Probabilistic forecasts of the magnitude and timing of peak electricity demand

By Patrick E. McSharry, S. Bouwman, Gabriël Bloemhof. IEEE Transactions on Power Systems, 2005

167 citations recorded by the source corpus.

Subfields: Atmospheric Science, Electrical and Electronic Engineering, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/f29167dc67f90e9e3ad3529ced53fef49fa917b1

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atmospheric Science

1. Rob J Hyndman
2. Shu Fan
3. Souhaib Ben Taieb
4. Tao Hong
5. James W. Taylor
6. Patrick E. McSharry
7. Jakub Nowotarski
8. Rafal Weron

In Electrical and Electronic Engineering

1. Rob J Hyndman
2. Rafal Weron
3. Jakub Nowotarski
4. Tao Hong
5. Shu Fan
6. Bidong Liu
7. James W. Taylor
8. Souhaib Ben Taieb

In Global and Planetary Change

1. Rob J Hyndman
2. Shu Fan
3. Souhaib Ben Taieb
4. Rafal Weron
5. Tao Hong
6. James W. Taylor
7. Patrick E. McSharry
8. Jakub Nowotarski

Citations in the corpus

• Short-Term Load Forecasting Based on a Semi-Parametric Additive Model (2012, background): “Although substantial attention has been paid to short-term load forecasting, a few researchers have proposed interval and probabilistic approaches for long-term forecast horizons; notably [29], [17], and [6].”
• Probabilistic Load Forecasting via Quantile Regression Averaging on Sister Forecasts (2017, methodology): “[18] proposed an approach to one-year ahead annual peak demand forecasting using daily peak”
• A gradient boosting approach to the Kaggle load forecasting competition (2014, methodology): “This idea has subsequently been used by Fay et al. (2003); McSharry et al. (2005); Fan and Chen (2006) and Fan and Hyndman (2012).”
• Modeling and Forecasting Medium-Term Electricity Consumption Using Component Estimation Technique (2020, background): “For example, the medium-term electricity demand forecast is required for electric power system operation and scheduling [7–9], whereas the long-term electricity demand forecasting is crucial for capacity scheduling and maintenance planning [10].”
• Short-Term Load Forecasting Methods: An Evaluation Based on European Data (2007, background): “Long-term forecasts of the peak electricity demand are needed for capacity planning and maintenance scheduling [1].”
• Density Forecasting for Long-Term Peak Electricity Demand (2010, methodology): “This is similar to the models developed by others including McSharry et al. (2005b) and Ramanathan et al. (1997), but we propose 9 Density forecasting for long-term peak electricity demand a large number of modifications and extensions to make the models more general, robust and effective.”