STATE SPACE MODELS: MULTIVARIATE FORMULATION APPLIED TO LOAD FORECASTING / MODELOS EM ESPAÇO DE ESTADO: FORMULAÇÃO MULTIVARIADA APLICADA À PREVISÃO DE CARGA ELÉTRICA
AUTOR(ES)
MARCELO RUBENS DOS SANTOS DO AMARAL
DATA DE PUBLICAÇÃO
1996
RESUMO
The analysis of time series is, nowadays one of the most important tools in the decision making process, due mainly to the globalization of the world. As an illustration of that we can mention the recent contract signed between NEC/PUC-Rio and CEPEL/Eletrobrás, where time series techniques are to be used in the planning process of the brazilian sector. The state-space approach forms the basis of two important forecasting models to time series analysis the structural model and the state space innovation model. Because of that one finds it difficult to have a clear cut definition of either one of them. These two models formulation were implemented in comercial softwares: the structural model of A. Harvey in STAMP and the state space innovation of R. Goodrich in FMP. In order to check the perfomance of these state space approaches vis-à-vis the traditional forecasting techniques, it was used the following statistics: MAPE (Mean Absolute Percentage Error), RMSE (Root Mean Squared Error) and U-Theil. The traditional approaches used in the comparison were: Holt-Winters, Box &Jenkins and Backpropagation Neural Network. All the methods, included the state space ones were applied to the demand series of 32 electrical utilities which form the brazilian electrical distribution system. If was also attempted the multivariate state-space formulation of R. Goodrich which is included in FMP software.
ASSUNTO(S)
load forecasting previsao de carga espaco de estado multivariate models state space modelos multivariados
