Load forecasting, dynamic pricing and DSM in smart grid: A review

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Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review (2020, background): “nature and have been shown to provide less accurate results in load forecasting [278].”
A Review of Electricity Demand Forecasting in Low and Middle Income Countries: The Demand Determinants and Horizons (2020, methodology): “Similarly, some review studies have focused on application areas such as smart grids and microgrids as well [11,13,14].”
Deep belief network based electricity load forecasting: An analysis of Macedonian case (2016, methodology): “As indicated in [33], the models that are used for load forecasting may be categorized into two groups: statistical and arti ﬁ cial intelligence based models.”