“…Because the coefficients in the model can be expected to change throughout the day, the data set is divided into 24 separate time series, and separate regressions are estimated for each hour. Separate treatment of each hour is common in the literature (e.g., Ramanathan et al, 1997;Fay et al, 2003;Longstaff and Wang, 2004), allows us to finesse the issue of controlling for intraday price fluctuations, and allows us to examine how the regression coefficients change during the day. Tables 2 through 4 display summary statistics for electricity spot and forward prices, and forward premia for each hour.…”
“…Because the coefficients in the model can be expected to change throughout the day, the data set is divided into 24 separate time series, and separate regressions are estimated for each hour. Separate treatment of each hour is common in the literature (e.g., Ramanathan et al, 1997;Fay et al, 2003;Longstaff and Wang, 2004), allows us to finesse the issue of controlling for intraday price fluctuations, and allows us to examine how the regression coefficients change during the day. Tables 2 through 4 display summary statistics for electricity spot and forward prices, and forward premia for each hour.…”
“…In order to deal with the everyday process of planning, scheduling and unit-commitment, the need for accurate short-term forecasts has led to the development of a wide range of models based on different techniques. Some interesting examples are related to periodic time series (Espinoza et al 2005), traditional time series analysis (Ramanathan et al 1997), neural networks applications (Steinherz et al 2001), and Support Vector Machines (Chen et al 2002). The main goal is to generate a model that can capture all the dynamics and interactions between possible explanatory variables for the load.…”
Section: Description and Objectivementioning
confidence: 99%
“…As an application to an interesting real-life problem, we study the case of the short-term load forecasting problem, which is an important area of quantitative research (Ramanathan et al 1997;Lotufo and Minussi 1999;Fay et al 2003;Bunn 2000;Espinoza et al 2005). Within this context, the goal of the modelling task is to generate a model that can capture all the dynamics and interaction between possible explanatory variables to explain the behavior of the load in an hourly scale.…”
Least squares support vector machines, Nyström approximation, Fixed-size LS-SVM, Kernel based methods, Sparseness, Primal space regression, Load forecasting, Time series,
“…Organizing forecasting competitions for the problems related to electric power system is a new development [24,25] and this is the first time that a solar energy prediction contest has been organized.…”
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