Abstract
Energy market liberalization brings new opportunities, since large consumers have direct access to energy trading to buy energy for the next day. However, that requires a good estimation of the expected amount of energy and its hourly distribution in advance. On the other hand, smart energy meters are being installed in many facilities with the aim of achieving holistic submetering systems. These systems consist of a set of meters structured in several levels, so that there are hierarchical relations among upstream and downstream meters. This information could be exploited for achieving accurate one-day-ahead energy predictions. However, submetering systems might be incomplete due to unavailable meters or lost energy. In this paper, we propose a hierarchical prediction method for incomplete submetering systems that is based on 2D convolutional neural network (2D CNN) and is able to perform day ahead prediction of power consumption. This method exploits the hierarchical relations among meters and considers periodicity in order to forecast the power consumption for the next day. The proposed hierarchical method has proved to be more accurate and fast to forecast power consumption in incomplete submetering systems than using an individual predictions.
Grant PID2020-117890RB-I00 funded by MCIN/AEI/10.13039/501100011033.
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Alonso, S., Morán, A., Pérez, D., Prada, M.A., Fuertes, J.J., Domínguez, M. (2023). Hierarchical Prediction in Incomplete Submetering Systems Using a CNN. In: Iliadis, L., Maglogiannis, I., Alonso, S., Jayne, C., Pimenidis, E. (eds) Engineering Applications of Neural Networks. EANN 2023. Communications in Computer and Information Science, vol 1826. Springer, Cham. https://doi.org/10.1007/978-3-031-34204-2_21
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