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- Title
Transformers for Energy Forecast.
- Authors
Oliveira, Hugo S.; Oliveira, Helder P.
- Abstract
Forecasting energy consumption models allow for improvements in building performance and reduce energy consumption. Energy efficiency has become a pressing concern in recent years due to the increasing energy demand and concerns over climate change. This paper addresses the energy consumption forecast as a crucial ingredient in the technology to optimize building system operations and identifies energy efficiency upgrades. The work proposes a modified multi-head transformer model focused on multi-variable time series through a learnable weighting feature attention matrix to combine all input variables and forecast building energy consumption properly. The proposed multivariate transformer-based model is compared with two other recurrent neural network models, showing a robust performance while exhibiting a lower mean absolute percentage error. Overall, this paper highlights the superior performance of the modified transformer-based model for the energy consumption forecast in a multivariate step, allowing it to be incorporated in future forecasting tasks, allowing for the tracing of future energy consumption scenarios according to the current building usage, playing a significant role in creating a more sustainable and energy-efficient building usage.
- Subjects
ENERGY consumption forecasting; ENERGY consumption of buildings; RECURRENT neural networks; ARTIFICIAL neural networks; CONSUMPTION (Economics); DEMAND forecasting
- Publication
Sensors (14248220), 2023, Vol 23, Issue 15, p6840
- ISSN
1424-8220
- Publication type
Article
- DOI
10.3390/s23156840