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• W2019968694 abstract "The Portuguese power grid company wants to improve the accuracy of the electricity load demand forecast within an horizon of 48 hours, in order to identify the need of reserves to be allocated in the Iberian Market. In this work we present updated results on the identification of radial basis function neural network load demand predictive models. The methodology follows the principles already employed by the authors in different applications: the NN models are trained by the Levenberg-Marquardt algorithm using a modified training criterion, and the model structure (number of neurons and input terms) is evolved using a multi-objective genetic algorithm. The set of goals and objectives used in the model optimisation reflect different requirements in the design: obtaining good generalisation ability, good balance between one-step-ahead prediction accuracy and model complexity, and good multi-step prediction accuracy. In this work the prediction horizon was increased, the model fitness assessment was altered, and the model structure search space was enlarged. Results are also presented for a predictive nearest neighbour type approach, which establishes a baseline for predictive methods comparison." @default.
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• W2019968694 date "2010-01-01" @default.
• W2019968694 modified "2023-09-27" @default.
• W2019968694 title "Improving the Identification of RBF Predictive Models to Forecast the Portuguese Electricity Consumption*" @default.
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• W2019968694 doi "https://doi.org/10.3182/20100329-3-pt-3006.00039" @default.
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