Using weather ensemble predictions in electricity demand forecasting

By James W. Taylor, Roberto Buizza. 2003

309 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/a396f2e877bcb9fbc13acceb6c36648539f32b67

How researchers see this paper

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

In Electrical and Electronic Engineering

In Global and Planetary Change

Citations in the corpus

Hourly electricity demand forecasting using Fourier analysis with feedback (2020, methodology): “[18] the author studies England and Wales electricity demand data in the half-hour period data series and applies the Holt-Winters method for different periods with AR model.”
Current methods and advances in forecasting of wind power generation (2012, unknown): “NWP is sensitive to initial conditions and to overcome this ensemble forecasting is used [7].”
A comparison of univariate methods for forecasting electricity demand up to a day ahead (2006, background): “Weather related variation is certainly critical in predicting electricity demand for lead times beyond a day-ahead (Chow & Leung, 1996; Taylor & Buizza, 2003 ).”
Correcting Forecasts with Multifactor Neural Attention (2016, background): “This implies, for example, that the model is interpretable both on the level of providing a prediction for the expected downturn in sales because of the predicted heat wave next week, and even further down to the specifics of the expectation based on the temperature that Wednesday.”
An evaluation of methods for very short-term load forecasting using minute-by-minute British data (2008, methodology): “The structure of the models is described by Taylor and Buizza (2003).”
VOLATILITY AND CORRELATION FORECASTING (2006, background): “In a related context, Taylor and Buizza (2003) use weather forecast uncertainty to model electricity demand uncertainty.”
Bridging the gap between weather and seasonal forecasting: intraseasonal forecasting for Australia (2011, background): “…for a range of sectors of society, such as agriculture (e.g. Hammer et al., 2000; Meinke and Stone, 2005), energy (e.g. Roulston et al., 2003; Taylor and Buizza, 2003), water management (e.g. Sankarasubramanian et al., 2009) and the financial markets and insurance (e.g. Zeng, 2000; Jewson…”
Short-term electricity demand forecasting using double seasonal exponential smoothing (2003, background): “operator and additional difference operators in expression ( 1 ).”
A comparison of temperature density forecasts from GARCH and atmospheric models (2004, methodology): “5 Method Q5 - Ensemble Quantiles Debiased Using TVP Quantile Regression The use of OLS regression to debias a point forecast was proposed by Theil (1971). In the context of judgmental point forecasting, Goodwin (1997) describes an approach that allows the debiasing regression parameters to vary over”
The economic value of ensemble forecasts as a tool for risk assessment: From days to decades (2002, background): “6 shows a box-and-whiskers plot for mean England and Wales electricity demand (Taylor and Buizza 2002).”