Analysis of the factors that influence the performance of an energy demand forecasting model
Keywords:Machine Learning, Artificial neural network, DemandForecast
The forecast of energy demand is one of the essential variables for the operation, planning, and estimation of tariffs for electric energy networks. There is currently a significant investment in modernizing the seven domains of an Intelligent Electric Grid (Smart-Grids). The operation of these various domains requires greater precision in forecasting demand for use and energy generation. Several techniques have been applied for this purpose, the most promising being machine learning, which in recent years sss received special attention. The focus of this work is to analyze a univariate demand forecasting method that employs deep artificial neural networks. The load time series history is used as input, added to the seasonality representation to predict the future load values of the electrical system. It will be shown that several factors influence the answer, and one of them is the choice of the way to decompose the problem and, therefore, the applied model.
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