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An Ensemble Detection Model using Multinomial Classification of Stochastic Gas Smart Meter Data to Improve Wellbeing Monitoring in Smart Cities

Hurst, W, Curbelo Montañez, CA, Shone, N and Al-Jumeily, D An Ensemble Detection Model using Multinomial Classification of Stochastic Gas Smart Meter Data to Improve Wellbeing Monitoring in Smart Cities. IEEE Access. ISSN 2169-3536 (Accepted)

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Abstract

Fuel poverty has a negative impact on the wellbeing of individuals within a household; affecting not only comfort levels but also increased levels of seasonal mortality. Wellbeing solutions within this sector are moving towards identifying how the needs of people in vulnerable situations can be improved or monitored by means of existing supply networks and public institutions. Therefore, the focus of this research is towards wellbeing monitoring solution, through the analysis of gas smart meter data. Gas smart meters replace the traditional analogue electro-mechanical and diaphragm-based meters that required regular reading. They have received widespread popularity over the last 10 years. This is primarily due to the fact that by using this technology, customers are able to adapt their consumption behaviours based on real-time information provided by In-Home Devices. Yet, the granular nature of the datasets generated has also meant that this technology is ideal for further scalable wellbeing monitoring applications. For example, the autonomous detection of households at risk of energy poverty is possible and of growing importance in order to face up to the impacts of fuel poverty, quality of life and wellbeing of low-income housing. However, despite their popularity (smart meters), the analysis of gas smart meter data has been neglected. In this paper, an ensemble model is proposed to achieve autonomous detection, supported by four key measures from gas usage patterns, consisting of i) a tariff detection, ii) a temporally-aware tariff detection, iii) a routine consumption detection and iv) an age-group detection. Using a cloud-based machine learning platform, the proposed approach yielded promising classification results of up to 84.1% Area Under Curve (AUC), when the Synthetic Minority Over-sampling Technique (SMOTE) was utilised.

Item Type: Article
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
Divisions: Computer Science
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date Deposited: 20 Dec 2019 13:56
Last Modified: 20 Dec 2019 13:56
URI: http://researchonline.ljmu.ac.uk/id/eprint/11942

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