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Time series models (Grey-Markov, Grey Model with rolling mechanism and singular spectrum analysis) to forecast energy consumption in India

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Cited by:

  1. Quoc Bao Pham & Tao-Chang Yang & Chen-Min Kuo & Hung-Wei Tseng & Pao-Shan Yu, 2021. "Coupling Singular Spectrum Analysis with Least Square Support Vector Machine to Improve Accuracy of SPI Drought Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(3), pages 847-868, February.
  2. Zhineng Hu & Jing Ma & Liangwei Yang & Xiaoping Li & Meng Pang, 2019. "Decomposition-Based Dynamic Adaptive Combination Forecasting for Monthly Electricity Demand," Sustainability, MDPI, vol. 11(5), pages 1-25, February.
  3. Naiming Xie & Alan Pearman, 2014. "Forecasting energy consumption in China following instigation of an energy-saving policy," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 74(2), pages 639-659, November.
  4. George C. Efthimiou & Panos Kalimeris & Spyros Andronopoulos & John G. Bartzis, 2018. "Statistical Projection of Material Intensity: Evidence from the Global Economy and 107 Countries," Journal of Industrial Ecology, Yale University, vol. 22(6), pages 1465-1472, December.
  5. R. Rajesh, 2023. "Grey Markov Models for Predicting the Social Sustainability Performances of Firms," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 168(1), pages 297-351, August.
  6. Deng, Yanqiao & Ma, Xin & Zhang, Peng & Cai, Yubin, 2022. "Multi-step ahead forecasting of daily urban gas load in Chengdu using a Tanimoto kernel-based NAR model and Whale optimization," Energy, Elsevier, vol. 260(C).
  7. Ying-Fang Huang & Chia-Nan Wang & Hoang-Sa Dang & Shun-Te Lai, 2015. "Predicting the Trend of Taiwan’s Electronic Paper Industry by an Effective Combined Grey Model," Sustainability, MDPI, vol. 7(8), pages 1-20, August.
  8. Wu, Zhibin & Xu, Jiuping, 2013. "Predicting and optimization of energy consumption using system dynamics-fuzzy multiple objective programming in world heritage areas," Energy, Elsevier, vol. 49(C), pages 19-31.
  9. Do, Linh Phuong Catherine & Lin, Kuan-Heng & Molnár, Peter, 2016. "Electricity consumption modelling: A case of Germany," Economic Modelling, Elsevier, vol. 55(C), pages 92-101.
  10. V. R. Bityukova, 2022. "Environmental Consequences of the Transformation of the Sectoral Structure of the Economy of Russian Regions and Cities in the Post-Soviet Period," Regional Research of Russia, Springer, vol. 12(1), pages 96-111, March.
  11. Wang, Qiang & Li, Shuyu & Li, Rongrong & Ma, Minglu, 2018. "Forecasting U.S. shale gas monthly production using a hybrid ARIMA and metabolic nonlinear grey model," Energy, Elsevier, vol. 160(C), pages 378-387.
  12. Tsai, Bi-Huei & Chang, Chih-Jen & Chang, Chun-Hsien, 2016. "Elucidating the consumption and CO2 emissions of fossil fuels and low-carbon energy in the United States using Lotka–Volterra models," Energy, Elsevier, vol. 100(C), pages 416-424.
  13. Lee, Yi-Shian & Tong, Lee-Ing, 2012. "Forecasting nonlinear time series of energy consumption using a hybrid dynamic model," Applied Energy, Elsevier, vol. 94(C), pages 251-256.
  14. Jinli Duan & Feng Jiao & Qishan Zhang & Zhibin Lin, 2017. "Predicting Urban Medical Services Demand in China: An Improved Grey Markov Chain Model by Taylor Approximation," IJERPH, MDPI, vol. 14(8), pages 1-12, August.
  15. Yuan, Chaoqing & Liu, Sifeng & Fang, Zhigeng, 2016. "Comparison of China's primary energy consumption forecasting by using ARIMA (the autoregressive integrated moving average) model and GM(1,1) model," Energy, Elsevier, vol. 100(C), pages 384-390.
  16. Lili Wang & Lina Zhan & Rongrong Li, 2019. "Prediction of the Energy Demand Trend in Middle Africa—A Comparison of MGM, MECM, ARIMA and BP Models," Sustainability, MDPI, vol. 11(8), pages 1-16, April.
  17. Ma, Xin & Deng, Yanqiao & Ma, Minda, 2024. "A novel kernel ridge grey system model with generalized Morlet wavelet and its application in forecasting natural gas production and consumption," Energy, Elsevier, vol. 287(C).
  18. Ju, Jingrui & Liu, Luning & Feng, Yuqiang, 2018. "Citizen-centered big data analysis-driven governance intelligence framework for smart cities," Telecommunications Policy, Elsevier, vol. 42(10), pages 881-896.
  19. Wu, Qunli & Peng, Chenyang, 2017. "A hybrid BAG-SA optimal approach to estimate energy demand of China," Energy, Elsevier, vol. 120(C), pages 985-995.
  20. Hiloidhari, Moonmoon & Das, Dhiman & Baruah, D.C., 2014. "Bioenergy potential from crop residue biomass in India," Renewable and Sustainable Energy Reviews, Elsevier, vol. 32(C), pages 504-512.
  21. Sotirios Bersimis & Stavros Degiannakis & Dimitrios Georgakellos, 2017. "Real-time monitoring of carbon monoxide using value-at-risk measure and control charting," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(1), pages 89-108, January.
  22. Emre Yakut & Ezel Özkan, 2020. "Modeling of Energy Consumption Forecast with Economic Indicators Using Particle Swarm Optimization and Genetic Algorithm: An Application in Turkey between 1979 and 2050," Alphanumeric Journal, Bahadir Fatih Yildirim, vol. 8(1), pages 59-78, June.
  23. Ding, Song & Hipel, Keith W. & Dang, Yao-guo, 2018. "Forecasting China's electricity consumption using a new grey prediction model," Energy, Elsevier, vol. 149(C), pages 314-328.
  24. Atif Maqbool Khan & Magdalena Osińska, 2021. "How to Predict Energy Consumption in BRICS Countries?," Energies, MDPI, vol. 14(10), pages 1-21, May.
  25. Franco, Sainu & Mandla, Venkata Ravibabu & Ram Mohan Rao, K., 2017. "Urbanization, energy consumption and emissions in the Indian context A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 71(C), pages 898-907.
  26. Wu, Lifeng & Gao, Xiaohui & Xiao, Yanli & Yang, Yingjie & Chen, Xiangnan, 2018. "Using a novel multi-variable grey model to forecast the electricity consumption of Shandong Province in China," Energy, Elsevier, vol. 157(C), pages 327-335.
  27. Xu, Ning & Dang, Yaoguo & Gong, Yande, 2017. "Novel grey prediction model with nonlinear optimized time response method for forecasting of electricity consumption in China," Energy, Elsevier, vol. 118(C), pages 473-480.
  28. Shuyu Li & Rongrong Li, 2017. "Comparison of Forecasting Energy Consumption in Shandong, China Using the ARIMA Model, GM Model, and ARIMA-GM Model," Sustainability, MDPI, vol. 9(7), pages 1-19, July.
  29. Shaikh, Faheemullah & Ji, Qiang & Shaikh, Pervez Hameed & Mirjat, Nayyar Hussain & Uqaili, Muhammad Aslam, 2017. "Forecasting China’s natural gas demand based on optimised nonlinear grey models," Energy, Elsevier, vol. 140(P1), pages 941-951.
  30. Ankit Kumar Srivastava & Ajay Shekhar Pandey & Rajvikram Madurai Elavarasan & Umashankar Subramaniam & Saad Mekhilef & Lucian Mihet-Popa, 2021. "A Novel Hybrid Feature Selection Method for Day-Ahead Electricity Price Forecasting," Energies, MDPI, vol. 14(24), pages 1-16, December.
  31. Wang, Jianzhou & Jiang, Haiyan & Zhou, Qingping & Wu, Jie & Qin, Shanshan, 2016. "China’s natural gas production and consumption analysis based on the multicycle Hubbert model and rolling Grey model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 1149-1167.
  32. Wang, Huan & Zhang, Chao & Yao, Xiaochuang & Yun, Wenju & Ma, Jiani & Gao, Lulu & Li, Pengshan, 2022. "Scenario simulation of the tradeoff between ecological land and farmland in black soil region of Northeast China," Land Use Policy, Elsevier, vol. 114(C).
  33. Hafezi, Reza & Akhavan, AmirNaser & Pakseresht, Saeed & A. Wood, David, 2021. "Global natural gas demand to 2025: A learning scenario development model," Energy, Elsevier, vol. 224(C).
  34. An, Ning & Zhao, Weigang & Wang, Jianzhou & Shang, Duo & Zhao, Erdong, 2013. "Using multi-output feedforward neural network with empirical mode decomposition based signal filtering for electricity demand forecasting," Energy, Elsevier, vol. 49(C), pages 279-288.
  35. Reza Hafezi & Amir Naser Akhavan & Mazdak Zamani & Saeed Pakseresht & Shahaboddin Shamshirband, 2019. "Developing a Data Mining Based Model to Extract Predictor Factors in Energy Systems: Application of Global Natural Gas Demand," Energies, MDPI, vol. 12(21), pages 1-22, October.
  36. Zhao, Huiru & Guo, Sen, 2016. "An optimized grey model for annual power load forecasting," Energy, Elsevier, vol. 107(C), pages 272-286.
  37. Suganthi, L. & Samuel, Anand A., 2012. "Energy models for demand forecasting—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(2), pages 1223-1240.
  38. Wei, Sun & Yanfeng, Xu, 2017. "Research on China's energy supply and demand using an improved Grey-Markov chain model based on wavelet transform," Energy, Elsevier, vol. 118(C), pages 969-984.
  39. Minglu Ma & Min Su & Shuyu Li & Feng Jiang & Rongrong Li, 2018. "Predicting Coal Consumption in South Africa Based on Linear (Metabolic Grey Model), Nonlinear (Non-Linear Grey Model), and Combined (Metabolic Grey Model-Autoregressive Integrated Moving Average Model," Sustainability, MDPI, vol. 10(7), pages 1-15, July.
  40. Yu, Shiwei & Wei, Yi-Ming & Wang, Ke, 2012. "A PSO–GA optimal model to estimate primary energy demand of China," Energy Policy, Elsevier, vol. 42(C), pages 329-340.
  41. Liu, Chong & Wu, Wen-Ze & Xie, Wanli & Zhang, Jun, 2020. "Application of a novel fractional grey prediction model with time power term to predict the electricity consumption of India and China," Chaos, Solitons & Fractals, Elsevier, vol. 141(C).
  42. Simon Pezzutto & Gianluca Grilli & Stefano Zambotti & Stefan Dunjic, 2018. "Forecasting Electricity Market Price for End Users in EU28 until 2020—Main Factors of Influence," Energies, MDPI, vol. 11(6), pages 1-18, June.
  43. Zaher Mundher Yaseen & Minglei Fu & Chen Wang & Wan Hanna Melini Wan Mohtar & Ravinesh C. Deo & Ahmed El-shafie, 2018. "Application of the Hybrid Artificial Neural Network Coupled with Rolling Mechanism and Grey Model Algorithms for Streamflow Forecasting Over Multiple Time Horizons," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(5), pages 1883-1899, March.
  44. Juan Bógalo & Pilar Poncela & Eva Senra, 2021. "Circulant Singular Spectrum Analysis to Monitor the State of the Economy in Real Time," Mathematics, MDPI, vol. 9(11), pages 1-17, May.
  45. Fu, Yang & Zheng, Zeyu, 2020. "Volatility modeling and the asymmetric effect for China’s carbon trading pilot market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 542(C).
  46. Wang, Qiang & Song, Xiaoxing & Li, Rongrong, 2018. "A novel hybridization of nonlinear grey model and linear ARIMA residual correction for forecasting U.S. shale oil production," Energy, Elsevier, vol. 165(PB), pages 1320-1331.
  47. Lu, Hongfang & Ma, Xin & Azimi, Mohammadamin, 2020. "US natural gas consumption prediction using an improved kernel-based nonlinear extension of the Arps decline model," Energy, Elsevier, vol. 194(C).
  48. Weiwei Pan & Lirong Jian & Tao Liu, 2019. "Grey system theory trends from 1991 to 2018: a bibliometric analysis and visualization," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(3), pages 1407-1434, December.
  49. Chaturvedi, Shobhit & Rajasekar, Elangovan & Natarajan, Sukumar & McCullen, Nick, 2022. "A comparative assessment of SARIMA, LSTM RNN and Fb Prophet models to forecast total and peak monthly energy demand for India," Energy Policy, Elsevier, vol. 168(C).
  50. Yi-Chung Hu, 2017. "Predicting Foreign Tourists for the Tourism Industry Using Soft Computing-Based Grey–Markov Models," Sustainability, MDPI, vol. 9(7), pages 1-12, July.
  51. Zhang, Yunxin & Guo, Huan & Sun, Ming & Liu, Sifeng & Forrest, Jeffrey, 2023. "A novel grey Lotka–Volterra model driven by the mechanism of competition and cooperation for energy consumption forecasting," Energy, Elsevier, vol. 264(C).
  52. Qian, Wuyong & Wang, Jue, 2020. "An improved seasonal GM(1,1) model based on the HP filter for forecasting wind power generation in China," Energy, Elsevier, vol. 209(C).
  53. Sun, Xu & Sun, Wangshu & Wang, Jianzhou & Zhang, Yixin & Gao, Yining, 2016. "Using a Grey–Markov model optimized by Cuckoo search algorithm to forecast the annual foreign tourist arrivals to China," Tourism Management, Elsevier, vol. 52(C), pages 369-379.
  54. Ankit Kumar Srivastava & Ajay Shekhar Pandey & Mohamad Abou Houran & Varun Kumar & Dinesh Kumar & Saurabh Mani Tripathi & Sivasankar Gangatharan & Rajvikram Madurai Elavarasan, 2023. "A Day-Ahead Short-Term Load Forecasting Using M5P Machine Learning Algorithm along with Elitist Genetic Algorithm (EGA) and Random Forest-Based Hybrid Feature Selection," Energies, MDPI, vol. 16(2), pages 1-23, January.
  55. Wei Sun & Yujun He & Hong Chang, 2015. "Forecasting Fossil Fuel Energy Consumption for Power Generation Using QHSA-Based LSSVM Model," Energies, MDPI, vol. 8(2), pages 1-21, January.
  56. Xinyu Han & Rongrong Li, 2019. "Comparison of Forecasting Energy Consumption in East Africa Using the MGM, NMGM, MGM-ARIMA, and NMGM-ARIMA Model," Energies, MDPI, vol. 12(17), pages 1-24, August.
  57. Debnath, Kumar Biswajit & Mourshed, Monjur, 2018. "Forecasting methods in energy planning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 297-325.
  58. Zhao, Ze & Wang, Jianzhou & Zhao, Jing & Su, Zhongyue, 2012. "Using a Grey model optimized by Differential Evolution algorithm to forecast the per capita annual net income of rural households in China," Omega, Elsevier, vol. 40(5), pages 525-532.
  59. Li-Ling Peng & Guo-Feng Fan & Min-Liang Huang & Wei-Chiang Hong, 2016. "Hybridizing DEMD and Quantum PSO with SVR in Electric Load Forecasting," Energies, MDPI, vol. 9(3), pages 1-20, March.
  60. Herui Cui & Ruirui Wu & Tian Zhao, 2018. "Decomposition and Forecasting of CO 2 Emissions in China’s Power Sector Based on STIRPAT Model with Selected PLS Model and a Novel Hybrid PLS-Grey-Markov Model," Energies, MDPI, vol. 11(11), pages 1-19, November.
  61. Shao, Zhen & Chao, Fu & Yang, Shan-Lin & Zhou, Kai-Le, 2017. "A review of the decomposition methodology for extracting and identifying the fluctuation characteristics in electricity demand forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 123-136.
  62. Li, Guo-Dong & Masuda, Shiro & Nagai, Masatake, 2012. "An optimal hybrid model for atomic power generation prediction in Japan," Energy, Elsevier, vol. 45(1), pages 655-661.
  63. Hamzacebi, Coskun & Es, Huseyin Avni, 2014. "Forecasting the annual electricity consumption of Turkey using an optimized grey model," Energy, Elsevier, vol. 70(C), pages 165-171.
  64. Singh, Jaswinder, 2015. "Overview of electric power potential of surplus agricultural biomass from economic, social, environmental and technical perspective—A case study of Punjab," Renewable and Sustainable Energy Reviews, Elsevier, vol. 42(C), pages 286-297.
  65. Gholam Hossein Hasantash & Hamidreza Mostafaei & Shaghayegh Kordnoori, 2012. "Modelling the Errors of EIA's Oil Prices and Production Forecasts by the Grey Markov Model," International Journal of Economics and Financial Issues, Econjournals, vol. 2(3), pages 312-319.
  66. Wang, Shuai & Yu, Lean & Tang, Ling & Wang, Shouyang, 2011. "A novel seasonal decomposition based least squares support vector regression ensemble learning approach for hydropower consumption forecasting in China," Energy, Elsevier, vol. 36(11), pages 6542-6554.
  67. Peng Zhang & Xin Ma & Kun She, 2019. "Forecasting Japan’s Solar Energy Consumption Using a Novel Incomplete Gamma Grey Model," Sustainability, MDPI, vol. 11(21), pages 1-23, October.
  68. Peng Jiang & Jun Dong & Hui Huang, 2019. "Forecasting China’s Renewable Energy Terminal Power Consumption Based on Empirical Mode Decomposition and an Improved Extreme Learning Machine Optimized by a Bacterial Foraging Algorithm," Energies, MDPI, vol. 12(7), pages 1-24, April.
  69. Kovačič, Miha & Šarler, Božidar, 2014. "Genetic programming prediction of the natural gas consumption in a steel plant," Energy, Elsevier, vol. 66(C), pages 273-284.
  70. Liping Zhang & Li Wang & Yanling Zheng & Kai Wang & Xueliang Zhang & Yujian Zheng, 2017. "Time Prediction Models for Echinococcosis Based on Gray System Theory and Epidemic Dynamics," IJERPH, MDPI, vol. 14(3), pages 1-14, March.
  71. Zhou, Chenyu & Shen, Yun & Wu, Haixin & Wang, Jianhong, 2022. "Using fractional discrete Verhulst model to forecast Fujian's electricity consumption in China," Energy, Elsevier, vol. 255(C).
  72. Ke Yan & Xudong Wang & Yang Du & Ning Jin & Haichao Huang & Hangxia Zhou, 2018. "Multi-Step Short-Term Power Consumption Forecasting with a Hybrid Deep Learning Strategy," Energies, MDPI, vol. 11(11), pages 1-15, November.
  73. Wang, Qiang & Li, Rongrong, 2016. "Drivers for energy consumption: A comparative analysis of China and India," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 954-962.
  74. Dashti, Reza & Afsharnia, Saeed & Ghaderi, Farid, 2010. "AGA (Asset Governance Assessment) for analyzing affect of subsidy on MC (Marginal Cost) in electricity distribution sector," Energy, Elsevier, vol. 35(12), pages 4996-5007.
  75. Yongwei, Cheng & Dong, Mu & Huanyu, Ren & Tijun, Fan & Jianbang, Du, 2020. "Using a temporal input-output approach to analyze the ripple effect of China’s energy consumption," Energy, Elsevier, vol. 211(C).
  76. Wang, Qiang & Song, Xiaoxin, 2019. "Forecasting China's oil consumption: A comparison of novel nonlinear-dynamic grey model (GM), linear GM, nonlinear GM and metabolism GM," Energy, Elsevier, vol. 183(C), pages 160-171.
  77. Guo-Feng Fan & Shan Qing & Hua Wang & Wei-Chiang Hong & Hong-Juan Li, 2013. "Support Vector Regression Model Based on Empirical Mode Decomposition and Auto Regression for Electric Load Forecasting," Energies, MDPI, vol. 6(4), pages 1-15, April.
  78. Jia, Zong-qian & Zhou, Zhi-fang & Zhang, Hong-jie & Li, Bo & Zhang, You-xian, 2020. "Forecast of coal consumption in Gansu Province based on Grey-Markov chain model," Energy, Elsevier, vol. 199(C).
  79. Wei Jiang & Jianzhong Zhou & Yanhe Xu & Jie Liu & Yahui Shan, 2019. "Multistep Degradation Tendency Prediction for Aircraft Engines Based on CEEMDAN Permutation Entropy and Improved Grey–Markov Model," Complexity, Hindawi, vol. 2019, pages 1-18, October.
  80. Aneeque A. Mir & Mohammed Alghassab & Kafait Ullah & Zafar A. Khan & Yuehong Lu & Muhammad Imran, 2020. "A Review of Electricity Demand Forecasting in Low and Middle Income Countries: The Demand Determinants and Horizons," Sustainability, MDPI, vol. 12(15), pages 1-35, July.
  81. Pao, Hsiao-Tien & Fu, Hsin-Chia & Tseng, Cheng-Lung, 2012. "Forecasting of CO2 emissions, energy consumption and economic growth in China using an improved grey model," Energy, Elsevier, vol. 40(1), pages 400-409.
  82. Yu, Shi-wei & Zhu, Ke-jun, 2012. "A hybrid procedure for energy demand forecasting in China," Energy, Elsevier, vol. 37(1), pages 396-404.
  83. Ene, Seval & Öztürk, Nursel, 2017. "Grey modelling based forecasting system for return flow of end-of-life vehicles," Technological Forecasting and Social Change, Elsevier, vol. 115(C), pages 155-166.
  84. Rehman, Aniqa & Zhu, Jun-Jie & Segovia, Javier & Anderson, Paul R., 2022. "Assessment of deep learning and classical statistical methods on forecasting hourly natural gas demand at multiple sites in Spain," Energy, Elsevier, vol. 244(PA).
  85. Gholami, M. & Barbaresi, A. & Torreggiani, D. & Tassinari, P., 2020. "Upscaling of spatial energy planning, phases, methods, and techniques: A systematic review through meta-analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
  86. Voyant, Cyril & Notton, Gilles & Darras, Christophe & Fouilloy, Alexis & Motte, Fabrice, 2017. "Uncertainties in global radiation time series forecasting using machine learning: The multilayer perceptron case," Energy, Elsevier, vol. 125(C), pages 248-257.
  87. Sinha, Avik, 2017. "Examination of oil import-exchange nexus for India after currency crisis," MPRA Paper 100359, University Library of Munich, Germany, revised 2017.
  88. Tyralis, Hristos & Karakatsanis, Georgios & Tzouka, Katerina & Mamassis, Nikos, 2017. "Exploratory data analysis of the electrical energy demand in the time domain in Greece," Energy, Elsevier, vol. 134(C), pages 902-918.
  89. Zheng-Xin Wang, 2015. "A Predictive Analysis of Clean Energy Consumption, Economic Growth and Environmental Regulation in China Using an Optimized Grey Dynamic Model," Computational Economics, Springer;Society for Computational Economics, vol. 46(3), pages 437-453, October.
  90. Huang, Liqiao & Liao, Qi & Qiu, Rui & Liang, Yongtu & Long, Yin, 2021. "Prediction-based analysis on power consumption gap under long-term emergency: A case in China under COVID-19," Applied Energy, Elsevier, vol. 283(C).
  91. Weibo Zhao & Dongxiao Niu, 2017. "Prediction of CO 2 Emission in China’s Power Generation Industry with Gauss Optimized Cuckoo Search Algorithm and Wavelet Neural Network Based on STIRPAT model with Ridge Regression," Sustainability, MDPI, vol. 9(12), pages 1-15, December.
  92. Manuel Jaramillo & Diego Carrión, 2022. "An Adaptive Strategy for Medium-Term Electricity Consumption Forecasting for Highly Unpredictable Scenarios: Case Study Quito, Ecuador during the Two First Years of COVID-19," Energies, MDPI, vol. 15(22), pages 1-19, November.
  93. de Oliveira Ventura, Lucas & Melo, Joel D. & Padilha-Feltrin, Antonio & Fernández-Gutiérrez, Juan Pablo & Sánchez Zuleta, Carmen C. & Piedrahita Escobar, Carlos César, 2020. "A new way for comparing solutions to non-technical electricity losses in South America," Utilities Policy, Elsevier, vol. 67(C).
  94. Voyant, Cyril & Soubdhan, Ted & Lauret, Philippe & David, Mathieu & Muselli, Marc, 2015. "Statistical parameters as a means to a priori assess the accuracy of solar forecasting models," Energy, Elsevier, vol. 90(P1), pages 671-679.
  95. Garg, Amit & Naswa, Prakriti & Shukla, P.R., 2015. "Energy infrastructure in India: Profile and risks under climate change," Energy Policy, Elsevier, vol. 81(C), pages 226-238.
  96. Majumder, Rajarshi & Ghosh, Subhadip & Chatterjee, Bidisha, 2022. "Energy infrastructure in India: challenges and opportunities," MPRA Paper 120106, University Library of Munich, Germany.
  97. Rajesh, R. & Agariya, Arun Kumar & Rajendran, Chandrasekharan, 2021. "Predicting resilience in retailing using grey theory and moving probability based Markov models," Journal of Retailing and Consumer Services, Elsevier, vol. 62(C).
  98. Dima, Bogdan & Dima, Ştefana Maria, 2017. "Energy consumption synchronization between Europe, United States and Japan: A spectral analysis assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 1261-1271.
  99. Yeqi An & Yulin Zhou & Rongrong Li, 2019. "Forecasting India’s Electricity Demand Using a Range of Probabilistic Methods," Energies, MDPI, vol. 12(13), pages 1-24, July.
  100. Xi Zhang & Zheng Li & Linwei Ma & Chinhao Chong & Weidou Ni, 2019. "Forecasting the Energy Embodied in Construction Services Based on a Combination of Static and Dynamic Hybrid Input-Output Models," Energies, MDPI, vol. 12(2), pages 1-26, January.
  101. Che, Jinxing & Wang, Jianzhou & Wang, Guangfu, 2012. "An adaptive fuzzy combination model based on self-organizing map and support vector regression for electric load forecasting," Energy, Elsevier, vol. 37(1), pages 657-664.
  102. Laha, Priyanka & Chakraborty, Basab, 2017. "Energy model – A tool for preventing energy dysfunction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 95-114.
  103. Liu, Xiuli & Moreno, Blanca & García, Ana Salomé, 2016. "A grey neural network and input-output combined forecasting model. Primary energy consumption forecasts in Spanish economic sectors," Energy, Elsevier, vol. 115(P1), pages 1042-1054.
  104. Wang, Qiang & Jiang, Feng, 2019. "Integrating linear and nonlinear forecasting techniques based on grey theory and artificial intelligence to forecast shale gas monthly production in Pennsylvania and Texas of the United States," Energy, Elsevier, vol. 178(C), pages 781-803.
  105. Suat Ozturk & Feride Ozturk, 2018. "Forecasting Energy Consumption of Turkey by Arima Model," Journal of Asian Scientific Research, Asian Economic and Social Society, vol. 8(2), pages 52-60, February.
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