Abstract
Electric energy plays an irreplaceable role in nearly every person’s life on earth; it has become an important subject in operational research. Day by day, electrical load demand grows rapidly with increasing population and developing technology such as smart grids, electric cars, and renewable energy production. Governments in deregulated economies make investments and operating plans of electric utilities according to mid- and long-term load forecasting results. For governments, load forecasting is a vitally important process including sales, marketing, planning, and manufacturing divisions of every industry. In this paper, we suggest three models based on multivariate adaptive regression splines (MARS), artificial neural network (ANN) and linear regression (LR) methods to model electrical load overall in the Turkish electricity distribution network, and this not only by long-term but also mid- and short-term load forecasting. These models predict the relationship between load demand and several environmental variables: wind, humidity, load-of-day type of the year (holiday, summer, week day, etc.) and temperature data. By comparison of these models, we show that MARS model gives more accurate and stable results than ANN and LR models.
Similar content being viewed by others
References
Azad HB, Mekhilef S, Ganapathy VG (2014) Long-term wind speed forecasting and general pattern recognition using neural networks. IEEE Trans Sustain Energy 5(2):546–553
Barrow DK, Crone SF (2016a) A comparison of AdaBoost algorithms for time series forecast combination. Int J Forecast 32(4):1103–1119
Barrow DK, Crone SF (2016b) Cross-validation aggregation for combining autoregressive neural network forecast Devon. Int J Forecast 32(4):1120–1137
Bezerra B, Veiga Á, Barroso LA, Pereira M (2017) Stochastic long-term hydrothermal scheduling with parameter uncertainty in autoregressive streamflow models. IEEE Trans Power Syst 32(2):999–1006
Black JD, Henson WLW (2014) Hierarchical load hindcasting using reanalysis weather. IEEE Trans Smart Grid 5(1):447–455
Cevik A, Weber GW, Eyüboglu BM, Karli-Oguz K, The Alzheimer’s Disease Neuroimaging Initiative (2017) Voxel-MARS: a method for early detection of Alzheimer’s disease by classification of structural brain MRI. Ann Oper Res (ANOR) Spec Issue Oper Res Neurosci 258(1):31–57
Chow JH, Wu FI, Momoh JA (2005) Applied mathematics for restructured electric power systems. Appl Math Restruct Electric Power Syst 1(1):269–317
De Giorgi MG, Congedo PM, Malvoni M (2014) Photovoltaic power forecasting using statistical methods: impact of weather data. IET Sci Meas Technol 8(3):90–97
Friedman JH (1991) Multivariate adaptive regression splines. Ann Stat 19(1):1–67
GENI, Global energy network institute. http://www.globalenergy/national_energy_grid/turkey/turkishnationalelectricitygrid.html
Goude Y, Nedellec R, Kong N (2014) Local short and middle term electricity load forecasting with semi-parametric additive models. IEEE Trans Smart Grid 5(1):440–446
Hastie T, Tibshirani R, Friedman J (2008) The elements of statistical learning data mining, inference, and prediction. Math Intell 2(1):251–264
Hippert HS, Pedreira CE, Souza RC (2001) Neural networks for short-term load forecasting: a review and evaluation. IEEE Trans Power Syst 16(1):44–55
Hong T, Fan S (2016) Probabilistic electric load forecasting: a tutorial review. Int J Forecast 32(3):914–935
Hong T, Wilson J, Xie J (2014) Long term probabilistic load forecasting and normalization with hourly information. IEEE Trans Smart Grid 5(1):456–462
Kandil MS, El-Debeiky SM, Hasanien NE (2002) Long-term load forecasting for fast developing utility using a knowledge-based expert system. IEEE Trans Power Syst 17(2):491–496
Khuntia SR, Rueda JL, van der Meijden MAMM (2016) Forecasting the load of electrical power systems in mid- and long-term horizons: a review. IET Gener Transm Distrib 10(16):3971–3977
Kuter S, Weber GW, Özmen A, Akyurek Z (2014) Modern applied mathematics for alternative modeling of the atmospheric effects on satellite images. In: Modeling, dynamics, optimization, and bioeconomics I: contributions from ICMOD 2010 and the 5th bioeconomy conference 2012. Springer International Publishing 73(1): 469–485
Kuter S, Akyurek Z, Weber GW (2018) Retrieval of fractional snow covered area from MODIS data by multivariate adaptive regression splines. Remote Sens Environ 205(1):236–252
Lee KY, Cha YT, Park JH (1992) Short-term load forecasting using an artificial neural network. IEEE Trans Power Syst 7(1):124–132
Liu B, Nowotarski J, Hong T, Weron R (2017) Probabilistic load forecasting via quantile regression averaging on sister forecasts. IEEE Trans Smart Grid 8(2):730–737
Montgomery DC, Peck EA, Vining GG (2015) Introduction to linear regression analysis. Wiley series in probability and statistics
Özmen A (2016) Robust optimization of spline models and complex regulatory networks: theory, methods and applications contributions to management science. Springer, Berlin
Özmen A, Weber GW (2014) RMARS: robustification of multivariate adaptive regression spline under polyhedral uncertainty. J Comput Appl Math 259(B):914–924
Özmen A, Weber GW, Batmaz I, Kropat E (2011) RCMARS: robustification of CMARS with different scenarios under polyhedral uncertainty set. Commun Nonlinear Sci Numer Simul (CNSNS) 16(12):4780–4787
Papalexopoulos AD, Hesterberg TC (1990) A regression-based approach to short-term system load forecasting. IEEE Trans Power Syst 5(4):1535–1547
Ravadanegh NS, Jahanyari N, Amini A, Taghizadeghan N (2016) Smart distribution grid multi-stage expansion planning under load forecasting uncertainty. IET Gener Transm Distrib 10(5):1136–1144
Rosenblatt F (1962) Principles of neurodynamics: perceptrons and the theory of brain mechanisms. Spartan Books 7(3):1–219
Rumelhart DE, Hinton GE, Williams RJ (1986) Learning representations by back-propagating errors. Nature 323(1):533–536
Saez-Gallego J, Morales JM (2017) Short-term forecasting of price-responsive loads using inverse optimization. IEEE Trans Smart Grid PP(99):1
Seber GAF, Lee AJ (2012) Linear regression analysis. Wiley Ser Probab Stat 1(1):1–565
Soliman SA, Al-Kandari AM (2010) Electric load modeling for long-term forecasting chapter. Electr Load Forecast 1(1):353–406
Song KB, Baek YS, Hong DH, Jang G (2005) Short-term load forecasting for the holidays using fuzzy linear regression method. IEEE Trans Power Syst 20(1):96–101
Tsoi AC (1989) Multilayer perceptron trained using radial basis functions. Electron Lett 25(19):1296–1297
Vapnik VN (1998) Statistical learning theory. Wiley, New York
Wang L, Zhang Z, Chen J (2017) Short-term electricity price forecasting with stacked denoising autoencoders. IEEE Trans Power Syst 32(4):2673–2681
Weber GW, Batmaz I, Köksal G, Taylan P, Yerlikaya Özkurt F (2012) CMARS: a new contribution to nonparametric regression with multivariate adaptive regression splines supported by continuous optimization. Inverse Probl Sci Eng (IPSE) 20(3):371–400
Werntges H (1990) Delta rule-based neural networks for inverse kinematics: error gradient reconstruction replaces the Teacher. IJCNN Int Joint Conf Neural Netw 3(1):415–420
Weron R (2014) Electricity price forecasting: a review of the state-of-the-art with a look into the future. Int J Forecast 30(4):1030–1081
Xiao L, Shao W, Liang T, Wang C (2016) A combined model based on multiple seasonal patterns and modified firefly algorithm for electrical load forecasting. Appl Energy 167(1):135–153
Xie J, Hong T (2017) Variable selection methods for probabilistic load forecasting: empirical evidence from the seven states of the United States. IEEE Trans Smart Grid PP(99):1
Xie J, Hong T, Stroud J (2015) Long-term retail energy forecasting with consideration of residential customer attrition. IEEE Trans Smart Grid 6(5):2245–2252
Zhang T, Goh AT (2016) Multivariate adaptive regression splines and neural network models for prediction of pile drivability. Geosci Front 7(1):45–52
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Nalcaci, G., Özmen, A. & Weber, G.W. Long-term load forecasting: models based on MARS, ANN and LR methods. Cent Eur J Oper Res 27, 1033–1049 (2019). https://doi.org/10.1007/s10100-018-0531-1
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10100-018-0531-1