@inbook{3874a8c6e6844007be5b76b667006c49,
title = "Machine Learning for Green Smart Homes",
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.",
keywords = "Big data, Energy management, Energy modelling, Green buildings, Machine learning, Smart home",
author = "Brian O{\textquoteright}Regan and F{\'a}bio Silva and Paula Carroll and Xavier Dubuisson and P{\'a}draig Lyons",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.",
year = "2022",
doi = "10.1007/978-3-030-96429-0\_2",
language = "English",
series = "Green Energy and Technology",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "41--66",
booktitle = "Green Energy and Technology",
address = "Germany",
}
