Forecasting of Energy Consumption Based on Gaussian Mixture Model and Classification Techniques
Palabras clave:
Predictive models, Load Forecasting, Gaussian Mixture Model, Mixture of models (en)Descargas
The estimation of energy demand is not always straightforward or reliable, as one or several classes may fail in the prediction. In this study, a novel methodology of load forecasting is proposed. Three different configurations of weak Artificial Neural Networks perform a supervised classification of energy consumption data, each one providing an output vector of unreliable predicted data. Under the clustering method k-means, multiple patterns are identified, and then processed by the Gaussian Mixture Model in order to provide higher relevance to the more accurate predicted samples of data. The accuracy of the prediction is evaluated with the several error rate measurements. Finally, a mixture of the generated forecasts by the methods is performed, showing a lower error rate compared to the inputs predictions, therefore, a more reliable forecast.
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Derechos de autor 2018 Simposio Internacional sobre la Calidad de la Energía Eléctrica - SICEL

Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.