A novel artificial neural network model for forecasting electricity demand enhanced with population-weighted temperature mean and the unemployment rate

Year 2022, Volume: 6 Issue: 2, 178 - 189, 15.04.2022

Mustafa Comert Ali Yıldız

https://doi.org/10.31127/tuje.903876

Cited By: 6

Abstract

Precise electricity demand forecasting has principal significance in the energy production planning of the developing countries. Especially during the last decade, numerous recent methods have been utilized to predict the forthcoming electricity demand in different time resolutions accurately. This contribution presents a novel approach, which improves the forecasting of Turkey’s electricity demand in monthly time resolution. An artificial neural network model has been proposed with appropriate input features. Yearly-based gross demand shows approximately linear increment due to population increase and economic growth, while monthly-based gross demand indicates an oscillation due to the effect of seasonal temperature fluctuations. However, there is no clear linear relation between electricity demand and temperature; for the ideal case, it is the V-shaped curve around balance point temperature. Since temperature levels in each region of the country demonstrate a high variance even in the same time period, weighted average temperature point was calculated with respect to the population weights of the selected regions of Turkey. In order to fit a function for monthly oscillations, a linear function according to weighted average temperature point was created. Unemployment data was added to the training data set as an indicator of economic fluctuations. The mean absolute percentage errors of the model were calculated for training, validation, and testing as 3.77 %, 2.02 %, and 1.95 % respectively.

Keywords

Monthly electricity demand, Balance point temperature, Unemployment Rate, Artificial neural network

Project Number

2019-2-TP3-3530

References

• Ahmed T, Muttaqi K M & Agalgaonkar A P (2012). Climate change impacts on electricity demand in the State of New South Wales, Australia. Applied Energy, 98, 376–383. https://doi.org/10.1016/j.apenergy.2012.03.059
• Ahmed T, Vu D H, Muttaqi K M & Agalgaonkar A P (2018). Load forecasting under changing climatic conditions for the city of Sydney, Australia. Energy, 142, 911–919. https:// doi.org/10.1016/j.energy.2017.10.070
• Akay D & Atak M (2007). Grey prediction with rolling mechanism for electricity demand forecasting of Turkey. Energy, 32(9), 1670–1675. https://doi.org/10.1016/j.energy. 2006.11.014
• Al-Bajjali S K & Shamayleh A Y (2018). Estimating the determinants of electricity consumption in Jordan. Energy, 147, 1311–1320. https:// doi.org/10.1016/j.energy.2018.01.010
• AL-Musaylh M S, Deo R C, Adamowski J F & Li Y (2019). Short-term electricity demand forecasting using machine learning methods enriched with ground-based climate and ECMWF Reanalysis atmospheric predictors in southeast Queensland, Australia. Renewable and Sustainable Energy Reviews, 113. https://doi.org/10.1016/j.rser.2019.109293
• Andersen F M, Baldini M, Hansen L G & Jensen C L (2017). Households’ hourly electricity consumption and peak demand in Denmark. Applied Energy, 208(May), 607–619. https://doi.org/10.1016/j.apenergy.2017.09.094
• Arisoy I & Ozturk I (2014). Estimating industrial and residential electricity demand in Turkey: A time varying parameter approach. Energy, 66, 959–964. https://doi.org/10.1016/j.energy. 2014.01.016
• Bedi J & Toshniwal D (2019). Deep learning framework to forecast electricity demand. Applied Energy, 238, 1312–1326. https://doi.org/10.1016/j.apenergy.2019.01.113
• Cabral J. de A, Legey L F L & Freitas Cabral M. V. de. (2017). Electricity consumption forecasting in Brazil: A spatial econometrics approach. Energy, 126, 124–131. https://doi.org/ 10.1016/j.energy.2017.03.005
• Çevik H H & Çunkaş M (2015). Short-term load forecasting using fuzzy logic and ANFIS. Neural Computing and Applications, 26(6), 1355–1367. https://doi.org/10.1007/s00521-014-1809-4
• Chang Y, Kim C S, Miller J I, Park J Y & Park S (2016). A new approach to modeling the effects of temperature fluctuations on monthly electricity demand. Energy Economics, 60, 206–216. https://doi.org/10.1016/j.eneco. 2016.09.016
• Cömert M (2020). Project Repository Electiricity Demand Estimatiton With Enhanced ANN Model. Retrieved April 1, 2021, from Forecasting electricity demand by artificial neural network enhanced with population-weighted temperature mean and the unemployment rate website: https:// github.com/mustasyon/electricityDemandEstimationANN
• Damari Y & Kissinger M (2018). An integrated analysis of households ’ electricity consumption in Israel. Energy Policy, 119(June 2017), 51–58. https://doi.org/10.1016/ j.enpol.2018.04.010
• Dedinec A, Filiposka S, Dedinec A & Kocarev L (2016). Deep belief network based electricity load forecasting: An analysis of Macedonian case. Energy, 115, 1688–1700. https:// doi.org/10.1016/j.energy.2016.07.090
• Dilaver Z & Hunt L C (2011). Modelling and forecasting Turkish residential electricity demand. Energy Policy, 39(6), 3117–3127. https://doi.org/10.1016/j.enpol.2011.02.059
• Ding S, Hipel K W & Dang Y guo (2018). Forecasting China’s electricity consumption using a new grey prediction model. Energy, 149, 314–328. https://doi.org/10.1016/ j.energy.2018.01.169
• Duran Toksari M (2007). Ant colony optimization approach to estimate energy demand of Turkey. Energy Policy, 35(8), 3984–3990. https://doi.org/10.1016/j.enpol.2007.01.028
• Ferreira P M, Cuambe I D, Ruano A E & Pestana R (2013). Forecasting the Portuguese electricity consumption using least-squares support vector machines. IFAC Proceedings Volumes (IFAC-PapersOnline), 3(PART 1), 411–416. https://doi.org/10.3182/20130902-3-CN-3020.00138
• Gulcu S & Kodaz H (2017). The estimation of the electricity energy demand using particle swarm optimization algorithm: A case study of Turkey. Procedia Computer Science, 111, 64–70. https://doi.org/10.1016/j.procs. 2017.06.011
• Günay M E (2016). Forecasting annual gross electricity demand by artificial neural networks using predicted values of socio-economic indicators and climatic conditions: Case of Turkey. Energy Policy, 90, 92–101. https://doi.org/10.1016/j.enpol.2015.12.019
• Hamzacebi C & Es H A (2014). Forecasting the annual electricity consumption of Turkey using an optimized grey model. Energy, 70, 165–171. https://doi.org/10.1016/j.energy.2014.03.105
• Hamzaçebi C, Es H A & Çakmak R (2017). Forecasting of Turkey’s monthly electricity demand by seasonal artificial neural network. Neural Computing and Applications, 1–15. https://doi.org/10.1007/s00521-017-3183-5
• Kavaklioglu K (2011). Modeling and prediction of Turkey’s electricity consumption using Support Vector Regression. Applied Energy, 88(1), 368–375. https://doi.org/10.1016/ j.apenergy.2010.07.021
• Kavaklioglu K (2014). Robust electricity consumption modeling of Turkey using Singular Value Decomposition. International Journal of Electrical Power and Energy Systems, 54, 268–276. https://doi.org/10.1016/j.ijepes. 2013.07.020
• Kavaklioglu K, Ceylan H, Ozturk H K & Canyurt O E (2009). Modeling and prediction of Turkey’s electricity consumption using Artificial Neural Networks. Energy Conversion and Management, 50(11), 2719–2727. https://doi.org/10.1016/ j.enconman.2009.06.016
• Kaytez F, Taplamacioglu M C, Cam E & Hardalac F (2015). Forecasting electricity consumption: A comparison of regression analysis, neural networks and least squares support vector machines. International Journal of Electrical Power and Energy Systems, 67, 431–438. https://doi.org/10.1016/j.ijepes.2014.12.036
• Kim Y, Son H-G & Kim S (2019). Short term electricity load forecasting for institutional buildings. Energy Reports, 5, 1270–1280. https://doi.org/10.1016/j.egyr.2019.08.086
• Kiran M S, Özceylan E, Gündüz M & Paksoy T (2012). Swarm intelligence approaches to estimate electricity energy demand in Turkey. Knowledge-Based Systems, 36, 93–103. https://doi.org/10.1016/j.knosys.2012.06.009
• Kucukali S & Baris K (2010). Turkey’s short-term gross annual electricity demand forecast by fuzzy logic approach. Energy Policy, 38(5), 2438–2445. https://doi.org/10.1016/j.enpol. 2009.12.037
• Lebotsa M E, Sigauke C, Bere A, Fildes R & Boylan J E (2018). Short term electricity demand forecasting using partially linear additive quantile regression with an application to the unit commitment problem. Applied Energy, 222(December 2017), 104–118. https://doi.org/10.1016/j.apenergy. 2018.03.155
• Maldonado S, González A & Crone S (2019). Automatic time series analysis for electric load forecasting via support vector regression. Applied Soft Computing Journal, 83, 105616. https://doi.org/10.1016/j.asoc.2019.105616
• Marmaras C, Javed A, Cipcigan L & Rana O (2017). Predicting the energy demand of buildings during triad peaks in GB. Energy and Buildings, 141, 262–273. https://doi.org/ 10.1016/j.enbuild.2017.02.046
• MGM (2020). MEVBIS. Retrieved April 15, 2020, from https://mevbis.mgm.gov.tr/mevbis/ ui/index.html#/Login
• Mostafavi E S, Mostafavi S I, Jaafari A & Hosseinpour F (2013). A novel machine learning approach for estimation of electricity demand: An empirical evidence from Thailand. Energy Conversion and Management, 74, 548–555. https://doi.org/10.1016/j.enconman. 2013.06.031
• Nalcaci G, Özmen A & Weber G W (2018). Long-term load forecasting: models based on MARS, ANN and LR methods. Central European Journal of Operations Research. https://doi.org/ 10.1007/s10100-018-0531-1
• Nick MacMackin, Miller L & Carriveau R (2019). Modeling and disaggregating hourly effects of weather on sectoral electricity demand. Energy, 188, 115956. https://doi.org/10.1016 /j.energy.2019.115956
• Oğcu G, Demirel O F & Zaim S (2012). Forecasting Electricity Consumption with Neural Networks and Support Vector Regression. Procedia - Social and Behavioral Sciences, 58, 1576–1585. https://doi.org/10.1016/j.sbspro.2012.09.1144
• Ozturk H K & Ceylan H (2005). Forecasting total and industrial sector electricity demand based on genetic algorithm approach: Turkey case study. International Journal of Energy Research, 29(9), 829–840. https://doi.org/ 10.1002/er.1092
• Ozturk H K, Ceylan H, Canyurt O E & Hepbasli A (2005). Electricity estimation using genetic algorithm approach: A case study of Turkey. Energy, 30(7), 1003–1012. https://doi.org/ 10.1016/j.energy.2004.08.008
• Rahman A, Srikumar V & Smith A D (2018). Predicting electricity consumption for commercial and residential buildings using deep recurrent neural networks. Applied Energy, 212(December 2017), 372–385. https://doi.org/10.1016/j.apenergy.2017.12.051
• Rallapalli S R & Ghosh S (2012). Forecasting monthly peak demand of electricity in India-A critique. Energy Policy, 45, 516–520. https://doi.org/10.1016/j.enpol.2012.02.064
• Singh P, Dwivedi P & Kant V (2019). A hybrid method based on neural network and improved environmental adaptation method using Controlled Gaussian Mutation with real parameter for short-term load forecasting. Energy, 174, 460–477. https://doi.org/ 10.1016/j.energy.2019.02.141
• Son H & Kim C (2017). Short-term forecasting of electricity demand for the residential sector using weather and social variables. Resources, Conservation and Recycling, 123, 200–207. https://doi.org/10.1016/j.resconrec.2016.01.016
• TEİAŞ (2020). ELECTRICAL STATISTICS. Retrieved April 15, 2020, from https://www.teias.gov.tr /tr/elektrik-istatistikleri
• Toksari M D (2016). A hybrid algorithm of Ant Colony Optimization (ACO) and Iterated Local Search (ILS) for estimating electricity domestic consumption: Case of Turkey. International Journal of Electrical Power and Energy Systems, 78, 776–782. https://doi.org/10.1016/ j.ijepes.2015.12.032
• Torrini F C, Souza R C, Cyrino Oliveira F L & Moreira Pessanha J F (2016). Long term electricity consumption forecast in Brazil: A fuzzy logic approach. Socio-Economic Planning Sciences, 54, 18–27. https://doi.org/10.1016/ j.seps.2015.12.002
• TUIK (2020). Unemplotment Statistics. Retrieved April 15, 2020, from https://biruni.tuik.gov.tr /isgucuapp/isgucu.zul
• Tunç M, Çamdali Ü & Parmaksizoglu C (2006). Comparison of Turkey’s electrical energy consumption and production with some European countries and optimization of future electrical power supply investments in Turkey. Energy Policy, 34(1), 50–59. https://doi.org/ 10.1016/j.enpol.2004.04.027
• Tutun S, Chou C A & Caniyilmaz E (2015). A new forecasting framework for volatile behavior in net electricity consumption: A case study in Turkey. Energy, 93, 2406–2422. https:// doi.org/10.1016/j.energy.2015.10.064
• Vu D H, Muttaqi K M & Agalgaonkar A P (2015). A variance inflation factor and backward elimination based robust regression model for forecasting monthly electricity demand using climatic variables. Applied Energy, 140, 385–394. https://doi.org/10.1016/j.apenergy. 2014.12.011
• Vu D H, Muttaqi K M, Agalgaonkar A P & Bouzerdoum A (2017). Short-term electricity demand forecasting using autoregressive based time varying model incorporating representative data adjustment. Applied Energy, 205(March), 790–801. https://doi.org /10.1016/j.apenergy.2017.08.135
• Wang L, Hu H, Ai X Y & Liu H (2018). Effective electricity energy consumption forecasting using echo state network improved by differential evolution algorithm. Energy, 153, 801–815. https://doi.org/10.1016/j.energy .2018.04.078
• Wu J, Cui Z, Chen Y, Kong D & Wang Y G (2019). A new hybrid model to predict the electrical load in five states of Australia. Energy, 166, 598–609. https://doi.org/10.1016/j.energy. 2018.10.076
• Wu L, Gao X, Xiao Y, Yang Y & Chen X (2018). Using a novel multi-variable grey model to forecast the electricity consumption of Shandong Province in China. Energy, 157, 327–335. https://doi.org/10.1016/j.energy. 2018.05.147
• Xu W, Peng H, Zeng X, Zhou F, Tian X & Peng X (2019). Deep belief network-based AR model for nonlinear time series forecasting. Applied Soft Computing Journal, 77, 605–621. https://doi.org/10.1016/j.asoc.2019.02.006
• Yang A, Li W & Yang X (2019). Short-term electricity load forecasting based on feature selection and Least Squares Support Vector Machines. Knowledge-Based Systems, 163, 159–173. https://doi.org/10.1016/j.knosys. 2018.08.027
• Yu S, Wang K & Wei Y M (2015). A hybrid self-adaptive Particle Swarm Optimization-Genetic Algorithm-Radial Basis Function model for annual electricity demand prediction. Energy Conversion and Management, 91, 176–185. https://doi.org/10.1016/j.enconman.2014.11.059
• Yukseltan E, Yucekaya A & Bilge A H (2017). Forecasting electricity demand for Turkey: Modeling periodic variations and demand segregation. Applied Energy, 193, 287–296. https://doi.org/10.1016/j.apenergy.2017.02.054
• Yumurtaci Z & Asmaz E (2004). Electric energy demand of Turkey for the year 2050. Energy Sources, 26(12), 1157–1164. https://doi.org/ 10.1080/00908310490441520
• Zahedi G, Azizi S, Bahadori A, Elkamel A & Wan Alwi S R (2013). Electricity demand estimation using an adaptive neuro-fuzzy network: A case study from the Ontario province - Canada. Energy, 49(1), 323–328. https://doi.org/10.1016/j.energy.2012.10.019