An Hourly Periodic State Space Model for Modelling French National Electricity Load

By Virginie Dordonnat, S. J. Koopman, Marius Ooms, Alain Dessertaine, Jérôme Collet. 2008

160 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/0b8b6a02fe5942e24d2ce6ea1e7d3a626a5ca34f

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In Electrical and Electronic Engineering

In Management Science and Operations Research

In Ocean Engineering

James W. Taylor

Citations in the corpus

Effects of data cleansing on load prediction algorithms (2013, background): “In previous work, this has been shown to be an acceptable simplification [22].”
Industrial electricity demand for Turkey: A structural time series analysis (2011, methodology): “Doornat et al. (2008) investigated French hourly electricity load by employing a multivariate periodic state space model that included a stochastic trend and concluded that their model gives satisfactory prediction results for one, two and three day ahead but some improvements can be made for longer”
Short-Term Forecasting of Anomalous Load Using Rule-Based Triple Seasonal Methods (2013, result): “Overall, the load on a PD tends to be lowered further if it is a B-PD, and hence we treat them as being separate from NB-PD, as also done by [8].”
An evaluation of methods for very short-term load forecasting using minute-by-minute British data (2008, background): “Cottet and Smith (2003) and Dordonnat et al. (2008) develop this approach by accommodating the dependencies between the hourly loads and estimating simultaneously the equations for each hour of the day.”
A refined parametric model for short term load forecasting (2014, methodology): “(2002), Dordonnat et al. (2008) and Pierrot & Goude (2011). Another alternative to MWS is used in Fan & Hyndman (2012). As in Pang (2012), we find that MWS produces better forecasts than the virtual weather station approach.”
Can Google Data Help Predict French Youth Unemployment (2012, background): “This representation, inspired by Dordonnat et al. (2008), is flexible and allows us to capture breaks in the seasonality because weights (aj,t, bj,t) are random walks:”