MLP neural network as load forecasting tool on short- term horizon

This paper focus on multilayer feedforward neural networks, the most popular and widely-used paradigms in many applications, including energy forecasting Precisely, it provides a multilayer perceptron (MLP) architecture, capable to forecast the DPcg (difference between the electricity produced and consumed) in relation with solar radiation, for short- term horizon. The forecasting accuracy and precision, in capturing nonlinear interdependencies between the load and solar radiation of this structure is illustrated and discussed using a data based obtain from an experimental photovoltaic amphitheatre of minimum dimension 0.4kV/10kW.

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