Aggregation and Prediction of Energy Consumption Data

What is the Aggregatino Level at which a Graph Neural Network Performs Optimally?

Bachelor thesis (2023)

Authors

L.J.K. Timp Electrical Engineering, Mathematics and Computer Science

Contributors

L. Cavalcante Siebert Interactive Intelligence - EEMCS (supervisor 1)
S.K. Kuilman Interactive Intelligence - EEMCS (supervisor 1)
M.M. de Weerdt Algorithmics - EEMCS (supervisor 2)
Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright: © 2023 Lennard Timp
Load forecasting Graph Neural Networks Aggregation
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Published Date

28-06-2023

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

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.