Abstract
Against the backdrop of the economically and ecologically optimal management of electrical energy systems, accurate predictions of consumption load profiles play an important role. On this basis, it is possible to plan and implement the use of controllable energy generation and storage systems as well as energy procurement with the required lead-time, taking into account the technical and contractual boundary conditions.
The recorded electrical load profiles will increase considerably in the course of the digitization of the energy industry. In order to make the most accurate predictions possible, it is necessary to develop and investigate models that take account of the growing quantity structure and, due to the significantly higher number of observations, improve the forecasting quality as far as possible.
Artificial neural networks (ANN) are increasingly being used to solve non-linear problems for a growing amount of data that is affected by human and other unpredictable influences. Consequently, the model approach of an ANN is chosen for predicting load profiles. Aim of the thesis is the simulative investigation and the evaluation of the quality and optimality of a prediction model based on an ANN for electrical load profiles.
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Bauer, F., Hagner, J., Bretschneider, P., Klaiber, S. (2021). Improvement of the prediction quality of electrical load profiles with artificial neural networks. In: Beyerer, J., Maier, A., Niggemann, O. (eds) Machine Learning for Cyber Physical Systems. Technologien für die intelligente Automation, vol 13. Springer Vieweg, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-62746-4_2
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DOI: https://doi.org/10.1007/978-3-662-62746-4_2
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