Long Term Probabilistic Load Forecasting and Normalization With Hourly Information

By Tao Hong, Jason Wilson, Jingrui Xie. IEEE Transactions on Smart Grid, 2014

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Probabilistic Load Forecasting via Quantile Regression Averaging on Sister Forecasts (2017, methodology): “one, named as Vanilla, is a scenario-based probabilistic load forecast generated using a similar methodology as discussed in [20].”
Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges (2018, methodology): “Various temperature scenario generation methods have been proposed in the literature, such as direct usage of the previous years of hourly temperatures with the dates fixed [128], shifting the historical temperatures by a few days”
Using Smart Meter Data to Improve the Accuracy of Intraday Load Forecasting Considering Customer Behavior Similarities (2015, background): “Load forecasting can be categorized by very short-term load forecasting (VSTLF), short-term load forecasting (STLF), medium-term load forecasting, and long-term load forecasting (LTLF), among which the forecasting horizon cut-offs are one day, two weeks, and three years, respectively [5], [7]–[9].”
Deep Learning for solar power forecasting — An approach using AutoEncoder and LSTM Neural Networks (2016, background): “The area of short-term forecasting is covered in [3], [5], mid-term forecasting is described in [6], long-term forecasting techniques are described in [7].”