Using Smart Meter Data to Improve the Accuracy of Intraday Load Forecasting Considering Customer Behavior Similarities

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Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges (2018, methodology): “Two datasets including the Irish CER residential dataset and another dataset from New York were used to build the case study in [31].”
Analysis and Clustering of Residential Customers Energy Behavioral Demand Using Smart Meter Data (2016, background): “tion of customer types can cause network constraint violations; improving forecast methodologies [22], [23]; and compare different groups and trends in behavior.”
Deep Learning for Household Load Forecasting—A Novel Pooling Deep RNN (2018, background): “lar customers, days or weather [6], [16]–[19] in the hope of reducing the variance of uncertainty within each cluster.”
Short-Term Residential Load Forecasting Based on LSTM Recurrent Neural Network (2019, methodology): “[17] also adopted the similar methodology that bases on smart meter data clustering and neural network to make intraday load forecasting at the system level.”
Review of low voltage load forecasting: Methods, applications, and recommendations (2021, methodology): “In [182], the authors also use k-means clustering of smart-meter data to produce aggregate forecasts.”