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Multivariate dynamic regression: modeling and forecasting for intraday electricity load
Author(s) -
Migon Helio S.,
Alves Larissa C.
Publication year - 2013
Publication title -
applied stochastic models in business and industry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.413
H-Index - 40
eISSN - 1526-4025
pISSN - 1524-1904
DOI - 10.1002/asmb.1990
Subject(s) - probabilistic forecasting , multivariate statistics , econometrics , variance (accounting) , context (archaeology) , computer science , term (time) , electricity , exponential smoothing , regression analysis , probabilistic logic , statistics , economics , mathematics , machine learning , artificial intelligence , engineering , paleontology , physics , accounting , quantum mechanics , electrical engineering , biology
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. Copyright © 2013 John Wiley & Sons, Ltd.