Time Series-Based GHG Emissions Prediction for Smart Homes
Abstract
Smart homes play a crucial role in reducing the residential sector electricity consumption and Greenhouse Gases (GHG) emissions. In this work, we present a time series approach to predict GHG emissions to be integrated into smart home management systems. More specifically, we used Long Short-Term Memory (LSTM), a variant of Recurrent Neural Networks. The prediction results get mean absolute percentage error (MAPE) close to 2 percent when the region under study has an energy matrix mostly based on fossil fuels, less intermittent. For regions in which more renewable sources are present, the MAPE is around 12 percent. However, in either case, LSTM can predict the hours well with smaller emissions among the next 24 hours. Such day-ahead information brings awareness to the users and allows the scheduling of appliances to work in the hours in which the emissions are minimal, reducing them without significantly affecting the consumers' behavior.
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Energy flexibility in buildings is increasingly useful to schedule consumption when supply is high.
“Thus, energy flexibility in buildings is increasingly useful to schedule consumption when supply is high [683].”
From Tackling climate change with machine learning (2023) · cites this paper as [683]
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Self-citation
- Authors
- Riekstin, A. C., Langevin, A., Dandres, T., Gagnon, G., & Cheriet, M.
- Affiliations
- Université du Québec (CA)
- CIRAIG (CA)
- Venue
- IEEE Transactions on Sustainable Computing
- Published
- 2020-01-01
- Volume
- 5
- Issue
- 1
- Pages
- 134-146
- ISSN
- 2377-3782 (ISSN-L)
- 2377-3790
- OpenAlex
- W2904906224
- Cited by
- 41
- References
- 44
- Citations by year
- 2026: 4 · 2025: 4 · 2024: 5 · 2023: 9 · 2022: 10 · 2021: 4 · 2020: 4 · 2019: 1
- FWCI
- 0.86
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Sustainable Computing (publishedVersion)
- Espace ÉTS (ETS) (submittedVersion)
- PolyPublie (École Polytechnique de Montréal) (submittedVersion)
- Funders
- Canada Research Chairs
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Energy Load and Power Forecasting
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid Energy Management
- Physical Sciences › Environmental Science › Environmental Engineering › Air Quality Monitoring and Forecasting
- Keywords
- home automation, residential electricity consumption, long short-term memory, recurrent neural networks, time series forecasting, appliance scheduling, renewable energy mix
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