Electric load forecasting: Literature survey and classification of methods

By Hesham K. Alfares, Mohammad Nazeeruddin. International Journal of Systems Science, 2002

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Transmission Expansion Planning From Past to Future (2006, methodology): “nature of the methodologies, nine categories can further be classified [104]: multiple regression [105], exponential smoothing [106], iterative reweighted least-squares [107],”
Forecasting for demand response in smart grids: An analysis on use of anthropologic and structural data and short term multiple loads forecasting (2012, background): “Forecasting for larger loads such as city or the entire grid has been achieved with relatively high accuracy [1].”
Tackling Climate Change with Machine Learning (2019, methodology): “[48] Hesham K Alfares and Mohammad Nazeeruddin.”
A Review of Electricity Demand Forecasting in Low and Middle Income Countries: The Demand Determinants and Horizons (2020, background): “These similarities in the data can be drawn with the help of various first and second order differences in the data as represented below [9].”
Effects of data cleansing on load prediction algorithms (2013, background): “We show that this is incorrect at the diurnal level, due to the characteristic pattern of power consumption, with two peak loads during daytime and a nighttime trough.”
Modeling and Forecasting Electricity Loads and Prices: A Statistical Approach (2006, methodology): “6 FURTHER READING For surveys of load forecasting techniques see Alfares and Nazeeruddin (2002), Bunn (2000), Bunn and Farmer (1985), Dobrzańska (2002), Feinberg and Genethliou (2005), Liu et al.”