Density Forecasting for Long-Term Peak Electricity Demand

By Rob J Hyndman, Shu Fan. IEEE Transactions on Power Systems, 2010

386 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/db027220cb90d2fc86da5ad06b4f4f0123e1fbfa

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In Electrical and Electronic Engineering

In Management Science and Operations Research

Citations in the corpus

The price elasticity of electricity demand in South Australia (2011, methodology): “The analysis is based on the demand models established in Hyndman and Fan (2008) on forecasting the long-term peak electricity demand for South Australia.”
Long Term Probabilistic Load Forecasting and Normalization With Hourly Information (2014, methodology): “A peak load forecasting methodology implemented at Australian Energy Market Operator (AEMO) has been reported in [11].”
Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches † (2018, background): “Conversely, medium and long-term forecasting was not sufficiently studied despite their crucial inputs for the power system planning and budget allocation [2].”
Probabilistic Load Forecasting via Quantile Regression Averaging on Sister Forecasts (2017, methodology): “sion analysis is a load forecasting technique that is transparent for reproduction and has been widely used in the industry [1], [6], [19], [25].”
Energy Forecasting: A Review and Outlook (2020, methodology): “Some choose to go a step further to develop packages in free software environment, such as the R project [85].”
Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges (2018, background): “to create additional scenarios [129], and bootstrapping the historical temperatures [130].”
Functional time series approach for forecasting very short-term electricity demand (2013, methodology): “The techniques proposed in this paper differ from those of Hyndman and Fan [21], Antoch et al.”
Global Energy Forecasting Competition 2012 (2014, background): “…probabilistic forecasts in the form of predictive densities or scenarios as inputs, such as annual peak demand forecasting for system planning (Hyndman & Fan, 2009), systems reserve quantification (Matos & Bessa, 2011), unit commitment (Tuohy, Meibom, Denny, & O’Malley, 2009), and trading of…”