Predicting Energy Consumption Through Machine Learning Using a Smart-Metering Architecture

Deligiannis, Paraskevas; Koutroubinas, S.; Koronias, George

doi:10.1109/mpot.2018.2852564

Cited by 13 publications

(15 citation statements)

References 4 publications

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“…For example, from constrained IoT devices employing CoAP to a cloud service that communicates through MQTT or AMQP [61,108,46]. They may also convert the lower-layer wireless protocols of the local, constrained devices for enabling Internet based transmission to the cloud [30]. At the network layer, gateways may be used to optimize routing, for example, by selectively prioritizing packets in networks connecting vehicles, which are bursty in nature.…”

Section: The Gatewaymentioning

confidence: 99%

Machine learning and data analytics for the IoT

Adi

Anwar

Baig

et al. 2020

Neural Comput & Applic

192

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The Internet of Things (IoT) applications has grown in exorbitant numbers, generating a large amount of data required for intelligent data processing. However, the varying IoT infrastructures (i.e. cloud, edge, fog) and the limitations of the IoT application layer protocols in transmitting/receiving messages become the barriers in creating intelligent IoT applications. These barriers prevent current intelligent IoT applications to adaptively learn from other IoT applications. In this paper, we critically review how IoT-generated data is processed for machine learning analysis, and highlight the current challenges in furthering intelligent solutions in the IoT environment. Furthermore, we propose a framework to enable IoT applications to adaptively learn from other IoT applications, and present a case study in how the framework can be applied to the real studies in the literature. Finally, we discuss the key factors that have an impact on future intelligent applications for the IoT.

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Section: The Gatewaymentioning

confidence: 99%

Machine learning and data analytics for the IoT

Adi

Anwar

Baig

et al. 2020

Neural Comput & Applic

192

View full text Add to dashboard Cite

show abstract

“…Through extensive simulation results, the authors prove that a timely extraction of the SM data leads to better energy predictions and thus, increases the economic viability of household consumers. In [65], again the random forest classifying technique is used however, the focus mainly remains on developing a flexible SM architecture to predict a small building's energy consumption. This work basically focuses on the design of an SM therefore, the OSI (open system interconnection) layer model has been adjusted to show the link between the consumers, the providers, and the cloud service using Zigbee.…”

Section: ) Smart Metering and Energy Management In Buildings/homesmentioning

confidence: 99%

Energy Management in Smart Buildings and Homes: Current Approaches, a Hypothetical Solution, and Open Issues and Challenges

et al. 2021

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Energy plays a pivotal role for economic development of a country. A reliable energy source is needed to improve the living standards of people. To achieve such a goal, governments and industries are trying to install a new energy infrastructure called the "Smart Grid". This helps to manage the electricity generation and distribution in an efficient manner. Buildings and other structures are the biggest consumers of electricity. There is a need to reduce the energy consumption so that the resources can be utilized efficiently. Therefore, in this paper, we give a comprehensive state-of-the-art on various recent techniques and solutions which provide energy savings in smart homes and buildings. This includes statistical models, cloud computing based solutions, fog computing and smart metering based architectures, and several other IoT (internet of things) inspired solutions. We also present a hypothetical model that treats energy supply and usage in buildings as a self-managing energy system (SES). This paper is concluded by highlighting several open issues and challenges related to energy management in buildings.INDEX TERMS Energy Management, Smart Buildings and Homes.

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“…Our experimental setup basically consists of a three-phase electricity meter designed by Meazon S.A., which is installed in the central electricity panel of a house. This meter is capable of remote monitoring and controlling the energy consumption of a household and/or an industrial building [33]. It is a rail-type device with small size (2 DIN), which can be easily installed and implements monitoring, measurement logging and controlling of:…”

Section: Din-rail μEter and Its Interfacementioning

confidence: 99%

Evaluation of Features Generated by a High-End Low-Cost Electrical Smart Meter

et al. 2021

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The problem of energy disaggregation is the separation of an aggregate energy signal into the consumption of individual appliances in a household. This is useful, since the goal of energy efficiency at the household level can be achieved through energy-saving policies towards changing the behavior of the consumers. This requires as a prerequisite to be able to measure the energy consumption at the appliance level. The purpose of this study is to present some initial results towards this goal by making heavy use of the characteristics of a particular din-rail meter, which is provided by Meazon S.A. Our thinking is that meter-specific energy disaggregation solutions may yield better results than general-purpose methods, especially for sophisticated meters. This meter has a 50 Hz sampling rate over 3 different lines and provides a rather rich set of measurements with respect to the extracted features. In this paper we aim at evaluating the set of features generated by the smart meter. To this end, we use well-known supervised machine learning models and test their effectiveness on certain appliances when selecting specific subsets of features. Three algorithms are used for this purpose: the Decision Tree Classifier, the Random Forest Classifier, and the Multilayer Perceptron Classifier. Our experimental study shows that by using a specific set of features one can enhance the classification performance of these algorithms.

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Predicting Energy Consumption Through Machine Learning Using a Smart-Metering Architecture

Cited by 13 publications

References 4 publications

Machine learning and data analytics for the IoT

Machine learning and data analytics for the IoT

Energy Management in Smart Buildings and Homes: Current Approaches, a Hypothetical Solution, and Open Issues and Challenges

Evaluation of Features Generated by a High-End Low-Cost Electrical Smart Meter

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