Free access to multivariate dynamic regression article

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  • Author: Helio S. Migon, Larissa C. Alves and Statistics Views
  • Date: 20 January 2014

Each week, we select an article hot off the press and provide free access for a limited period. This week's is from Applied Stochastic Models in Business and Industry.

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Multivariate dynamic regression: modeling and forecasting for intraday electricity load
Helio S. Migon and Larissa C. Alves

Applied Stochastic Models in Business and Industry, Vol. 29 (6), pp. 579-598.
DOI: 10.1002/asmb.1990

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This paper introduces electricity load curve models for short-term forecasting purposes. A broad class of multivariate dynamic regression models is proposed to model hourly electricity load. Alternative forecasting models, special cases of our general model, include separate time series regressions for each hour and week day. All the models developed include components that represent trends, seasons at different levels (yearly, weekly, etc.), dummies to take into account weekends/holidays and other special days, and short-term dynamics and weather regression effects, discussing the necessity of nonlinx ear functions for cooling effects. Our developments explore the facilities of dynamic linear models such as the use of discount factors, subjective intervention, variance learning and smoothing/filtering. The elicitation of the load curve is considered in the context of subjective intervention analysis, which is especially useful to take into account the adjustments for daylight savings time. The theme of combination of probabilistic forecasting is also briefly addressed.

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