Machine Learning Applications for Smart Building Energy Utilization: A Survey

Huotari, Matti; Malhi, Avleen; Främling, Kary

doi:10.1007/s11831-023-10054-7

Cited by 3 publications

References 153 publications

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A Meta-Survey on Intelligent Energy-Efficient Buildings

Islam,

Guerrieri,

Gravina

et al. 2024

BDCC

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The rise of the Internet of Things (IoT) has enabled the development of smart cities, intelligent buildings, and advanced industrial ecosystems. When the IoT is matched with machine learning (ML), the advantages of the resulting enhanced environments can span, for example, from energy optimization to security improvement and comfort enhancement. Together, IoT and ML technologies are widely used in smart buildings, in particular, to reduce energy consumption and create Intelligent Energy-Efficient Buildings (IEEBs). In IEEBs, ML models are typically used to analyze and predict various factors such as temperature, humidity, light, occupancy, and human behavior with the aim of optimizing building systems. In the literature, many review papers have been presented so far in the field of IEEBs. Such papers mostly focus on specific subfields of ML or on a limited number of papers. This paper presents a systematic meta-survey, i.e., a review of review articles, that compares the state of the art in the field of IEEBs using the Prisma approach. In more detail, our meta-survey aims to give a broader view, with respect to the already published surveys, of the state-of-the-art in the IEEB field, investigating the use of supervised, unsupervised, semi-supervised, and self-supervised models in a variety of IEEB-based scenarios. Moreover, our paper aims to compare the already published surveys by answering five important research questions about IEEB definitions, architectures, methods/models used, datasets and real implementations utilized, and main challenges/research directions defined. This meta-survey provides insights that are useful both for newcomers to the field and for researchers who want to learn more about the methodologies and technologies used for IEEBs’ design and implementation.

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A Meta-Survey on Intelligent Energy-Efficient Buildings

Islam,

Guerrieri,

Gravina

et al. 2024

BDCC

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Introducing Security Mechanisms in OpenFog-Compliant Smart Buildings

Martín Toral,

Calvo,

Villar

et al. 2024

Electronics

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Designing smart building IoT applications is a complex task. It requires efficiently integrating a broad number of heterogeneous, low-resource devices that adopt lightweight strategies. IoT frameworks, especially if they are standard-based, may help designers to scaffold the applications. OpenFog, established as IEEE 1934 standard, promotes the use of free open source (FOS) technologies and has been identified for use in smart buildings. However, smart building systems may present vulnerabilities, which can put their integrity at risk. Adopting state-of-the-art security mechanisms in this domain is critical but not trivial. It complicates the design and operation of the applications, increasing the cost of the deployed systems. In addition, difficulties may arise in finding qualified cybersecurity personnel. OpenFog identifies the security requirements of the applications, although it does not describe clearly how to implement them. This article presents a scalable architecture, based on the OpenFog reference architecture, to provide security by design in buildings of different sizes. It adopts FOS technologies over low-cost IoT devices. Moreover, it presents guidelines to help developers create secure applications, even if they are not security experts. It also proposes a selection of technologies in different layers to achieve the security dimensions defined in the X.805 ITU-T recommendation. A proof-of-concept Indoor Environment Quality (IEQ) system, based on low-cost smart nodes, was deployed in the Faculty of Engineering of Vitoria-Gasteiz to illustrate the implementation of the presented approach. The operation of the IEQ system was analyzed using software tools frequently used to find vulnerabilities in IoT applications. The use of state-of-the-art security mechanisms such as encryption, certificates, protocol selection and network partitioning/configuration in the OpenFog-based architecture improves smart building security.

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Electricity Production Prediction by Microsoft Azure Machine Learning Service and Python User Blocks

Pliuhin,

Tsegelnyk,

Sukhonos

et al. 2024

Machine Learning and Computer Vision for Renewable Energy

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In this chapter, the forecasting of electricity consumption and production is conducted by analyzing indicators from previous years. The problem is addressed using machine learning within Microsoft Azure Machine Learning Studio. The outcome is an independent service integrated into Excel, enabling consumption forecasting for specified dates. The Excel user interface is developed using Visual Basic for Applications. Python was used to create user blocks for modifying input data pools and forming graphical dependencies, seamlessly integrated into the original modules of Microsoft Azure Machine Learning Studio. An additional aspect of the forecast results involves evaluating the quality of the predicted electricity consumption indicators. The materials used for this chapter were sourced with the support of Ukraine's National Power Company UKRENERGO.

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Machine Learning Applications for Smart Building Energy Utilization: A Survey

Cited by 3 publications

References 153 publications

A Meta-Survey on Intelligent Energy-Efficient Buildings

A Meta-Survey on Intelligent Energy-Efficient Buildings

Introducing Security Mechanisms in OpenFog-Compliant Smart Buildings

Electricity Production Prediction by Microsoft Azure Machine Learning Service and Python User Blocks

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