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Machine learning for green smart homes
Springer Nature Switzerland AG
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.
Big data , Energy management , Energy modelling , Green buildings , Machine learning , Smart home
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
© 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