Energy models for demand forecasting—A review

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Forecasting energy consumption using ensemble ARIMA-ANFIS hybrid algorithm (2016, methodology): “Suganthi and Samuel [4] have surveyed many different models in the field of energy forecasting and introduced two types of models including: 1-Traditional forecasting models such as: time series, regression, econometrics models, and ARIMA, 2-Soft computing models such as: fuzzy logic, genetic…”
A roadmap for China to peak carbon dioxide emissions and achieve a 20% share of non-fossil fuels in primary energy by 2030 (2019, methodology): “Starting in the early 2000s, various models have been used to forecast China’s energy consumption and CO2 emissions, including: (1) econometric models, autoregressive integrated moving average (ARIMA) models, and Grey prediction models (GM) [24,25], (2) top-down models such as input-output models an”
A variance inflation factor and backward elimination based robust regression model for forecasting monthly electricity demand using climatic variables (2015, background): “31 An extensive literature on forecasting models and strategies has been reviewed in [2].”