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Short-term load forecasting based on the Kalman filter and the neural-fuzzy network (ANFIS)

Stavrakakis Georgios, Triantafyllia G. Nikolaou, Stelios A. Markoulakis

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Περίληψη

In this article the possibilities of the KALMAN filter as well as the neural-fuzzy network ANFIS (adaptive neural-fuzzy inference system) for the short-term load forecasting are presented and compared. In any case, the load forecasting for one entire day as well as the load forecasting for one or more hours ahead are possible. The approach followed in this case is as follows: considering that the medium hourly load is divided in 24 distinguishable time series (each time-series concerns the load history for one concrete hour of the day in the duration of a year).One can take the corresponding 24 models (distinguishable between them) aiming at the forecast of the medium hourly electric load for each hour separately. The evaluation of the precision (quality) of the forecasts is realised via their comparison with the corresponding real values of the hourly load of the electric consumption of the Crete Island, which have not been used for the training of the forecast models

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