Forecasting the short-term demand for electricity: Do neural networks stand a better chance?

By Georges A. Darbellay, M. Sláma. 2000

358 citations recorded by the source corpus.

Keywords: LTS1 Reference LTS-ARTICLE-2000-009 Record created on 2006-06-14, modified on 2016-08-08

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

View Paper: https://www.semanticscholar.org/paper/f080c4650c2acbc990e0ebddaf3733e11e8d582b

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In Artificial Intelligence

In Electrical and Electronic Engineering

In Management Science and Operations Research

Citations in the corpus

Bayesian Modeling and Forecasting of Intraday Electricity Load (2003, methodology): “Although various ANN solutions are available (see Darbellay and Slama 2000 for a recent summary), one of the most popular involvesusing back-propagation algorithms to select parsimonious radial basis function representations of the nonlinear periodic daily and weekly effects (Hsu and Yang 1995;…”
Modeling and forecasting short-term electricity load: A comparison of methods with an application to Brazilian data (2008, methodology): “A similar result has been reported in the literature by Darbellay and Slama (2000).”
Triple seasonal methods for short-term electricity demand forecasting (2010, background): “Darbellay and Slama (2000) and Taylor et al. (2006) build univariate neural network models to model the intraday and intraweek cycles in series of intraday load data.”
Empirical Mode Decomposition based ensemble deep learning for load demand time series forecasting (2017, background): “In the recent years, with the rapid development of , artiﬁcial neural network (ANN) [9], fuzzy comprehensive and support vector machine (SVM) methods [11] have been short-term load forecasting.”
Comparing linear and nonlinear forecasts for Taiwan's electricity consumption (2006, background): “Darbellay and Slama [8] compared the predictions of nonlinear artificial neural networks (ANNs) with linear ARIMA models for Czech electric consumption.”
25 Years of IIF Time Series Forecasting: A Selective Review (2005, background): “The artificial neural network (ANN) can be useful for nonlinear processes that have an unknown functional relationship and as a result are difficult to fit (Darbellay & Slama, 2000).”
An evaluation of methods for very short-term load forecasting using minute-by-minute British data (2008, methodology): “These seasonal features are typical of series of intraday load (see, for example, Darbellay and Slama, 2000; Taylor et al., 2006).”
A comparison of univariate methods for forecasting electricity demand up to a day ahead (2006, methodology): “Darbellay and Slama (2000) present a neural network, built only from historical hourly load, which matched an ARIMA model for prediction from an hour ahead to 36 hours ahead. Several studies focused on short-term load forecasting and took one of two approaches. In the first, a neural network is cons”
25 years of time series forecasting (2006, background): “The artificial neural network (ANN) can be useful for nonlinear processes that have an unknown functional relationship and as a result are difficult to fit (Darbellay & Slama, 2000).”