Probabilistic electric load forecasting: A tutorial review

By Tao Hong, Shu Fan. 2016

1,260 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/154102a5a5e3a96fcd37bd5e672137126f323490

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In Electrical and Electronic Engineering

In Management Science and Operations Research

Citations in the corpus

Short term electricity demand forecasting using partially linear additive quantile regression with an application to the unit commitment problem (2018, methodology): “A study that applied one of the variable selection methods mentioned above is that of Fan and Hyndman [10].”
Wind turbine power curve modeling using maximum likelihood estimation method (2019, background): “Day-ahead load forecasts which have already been developed for the conventional grid systems [14] are required to plan maintenance of wind farms and to schedule grid maintenance and energy storage operations [15].”
Probabilistic Load Forecasting via Quantile Regression Averaging on Sister Forecasts (2017, background): “Hyndman and Fan [19] developed density peak load forecasts for the Australian energy market operator.”
On the importance of the long-term seasonal component in day-ahead electricity price forecasting (2016, background): “With the introduction of smart grids and renewable integration requirements, probabilistic load and price forecasting has become more important to energy systems planning and operations (Hong and Fan, 2016).”
A Review of Electricity Demand Forecasting in Low and Middle Income Countries: The Demand Determinants and Horizons (2020, methodology): “This adds to the major drawbacks of a regression analysis [2].”
Deep Learning for Household Load Forecasting—A Novel Pooling Deep RNN (2018, background): “forecasting at aggregated level already exist [14-26, 40-42].”
Tackling Climate Change with Machine Learning (2019, methodology): “[46] Tao Hong and Shu Fan.”
Recent advances in electricity price forecasting: A review of probabilistic forecasting (2016, background): “For this reason, in their review on probabilistic load forecasting, Hong and Fan [5] use the term ‘unconditional coverage’ as a substitute for ‘reliability’.”
Review of low voltage load forecasting: Methods, applications, and recommendations (2021, background): “Hong and Fan [100] provide a tutorial review of probabilistic load forecasting.”
Residential probabilistic load forecasting: A method using Gaussian process designed for electric load data (2018, methodology): “Electric load forecasting is vital for several businesses that are dealing with the operation, trading, and planning of energy, for example, banks, electric utilities, and insurance companies [1].”
Forecasting: theory and practice (2020, methodology): “A winning team of the Global Energy Forecasting Competition 2014 (GEFCom2014) also incorporated a model-based approach for anomaly detection and data cleansing (Xie and Hong, 2016).”