Short-term electricity demand forecasting using double seasonal exponential smoothing

By James W. Taylor. Journal of the Operational Research Society, 2003

771 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/44c00b621735911ce7a889f9ce0267393973b37d

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In Electrical and Electronic Engineering

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Citations in the corpus

Short-Term Load Forecasting Methods: An Evaluation Based on European Data (2007, methodology): “The seasonal Holt-Winters method has been adapted in order to accommodate the two seasonal cycles in electricity demand series [18].”
Forecasting intraday time series with multiple seasonal cycles using parsimonious seasonal exponential smoothing (2009, background): “Keywords: Forecasting; Time Series; Exponential smoothing; Electricity load; Call centre arrivals”
Holt–Winters Forecasting: An Alternative Formulation Applied to UK Air Passenger Data (2007, methodology): “Also, some recent papers have introduced modified versions of Holt-Winters methods that outperform the traditional ones in specific applications (Taylor, 2003).”
A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting (2020, positive): “Moreover, non-seasonal (yearly and daily data), single-seasonal (monthly, quarterly, and weekly data) or double-seasonal (hourly data) models (Taylor, 2003) were used, depending on the frequency of the data.”
Probabilistic forecasts of the magnitude and timing of peak electricity demand (2005, background): “S. Bouwman and G. Bloemhof are with the KEMA Transmission and Distribution Consulting, 6800 ET Arnhem, The Netherlands (e-mail: Sonja.Bouwman@kema.com; Gabriel.Bloemhof@kema.com).”
A comparison of univariate methods for forecasting electricity demand up to a day ahead (2006, methodology): “We therefore followed a similar approach to that of Taylor (2003) for his exponential smoothing method, and adjusted the model by adding an AR model of the error process.”
An evaluation of methods for very short-term load forecasting using minute-by-minute British data (2008, methodology): “The seasonal Holt-Winters exponential smoothing method has been adapted in order to accommodate the two seasonal cycles in electricity load series (Taylor, 2003).”
Exponentially weighted methods for forecasting intraday time series with multiple seasonal cycles (2010, methodology): “Inclusion of this term substantially improves forecast accuracy, and so is integral to the method (Taylor, 2003).”
Short-Term Load Forecasting With Exponentially Weighted Methods (2012, methodology): “The HWT method, introduced in [12], extends Holt-Winters exponential smoothing with the aim of modeling the intraday and intraweek cycles in intraday data.”
Forecasting Time Series With Complex Seasonal Patterns Using Exponential Smoothing (2011, methodology): “Taylor (2003) extended the linear version of the Holt–Winters method to incorporate a second seasonal component as follows: yt = t−1 + bt−1 + s(1)t + s(2)t + dt, (1a) t = t−1 + bt−1 + αdt, (1b) bt = bt−1 + βdt, (1c) s(1)t = s(1)t−m1 + γ1dt, (1d) s(2)t = s(2)t−m2 + γ2dt, (1e) where m1 and m2 are the…”
Electricity load demand forecasting using exponential smoothing methods (2013, methodology): “The local s-period seasonalt t-1 t index, I , is estimated by smoothing the ratio of observedt value, X , to local level, S .t t In this paper, we produced forecasts for two different standard Holt-Winters methods, which are Holt-Winters for Intraday Seasonality (using a 48-period seasonal cycle; re”
TUBE: time-dependent pricing for mobile data (2012, methodology): “We use a variation of the traditional TES equations [15, 23] to incorporate periodicity over one day and over one week.”
A Review of Electricity Demand Forecasting in Low and Middle Income Countries: The Demand Determinants and Horizons (2020, methodology): “While dealing with multiple seasonalities in the data, the Holt–Winters exponential smoothing technique can be formulated to adapt to two seasonalities [87].”
Density Forecasting for Long-Term Peak Electricity Demand (2010, neutral): “…including linear regression and time series methods (Weron, 2006; Huang 3 Density forecasting for long-term peak electricity demand and Shih, 2003; Taylor, 2003; Al-Hamadi and Soliman, 2005; McSharry et al., 2005a); artificial intelligent approaches including artificial neural networks and fuzzy…”