda SK, Mishra DP (2019) Short-term load forecasting using genetic algorithm. Comput Intell Data Min 711:863–872
Ray P, Mishra DP, Lenka RK (2016) Short term load forecasting by artificial neural network. In: IEEE international conference on next generation intelligent systems, Kottayam, Kerala, India, 1–3 Sept 2016, pp 1–6
Reddy SS (2018) Bat algorithm-based back propagation approach for short-term load forecasting considering weather factors. Electr Eng 100(3):1297–1303
Reddy SS (2018) Short-term electrical load forecasting using radial basis function neural networks considering weather factors. Electr Eng 100(3):1985–1995
Islam BU (2011) Comparison of conventional and modern load forecasting techniques based on artificial intelligence and expert systems. Int J Comput Sci Issues 8(5):504–513
El Ela AAA, Abido MA, Spea SR (2011) Differential evolution algorithm for optimal reactive power dispatch. Electr Power Syst Res 81(2):458–464
Storn R, Prince K (1997) Differential evolution: a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359
Liao GC (2014) Hybrid improved differential evolution and wavelet neural network with load forecasting problem of air conditioning. Int J Electr Power Energy Syst 61:673–682
Shrivastava A, Siddiqui HM (2014) A simulation analysis of optimal power flow using differential evolution algorithm for IEEE-30 bus system. Int J Recent Dev Eng Technol 2(3):50–57
Kumari GS, Kumar SK, Anusha J, Rao MP (2015) Electrocardiographic signal analysis using wavelet transforms. In: International conference on electrical, electronics, signals, communication and optimization, Visakhapatnam, India, 24–25 Jan 2015, pp 1–6
Zhai MY (2015) A new method for short-term load forecasting based on fractal interpretation and wavelet analysis. Electr Power Energy Syst 69:241–245
Li S, Wang P, Goel L (2015) Short-term load forecasting by wavelet transform and evolutionary extreme learning machine. Electr Power Syst Res 122:96–103
Haddadi R, Abdelmounim E, Hanine ME, Belaguid A (2014) Discrete wavelet transform based algorithm for recognition of QRS complexes. In: International conference on multimedia computing and systems, Marrakech, Morocco, 14–16 Apr 2014, pp 375–379
California ISO Website (2018). www.caiso.com/Pages/default.aspx. Accessed 3 Sept 2018
Acknowledgement
This research work is based on the support of ‘Woosong University’s Academic Research Funding—2018.’
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Salkuti, S.R. Short-term electrical load forecasting using hybrid ANN–DE and wavelet transforms approach. Electr Eng 100, 2755–2763 (2018). https://doi.org/10.1007/s00202-018-0743-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00202-018-0743-3