Short-Term Load Forecasting Methods: An Evaluation Based on European Data

By James W. Taylor, Patrick E. McSharry. IEEE Transactions on Power Systems, 2007

599 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/5aeac318b22f372d589ecb9be1089f2d8c0bb030

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Citations in the corpus

Short-Term Load Forecasting With Exponentially Weighted Methods (2012, methodology): “2161780 exponential smoothing method [4], [5].”
Short-Term Load Forecasting Based on a Semi-Parametric Additive Model (2012, methodology): “models are widely adopted for the load forecasting problem, which include linear regression models, stochastic process models, exponential smoothing, and ARIMA models [2]–[7].”
Forecasting Electricity Spot Prices Accounting for Wind Power Predictions (2013, background): “Me anwhile the supply function is discontinuous, convex and stee ply increasing at the high production end [9], [13], [14].”
Short term electricity demand forecasting using partially linear additive quantile regression with an application to the unit commitment problem (2018, background): “Another study that examines meteorological effects is that of Taylor and MacSharry [42], in which the seasonality patterns and their effects were studied from European countries.”
An Hourly Periodic State Space Model for Modelling French National Electricity Load (2008, methodology): “Following Taylor & McSharry (2007) we use a simple univariate weekly random walk model as a benchmark in our forecast evaluation.”
Short-Term Load and Wind Power Forecasting Using Neural Network-Based Prediction Intervals (2014, methodology): “In [1], different forecasting methods including ARIMA modeling, periodic AR modeling, double seasonality of Holt-Winters ES, and principal component analysis are considered for STLF. Electricity demands from 10 European countries are used as case studies to compare these methods.”
Triple seasonal methods for short-term electricity demand forecasting (2010, methodology): “In two recent empirical studies, several univariate statistical methods were considered that aim to accommodate both of these seasonal cycles (Taylor and McSharry, 2007, Taylor, 2008).”