Conventional models and artificial intelligence-based models for energy consumption forecasting: A review

Abdel-Aal, 1997, Forecasting monthly electric energy consumption in eastern Saudi Arabia using univariate time-series analysis, Energy, 22, 1059, 10.1016/S0360-5442(97)00032-7 Ahmad, 2018, A comprehensive overview on the data driven and large scale based approaches for forecasting of building energy demand: a review, Energy Build., 165, 301, 10.1016/j.enbuild.2018.01.017 Akdemir, 2012, 30 Akpinar, 2013, Estimating household natural gas consumption with multiple regression: effect of cycle, 188 Akpinar, 2013, 1 Amasyali, 2018, A review of data-driven building energy consumption prediction studies, Renew. Sustain. Energy Rev., 81, 1192, 10.1016/j.rser.2017.04.095 Amber, 2018, Intelligent techniques for forecasting electricity consumption of buildings, Energy, 157, 886, 10.1016/j.energy.2018.05.155 Amjady, 2011, Wind power prediction by a new forecast engine composed of modified hybrid neural network and enhanced particle swarm optimization, IEEE Trans. Sustain. Energy, 2, 265, 10.1109/TSTE.2011.2114680 Andersen, 2013, Long term forecasting of hourly electricity consumption in local areas in Denmark, Appl. Energy, 110, 147, 10.1016/j.apenergy.2013.04.046 Andersen, 2014, Differentiated long term projections of the hourly electricity consumption in local areas. The case of Denmark West, Appl. Energy, 135, 523, 10.1016/j.apenergy.2014.08.075 Ardakani, 2014, Novel effects of demand side management data on accuracy of electrical energy consumption modeling and long-term forecasting, Energy Convers. Manag., 78, 745, 10.1016/j.enconman.2013.11.019 Assareh, 2010, Application of PSO (particle swarm optimization) and GA (genetic algorithm) techniques on demand estimation of oil in Iran, Energy, 35, 5223, 10.1016/j.energy.2010.07.043 Aydin, 2014, The application of trend analysis for coal demand modeling, Energy Sources B Energy Econ. Plan. Policy, 10, 183, 10.1080/15567249.2013.813611 Aydin, 2014, The modeling and projection of primary energy consumption by the sources, Energy Sources B Energy Econ. Plan. Policy, 10, 67, 10.1080/15567249.2013.771716 Azadeh, 2011, A Neuro-fuzzy-stochastic frontier analysis approach for long-term natural gas consumption forecasting and behavior analysis: the cases of Bahrain, Saudi Arabia, Syria, and UAE, Appl. Energy, 88, 3850, 10.1016/j.apenergy.2011.04.027 Azadeh, 2013, Optimum estimation and forecasting of renewable energy consumption by artificial neural networks, Renew. Sustain. Energy Rev., 27, 605, 10.1016/j.rser.2013.07.007 Azadeh, 2008, A simulated-based neural network algorithm for forecasting electrical energy consumption in Iran, Energy Policy, 36, 2637, 10.1016/j.enpol.2008.02.035 Azadeh, 2009, A flexible fuzzy regression algorithm for forecasting oil consumption estimation, Energy Policy, 37, 5567, 10.1016/j.enpol.2009.08.017 Azadeh, 2013, A neuro-fuzzy-multivariate algorithm for accurate gas consumption estimation in South America with noisy inputs, Int. J. Electr. Power Energy Syst., 46, 315, 10.1016/j.ijepes.2012.10.013 Azadeh, 2009, A hybrid simulation-adaptive network based fuzzy inference system for improvement of electricity consumption estimation, Expert Syst. Appl., 36, 11108, 10.1016/j.eswa.2009.02.081 Azadeh, 2014, Artificial immune simulation for improved forecasting of electricity consumption with random variations, Int. J. Electr. Power Energy Syst., 55, 205, 10.1016/j.ijepes.2013.08.017 Azadeh, 2007, Integration of genetic algorithm, computer simulation and design of experiments for forecasting electrical energy consumption, Energy Policy, 35, 5229, 10.1016/j.enpol.2007.04.020 Azadeh, 2015, A neuro-fuzzy algorithm for improved gas consumption forecasting with economic, environmental and IT/IS indicators, J. Pet. Sci. Eng., 133, 716, 10.1016/j.petrol.2015.07.002 Bai, 2016, Daily natural gas consumption forecasting based on a structure-calibrated support vector regression approach, Energy Build., 127, 571, 10.1016/j.enbuild.2016.06.020 Baldacci, 2016, Natural gas consumption forecasting for anomaly detection, Expert Syst. Appl., 62, 190, 10.1016/j.eswa.2016.06.013 Behrang, 2011, Forecasting future oil demand in Iran using GSA (Gravitational Search Algorithm), Energy, 36, 5649, 10.1016/j.energy.2011.07.002 Bhaskar, 2012, AWNN-assisted wind power forecasting using feed-forward neural network, IEEE Trans. Sustain. Energy, 3, 306, 10.1109/TSTE.2011.2182215 Bianchi, 2015, Short-term electric load forecasting using echo state networks and PCA decomposition, IEEE Access, 3, 1931, 10.1109/ACCESS.2015.2485943 Bianco, 2010, Analysis and forecasting of nonresidential electricity consumption in Romania, Appl. Energy, 87, 3584, 10.1016/j.apenergy.2010.05.018 Bianco, 2014, Analysis and future outlook of natural gas consumption in the Italian residential sector, Energy Convers. Manag., 87, 754, 10.1016/j.enconman.2014.07.081 Bianco, 2014, Scenario analysis of nonresidential natural gas consumption in Italy, Appl. Energy, 113, 392, 10.1016/j.apenergy.2013.07.054 Bourdeau, 2019, Modeling and forecasting building energy consumption: a review of data-driven techniques, Sustain. Cities Soc., 48, 10.1016/j.scs.2019.101533 Burger, 2015, Gated ensemble learning method for demand-side electricity load forecasting, Energy Build., 109, 23, 10.1016/j.enbuild.2015.10.019 Canyurt, 2008, Application of genetic algorithm (GA) technique on demand estimation of fossil fuels in Turkey, Energy Policy, 36, 2562, 10.1016/j.enpol.2008.03.010 Cao, 2016, Support vector regression with fruit fly optimization algorithm for seasonal electricity consumption forecasting, Energy, 115, 734, 10.1016/j.energy.2016.09.065 Catalão, 2011, Short-term wind power forecasting in Portugal by neural networks and wavelet transform, Renew. Energy, 36, 1245, 10.1016/j.renene.2010.09.016 Cattaneo, 2011, Industrial coal demand in China: a provincial analysis, Resour. Energy Econ., 33, 12, 10.1016/j.reseneeco.2009.12.002 Chae, 2016, Artificial neural network model for forecasting sub-hourly electricity usage in commercial buildings, Energy Build., 111, 184, 10.1016/j.enbuild.2015.11.045 Chai, 2012, Demand forecast of petroleum product consumption in the Chinese transportation industry, Energies, 5, 577, 10.3390/en5030577 Chan, 1997, Modelling and forecasting the demand for coal in China, Energy Econ., 19, 271, 10.1016/S0140-9883(96)01019-5 Chang, 2011, Monthly electricity demand forecasting based on a weighted evolving fuzzy neural network approach, Int. J. Electr. Power Energy Syst., 33, 17, 10.1016/j.ijepes.2010.08.008 Chen, 2018, A novel data-driven approach for residential electricity consumption prediction based on ensemble learning, Energy, 150, 49, 10.1016/j.energy.2018.02.028 Chen, 2018, Forecasting day-ahead high-resolution natural-gas demand and supply in Germany, Appl. Energy, 228, 1091, 10.1016/j.apenergy.2018.06.137 Conejo, 2005, Day-ahead electricity price forecasting using the wavelet transform and ARIMA models, 20, 1035 Crompton, 2005, Energy consumption in China: past trends and future directions, Energy Econ., 27, 195, 10.1016/j.eneco.2004.10.006 Dalfard, 2013, A mathematical modeling for incorporating energy price hikes into total natural gas consumption forecasting, Appl. Math. Model., 37, 5664, 10.1016/j.apm.2012.11.012 Daut, 2017, Building electrical energy consumption forecasting analysis using conventional and artificial intelligence methods: a review, Renew. Sustain. Energy Rev., 70, 1108, 10.1016/j.rser.2016.12.015 Deb, 2017, A review on time series forecasting techniques for building energy consumption, Renew. Sustain. Energy Rev., 74, 902, 10.1016/j.rser.2017.02.085 Ding, 2018, A novel self-adapting intelligent grey model for forecasting China's natural-gas demand, Energy, 162, 393, 10.1016/j.energy.2018.08.040 Ding, 2017, Research on short-term and ultra-short-term cooling load prediction models for office buildings, Energy Build., 154, 254, 10.1016/j.enbuild.2017.08.077 Ediger, 2007, ARIMA forecasting of primary energy demand by fuel in Turkey, Energy Policy, 35, 1701, 10.1016/j.enpol.2006.05.009 Enerdata, 2018 Ervural, 2016, Model estimation of ARMA using genetic algorithms: a case study of forecasting natural gas consumption, Procedia Soc. Behav. Sci., 235, 537, 10.1016/j.sbspro.2016.11.066 Forouzanfar, 2012, Transport energy demand forecast using multi-level genetic programming, Appl. Energy, 91, 496, 10.1016/j.apenergy.2011.08.018 Forouzanfar, 2010, Modeling and estimation of the natural gas consumption for residential and commercial sectors in Iran, Appl. Energy, 87, 268, 10.1016/j.apenergy.2009.07.008 Foucquier, 2013, State of the art in building modelling and energy performances prediction: a review, Renew. Sustain. Energy Rev., 23, 272, 10.1016/j.rser.2013.03.004 Fuinhas, 2012, Energy consumption and economic growth nexus in Portugal, Italy, Greece, Spain and Turkey: an ARDL bounds test approach (1965–2009), Energy Econ., 34, 511, 10.1016/j.eneco.2011.10.003 Furtado, 1993, Forecasting of petroleum consumption in Brazil using the intensity of energy technique, Energy Policy, 21, 958, 10.1016/0301-4215(93)90184-H Ghanbari, 2013, A Cooperative Ant Colony Optimization-Genetic Algorithm approach for construction of energy demand forecasting knowledge-based expert systems, Knowl. Based Syst., 39, 194, 10.1016/j.knosys.2012.10.017 Ghasemi, 2016, A novel hybrid algorithm for electricity price and load forecasting in smart grids with demand-side management, Appl. Energy, 177, 40, 10.1016/j.apenergy.2016.05.083 Graves, 2012 Hao, 2015, China's farewell to coal: a forecast of coal consumption through 2020, Energy Policy, 86, 444, 10.1016/j.enpol.2015.07.023 Harris, 2018, Logistic growth curve modeling of US energy production and consumption, Renew. Sustain. Energy Rev., 96, 46, 10.1016/j.rser.2018.07.049 He, 2005, Oil consumption and CO2 emissions in China's road transport: current status, future trends, and policy implications, Energy Policy, 33, 1499, 10.1016/j.enpol.2004.01.007 He, 2017, Load forecasting via deep neural networks, Procedia Comput. Sci., 122, 308, 10.1016/j.procs.2017.11.374 He, 2018, Forecasting China's total energy demand and its structure using ADL-MIDAS model, Energy, 151, 420, 10.1016/j.energy.2018.03.067 Hussain, 2016, Forecasting electricity consumption in Pakistan: the way forward, Energy Policy, 90, 73, 10.1016/j.enpol.2015.11.028 Hyndman, 1996, Sample quantiles in statistical packages, 50, 361 Iniyan, 2003, The application of a Delphi technique in the linear programming optimization of future renewable energy options for India, Biomass Bioenergy, 24, 39, 10.1016/S0961-9534(02)00089-2 Jang, 1991, 762 Jebaraj, 2011, Forecasting of coal consumption using an artificial neural network and comparison with various forecasting techniques, Energy Sources, Part A Recovery, Util. Environ. Eff., 33, 1305, 10.1080/15567030903397859 Jiang, 2017, A hybrid forecasting model based on date-framework strategy and improved feature selection technology for short-term load forecasting, Energy, 119, 694, 10.1016/j.energy.2016.11.034 Jurado, 2015, Hybrid methodologies for electricity load forecasting: entropy-based feature selection with machine learning and soft computing techniques, Energy, 86, 276, 10.1016/j.energy.2015.04.039 Karadede, 2017, Breeder hybrid algorithm approach for natural gas demand forecasting model, Energy, 141, 1269, 10.1016/j.energy.2017.09.130 Kaynar, 2011, Forecasting of natural gas consumption with neural network and neuro fuzzy system, Energy Educ. Sci. Technol. A Energy Sci. Res., 26, 221 Kermanshahi, 1998, Recurrent neural network for forecasting next 10 years loads of nine Japanese utilities, Neurocomputing, 23, 125, 10.1016/S0925-2312(98)00073-3 Kipping, 2016, Modeling and disaggregating hourly electricity consumption in Norwegian dwellings based on smart meter data, Energy Build., 118, 350, 10.1016/j.enbuild.2016.02.042 Kovačič, 2014, Genetic programming prediction of the natural gas consumption in a steel plant, Energy, 66, 273, 10.1016/j.energy.2014.02.001 Kumar, 2010, Time series models (Grey-Markov, Grey Model with rolling mechanism and singular spectrum analysis) to forecast energy consumption in India, Energy, 35, 1709, 10.1016/j.energy.2009.12.021 Kutner, 2004 LeCun, 2015, Deep learning, Nature, 521, 436, 10.1038/nature14539 Lendasse, 2002, Forecasting electricity consumption using nonlinear projection and self-organizing maps, Neurocomputing, 48, 299, 10.1016/S0925-2312(01)00646-4 Lewis, 1986 Li, 2011, Forecasting the growth of China's natural gas consumption, Energy, 36, 1380, 10.1016/j.energy.2011.01.003 Li, 2018, Analysis and forecasting of the oil consumption in China based on combination models optimized by artificial intelligence algorithms, Energy, 144, 243, 10.1016/j.energy.2017.12.042 Li, 2015, Building's electricity consumption prediction using optimized artificial neural networks and principal component analysis, Energy Build., 108, 106, 10.1016/j.enbuild.2015.09.002 Li, 2018, A hybrid teaching-learning artificial neural network for building electrical energy consumption prediction, Energy Build., 174, 323, 10.1016/j.enbuild.2018.06.017 Limanond, 2011, Projection of future transport energy demand of Thailand, Energy Policy, 39, 2754, 10.1016/j.enpol.2011.02.045 Liu, 2016, A grey neural network and input-output combined forecasting model. Primary energy consumption forecasts in Spanish economic sectors, Energy, 115, 1042, 10.1016/j.energy.2016.09.017 López, 2012, Application of SOM neural networks to short-term load forecasting: the Spanish electricity market case study, Electr. Power Syst. Res., 91, 18, 10.1016/j.epsr.2012.04.009 Lu, 2015, Distributed HS-ARTMAP and its forecasting model for electricity load, Appl. Soft Comput., 32, 13, 10.1016/j.asoc.2015.03.037 Ma, 2017, Application of a novel time-delayed polynomial grey model to predict the natural gas consumption in China, J. Comput. Appl. Math., 324, 17, 10.1016/j.cam.2017.04.020 Marino, 2016, Building energy load forecasting using deep neural networks, 7046 Meer, 2018, Probabilistic forecasting of electricity consumption, photovoltaic power generation and net demand of an individual building using Gaussian Processes, Appl. Energy, 213, 195, 10.1016/j.apenergy.2017.12.104 Melikoglu, 2013, Vision 2023: forecasting Turkey's natural gas demand between 2013 and 2030, Renew. Sustain. Energy Rev., 22, 393, 10.1016/j.rser.2013.01.048 Miljanovic, 2012, Comparative analysis of recurrent and finite impulse response neural networks in time series prediction, Indian J. Comput. Sci. Eng., 3, 180 Nadimi, 2017, Analyzing of renewable and non-renewable energy consumption via Bayesian inference, 2773 Ou, 2010, Scenario analysis on alternative fuel/vehicle for China's future road transport: life-cycle energy demand and GHG emissions, Energy Policy, 38, 3943, 10.1016/j.enpol.2010.03.018 Ouedraogo, 2017, Modeling sustainable long-term electricity supply-demand in Africa, Appl. Energy, 190, 1047, 10.1016/j.apenergy.2016.12.162 Özmen, 2018, Natural gas consumption forecast with MARS and CMARS models for residential users, Energy Econ., 70, 357, 10.1016/j.eneco.2018.01.022 Panapakidis, 2017, Day-ahead natural gas demand forecasting based on the combination of wavelet transform and ANFIS/genetic algorithm/neural network model, Energy, 118, 231, 10.1016/j.energy.2016.12.033 Pao, 2009, Forecasting energy consumption in Taiwan using hybrid nonlinear models, Energy, 34, 1438, 10.1016/j.energy.2009.04.026 Pappas, 2010, Electricity demand load forecasting of the Hellenic power system using an ARMA model, Electr. Power Syst. Res., 80, 256, 10.1016/j.epsr.2009.09.006 Potočnik, 2014, Comparison of static and adaptive models for short-term residential natural gas forecasting in Croatia, Appl. Energy, 129, 94, 10.1016/j.apenergy.2014.04.102 Pourazarm, 2013, Estimating and forecasting residential electricity demand in Iran, Econ. Modell., 35, 546, 10.1016/j.econmod.2013.08.006 Qing, 2018, Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM, Energy, 148, 461, 10.1016/j.energy.2018.01.177 Rueda, 2019, Straight line programs for energy consumption modelling, Appl. Soft Comput., 80, 310, 10.1016/j.asoc.2019.04.001 Sabo, 2011, Mathematical models of natural gas consumption, Energy Convers. Manag., 52, 1721, 10.1016/j.enconman.2010.10.037 Sánchez-Úbeda, 2007, Modeling and forecasting industrial end-use natural gas consumption, Energy Econ., 29, 710, 10.1016/j.eneco.2007.01.015 Shaikh, 2016, Forecasting natural gas demand in China: logistic modelling analysis, Int. J. Electr. Power Energy Syst., 77, 25, 10.1016/j.ijepes.2015.11.013 Shaikh, 2017, Forecasting China's natural gas demand based on optimised nonlinear grey models, Energy, 140, 941, 10.1016/j.energy.2017.09.037 Shao, 2015, A new semiparametric and EEMD based framework for mid-term electricity demand forecasting in China: hidden characteristic extraction and probability density prediction, Renew. Sustain. Energy Rev., 52, 876, 10.1016/j.rser.2015.07.159 Shayeghi, 2015, Simultaneous day-ahead forecasting of electricity price and load in smart grids, Energy Convers. Manag., 95, 371, 10.1016/j.enconman.2015.02.023 Shine, 2018, Machine-learning algorithms for predicting on-farm direct water and electricity consumption on pasture based dairy farms, Comput. Electron. Agric., 150, 74, 10.1016/j.compag.2018.03.023 Soldo, 2012, Forecasting natural gas consumption, Appl. Energy, 92, 26, 10.1016/j.apenergy.2011.11.003 Soldo, 2014, Improving the residential natural gas consumption forecasting models by using solar radiation, Energy Build., 69, 498, 10.1016/j.enbuild.2013.11.032 Suganthi, 2012, Energy models for demand forecasting—a review, Renew. Sustain. Energy Rev., 16, 1223, 10.1016/j.rser.2011.08.014 Sujjaviriyasup, 2017, A new class of MODWT-SVM-DE hybrid model emphasizing on simplification structure in data pre-processing: a case study of annual electricity consumptions, Appl. Soft Comput., 54, 150, 10.1016/j.asoc.2017.01.022 Suykens, 2002 Szoplik, 2015, Forecasting of natural gas consumption with artificial neural networks, Energy, 85, 208, 10.1016/j.energy.2015.03.084 Tamba, 2018, Forecasting natural gas: a literature survey, Int. J. Energy Econ. Policy, 8, 216 Tascikaraoglu, 2014, An adaptive load dispatching and forecasting strategy for a virtual power plant including renewable energy conversion units, Appl. Energy, 119, 445, 10.1016/j.apenergy.2014.01.020 Taşpınar, 2013, Forecasting of daily natural gas consumption on regional basis in Turkey using various computational methods, Energy Build., 56, 23, 10.1016/j.enbuild.2012.10.023 Tsai, 2015, Using grey models for forecasting China's growth trends in renewable energy consumption, Clean Technol. Environ. Policy, 18, 563, 10.1007/s10098-015-1017-7 Tsai, 2017, Models for forecasting growth trends in renewable energy, Renew. Sustain. Energy Rev., 77, 1169, 10.1016/j.rser.2016.06.001 Vondráček, 2008, A statistical model for the estimation of natural gas consumption, Appl. Energy, 85, 362, 10.1016/j.apenergy.2007.07.004 Wahid, 2015, The determinants and forecasting of coal consumption in Pakistan, J. Energy Technol. Policy, 5 Wang, 2018, Has China's coal consumption already peaked? A demand-side analysis based on hybrid prediction models, Energy, 162, 272, 10.1016/j.energy.2018.08.031 Wang, 2018, An improved grey model optimized by multi-objective ant lion optimization algorithm for annual electricity consumption forecasting, Appl. Soft Comput., 72, 321, 10.1016/j.asoc.2018.07.022 Wang, 2016, China's natural gas production and consumption analysis based on the multicycle Hubbert model and rolling Grey model, Renew. Sustain. Energy Rev., 53, 1149, 10.1016/j.rser.2015.09.067 Wang, 2018, Effective sparse adaboost method with ESN and FOA for industrial electricity consumption forecasting in China, Energy, 155, 1013, 10.1016/j.energy.2018.04.175 Wang, 2018, A seasonal GM(1,1) model for forecasting the electricity consumption of the primary economic sectors, Energy, 154, 522, 10.1016/j.energy.2018.04.155 Wei, 2019, Daily natural gas load forecasting based on a hybrid deep learning model, Energies, 12, 218, 10.3390/en12020218 Wei, 2019, Short-term forecasting of natural gas consumption using factor selection algorithm and optimized support vector regression, J. Energy Resour. Technol., 141, 10.1115/1.4041413 Wei, 2019, Daily natural gas consumption forecasting via the application of a novel hybrid model, Appl. Energy, 250, 358, 10.1016/j.apenergy.2019.05.023 Wright, 2012 Wu, 2018, Grey-related least squares support vector machine optimization model and its application in predicting natural gas consumption demand, J. Comput. Appl. Math., 338, 212, 10.1016/j.cam.2018.01.033 Xie, 2015, Forecasting China's energy demand and self-sufficiency rate by grey forecasting model and Markov model, Int. J. Electr. Power Energy Syst., 66, 1, 10.1016/j.ijepes.2014.10.028 Xie, 2005, Discrete GM (1, 1) and mechanism of grey forecasting model, Syst. Eng. Theor. Pract., 25, 93 Xu, 2010, Forecasting China's natural gas consumption based on a combination model, J. Nat. Gas Chem., 19, 493, 10.1016/S1003-9953(09)60100-6 Yang, 2014, Thermal comfort and building energy consumption implications – a review, Appl. Energy, 115, 164, 10.1016/j.apenergy.2013.10.062 Yang, 2016, Modelling a combined method based on ANFIS and neural network improved by DE algorithm: a case study for short-term electricity demand forecasting, Appl. Soft Comput., 49, 663, 10.1016/j.asoc.2016.07.053 Yaslan, 2017, Empirical mode decomposition based denoising method with support vector regression for time series prediction: a case study for electricity load forecasting, Measurement, 103, 52, 10.1016/j.measurement.2017.02.007 Yezioro, 2008, An applied artificial intelligence approach towards assessing building performance simulation tools, Energy Build., 40, 612, 10.1016/j.enbuild.2007.04.014 Yona, 2008, 1 Yu, 2014, A short-term load forecasting model of natural gas based on optimized genetic algorithm and improved BP neural network, Appl. Energy, 134, 102, 10.1016/j.apenergy.2014.07.104 Yu, 2018, Online big data-driven oil consumption forecasting with Google trends, Int. J. Forecast., 35, 213, 10.1016/j.ijforecast.2017.11.005 Zeng, 2016, Forecasting the natural gas demand in China using a self-adapting intelligent grey model, Energy, 112, 810, 10.1016/j.energy.2016.06.090 Zhang, 2018, Short term electricity load forecasting using a hybrid model, Energy, 158, 774, 10.1016/j.energy.2018.06.012 Zhang, 2009, Forecasting the transport energy demand based on PLSR method in China, Energy, 34, 1396, 10.1016/j.energy.2009.06.032 Zhang, 2012, Fuzzy wavelet neural networks for city electric energy consumption forecasting, Energy Procedia, 17, 1332, 10.1016/j.egypro.2012.02.248 Zhang, 2015, Forecasting natural gas consumption in China by Bayesian model averaging, Energy Rep., 1, 216, 10.1016/j.egyr.2015.11.001 Zhang, 2017, Short-term electric load forecasting based on singular spectrum analysis and support vector machine optimized by Cuckoo search algorithm, Electr. Power Syst. Res., 146, 270, 10.1016/j.epsr.2017.01.035 Zhao, 2011, An investigation of coal demand in China based on the variable weight combination forecasting model, J. Resour. Ecol., 2, 126 Zhu, 2015, Short-term natural gas demand prediction based on support vector regression with false neighbours filtered, Energy, 80, 428, 10.1016/j.energy.2014.11.083 Zhu, 2014, Structural analysis and total coal demand forecast in China, Discrete Dynam Nat. Soc., 1 Zhu, 2011, A seasonal hybrid procedure for electricity demand forecasting in China, Appl. Energy, 88, 3807, 10.1016/j.apenergy.2011.05.005