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Using Influencing Factors and Multilayer Perceptrons for Energy Demand Prediction

Boersma, Kitty (2019) Using Influencing Factors and Multilayer Perceptrons for Energy Demand Prediction.

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Abstract:Energy demand is rising, exhibiting more and more fluctuations, and smart grids need to be able to adjust accordingly. Therefore, an accurate way of predicting the energy consumption of a household is needed. In this research, the Pearson Correlation Coefficient is used to determine the effects of using internal and external influencing factors that influence the energy consumption of a household. These internal and external influencing factors are taken into account and are combined with existing and experimental knowledge about Multilayer Perceptrons. Next to that, two data resolutions are compared. The study found that using a 1-hour data resolution produces a more accurate prediction. Additionally, by using influencing factors, a possible manner of improving the accuracy of energy prediction is found. By these means, the research aims to aid future research on this topic.
Item Type:Essay (Bachelor)
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:54 computer science
Programme:Computer Science BSc (56964)
Keywords:Energy prediction, Multilayer perceptron, Pearson correlation coefficient, Influencing features, Deep learning
Link to this item:http://purl.utwente.nl/essays/78789
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