Reactive Power Forecasting

E. da Silva Christo and R. Castro Souza (Brazil)


Reactive Power Forecasting, Iteratively Reweighted Least Squares, Lags Distributed Autoregressive Model, Kohonen SelfOrganized Map.


The present work has as main purpose the development of a new short-term reactive power hourly forecast technique, which can be used at utility or substations levels. The proposed model, named A Hybrid Model for Reactive Forecasting, is divided in two stages. In the first stage, the active and reactive power data are classified by an unsupervised neural network – the well known Self Organized Maps of Kohonen (SOM); In the second stage, a Lag Distributed Autoregressive Model (ADL) is used with its parameters estimated by an Iteratively Reweighted Least Square (IRLS). It also includes a correction lag structure for serial autocorrelation of the residuals as used in the Cochrane-Orcutt formulation. The proposed model is applied to real data of one substation and the results are compared with two other approaches.

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