2006
DOI: 10.1016/j.ijforecast.2005.06.006
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A comparison of univariate methods for forecasting electricity demand up to a day ahead

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Cited by 402 publications
(258 citation statements)
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“…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. As inputs, they use load at various lags.…”
Section: Artificial Neural Networkmentioning
confidence: 99%
“…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. As inputs, they use load at various lags.…”
Section: Artificial Neural Networkmentioning
confidence: 99%
“…Only few research works have considered price forecasting in the Finnish day-ahead energy market and it was not possible to find price forecast methods considering the above-mentioned test period for price forecast. Therefore, the overall accuracy of the proposed method is compared with some of the most popular price forecast techniques applied for case studies of energy markets of other countries: seasonal ARIMA [5,44,50]; WT+ARIMA [26,28]; NN [6,50]; WT + NN [11]. Additionally, WT + ARIMA + NN, which has not been found in the literature is among competitive techniques.…”
Section: Numerical Resultsmentioning
confidence: 99%
“…The class of short-term forecasting techniques include models with a short future horizons. A one-step forward future horizon is adequate for short-term time series forecasting [1] delivering methods which are widely used in finance [3][4][5]; electricity price/load, solar and wind energy forecasting problem [6,7]; passenger demand [8] and many others.…”
Section: Introductionmentioning
confidence: 99%