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Transformers and Contextual Information in Temperature Prediction of Residential Buildings for Improved Energy Consumption
Örebro University, School of Science and Technology. (Center for Applied Autonomous Sensor Systems (AASS))
Örebro University, School of Science and Technology. (Center for Applied Autonomous Sensor Systems (AASS))ORCID iD: 0000-0002-9607-9504
Örebro University, School of Science and Technology. (Center for Applied Autonomous Sensor Systems (AASS))ORCID iD: 0000-0002-3122-693X
2022 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Energy optimization plays a vital role in decreasing the carbon footprint of residential buildings. In this paper, we present a prediction model of indoor temperature in residential buildings in three different case studies in different towns in Sweden. To predict the indoor temperature accurately, a dataset based on several years of data collection (up to 7 years) has been used. This paper applies both the traditional LSTM model as well as the more recent transformer model. The latter has been used because of its ability to perform a mechanism of self-attention that shows particular promise in multivariate sensor data. In addition to these algorithms, the data set is also modified based on contextual information and compared against an approach where no contextual information is used. Contextual information in this case takes into account the physical location of specific apartment units within the full residence and builds individual models based on the location of the unit. The results demonstrate that transformers are better suited for task of prediction, and that transformers combined with contextual information, provide a suitable approach for energy consumption prediction. 

Place, publisher, year, edition, pages
2022.
Keywords [en]
Transformers, Contextual Information, Residential Buildings
National Category
Computer Systems
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:oru:diva-112197OAI: oai:DiVA.org:oru-112197DiVA, id: diva2:1842991
Conference
1st Annual AAAI Workshop on AI to Accelerate Science and Engineering (AI2ASE), February 28, 2022
Funder
Knowledge Foundation, 20190128
Note

This work has been supported by the Industrial Graduate School Collaborative AI & Robotics funded by the Swedish Knowledge Foundation Dnr:20190128.

Available from: 2024-03-07 Created: 2024-03-07 Last updated: 2024-03-08Bibliographically approved

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Kalidindi, Sai Sushanth VarmaBanaee, HadiLoutfi, Amy

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Citation style
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