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Machine learning for green smart homes
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Date
2022-04-22
Authors
O'Regan, Brian
Silva, Fábio
Carroll, Paula
Dubuisson, Xavier
Lyons, Pádraig
Journal Title
Journal ISSN
Volume Title
Publisher
Springer Nature Switzerland AG
Published Version
Abstract
Smarter approaches to data processing are essential to realise the potential benefits of the exponential growth in energy data in homes from a variety of sources, such as smart metres, sensors and other devices. Machine learning encompasses several techniques to process and visualise data. Each technique is specifically suited to certain data types and problems, whether it be supervised, unsupervised or reinforcement learning. These techniques can be applied to increase the efficient use of energy within a home, enable better and more accurate home owner decision-making and help contribute to greener building stock. This chapter presents the state of the art in this area and looks forward to potential new uses for machine learning in renewable energy data.
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Keywords
Big data , Energy management , Energy modelling , Green buildings , Machine learning , Smart home
Citation
O'Regan, B., Silva, F., Carroll, P., Dubuisson, X. and Lyons, P. (2022) 'Machine learning for green smart homes', in: Lahby, M., Al-Fuqaha, A. and Maleh, Y. (eds.) Computational Intelligence Techniques for Green Smart Cities. Green Energy and Technology. Springer, Cham, pp. 41-66. doi: 10.1007/978-3-030-96429-0_2
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Copyright
© 2022, the Authors, under exclusive licence to Springer Nature Switzerland AG. This is a post-peer-review, pre-copyedit version of a paper published in Lahby, M., Al-Fuqaha, A. and Maleh, Y. (eds.) Computational Intelligence Techniques for Green Smart Cities. Green Energy and Technology. Springer, Cham, pp. 41-66. doi: 10.1007/978-3-030-96429-0_2. The final authenticated version is available online at: https://doi.org/10.1007/978-3-030-96429-0_2