Aggregation and Prediction of Energy Consumption Data
What is the Aggregatino Level at which a Graph Neural Network Performs Optimally?
L.J.K. Timp (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Luciano Siebert – Mentor (TU Delft - Interactive Intelligence)
Sietze Kai Kuilman – Mentor (TU Delft - Interactive Intelligence)
MM De Weerdt – Graduation committee member (TU Delft - Algorithmics)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
Electrical load forecasting, namely short-term load forecasting, is essential to power grids’ safe and efficient operations. The need for accurate short-term load forecasting becomes increasingly pressing with increased renewable energy sources, which are stochastic in their power supply. Most forecasting models are focused on the temporal information for predictions, ignoring the spatial information of neighbouring houses. However, as neighbouring houses are often under similar circumstances, such as weather and holiday conditions, predictions
could benefit from this information. Moreover, aggregating electric loads could further improve the accuracy of predictions, as the loads become less
stochastic when aggregated. This paper looks at Graph WaveNet, which can capture the hidden spatial dependencies of different houses without needing geographic information about the houses. The framework is compared against a baseline on different aggregation levels. The results show that the framework can benefit from aggregating the residential electrical loads and improves over the baseline on all aggregation levels.