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Accurate load forecasts are required in most tasks of energy planning. In this paper we present a hybrid method for short-term load forecasting. We combined Exponential Smoothing, a classical method for time series forecasting, with Gradient Boosting, a powerful machine learning algorithm. The proposed model was tested with real data and the results showed a considerable improvement in forecasting...
Artificial neural networks have frequently been proposed for electricity load forecasting because of their capabilities for the nonlinear modelling of large multivariate data sets. Modelling with neural networks is not an easy task though; two of the main challenges are defining the appropriate level of model complexity, and choosing the input variables. This paper evaluates techniques for automatic...
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