Transformers for Energy Forecast
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 foreca...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-08-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/15/6840 |
| _version_ | 1797586002360926208 |
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| author | Hugo S. Oliveira Helder P. Oliveira |
| author_facet | Hugo S. Oliveira Helder P. Oliveira |
| author_sort | Hugo S. Oliveira |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T00:16:09Z |
| format | Article |
| id | doaj.art-178212d9e31b4981a256966bb9cc7fbf |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T00:16:09Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-178212d9e31b4981a256966bb9cc7fbf2023-11-18T23:35:09ZengMDPI AGSensors1424-82202023-08-012315684010.3390/s23156840Transformers for Energy ForecastHugo S. Oliveira0Helder P. Oliveira1Institute for Systems and Computer Engineering, Technology and Science—INESC TEC, University of Porto, 4200-465 Porto, PortugalInstitute for Systems and Computer Engineering, Technology and Science—INESC TEC, University of Porto, 4200-465 Porto, PortugalForecasting 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.https://www.mdpi.com/1424-8220/23/15/6840transformerstime-series forecast |
| spellingShingle | Hugo S. Oliveira Helder P. Oliveira Transformers for Energy Forecast Sensors transformers time-series forecast |
| title | Transformers for Energy Forecast |
| title_full | Transformers for Energy Forecast |
| title_fullStr | Transformers for Energy Forecast |
| title_full_unstemmed | Transformers for Energy Forecast |
| title_short | Transformers for Energy Forecast |
| title_sort | transformers for energy forecast |
| topic | transformers time-series forecast |
| url | https://www.mdpi.com/1424-8220/23/15/6840 |
| work_keys_str_mv | AT hugosoliveira transformersforenergyforecast AT helderpoliveira transformersforenergyforecast |