Connecting to smart cities : analyzing energy times series to visualize monthly electricity peak load in residential buildings

Iram, S, Fernando, TP ORCID: https://orcid.org/0000-0001-5321-9071 and Hill, R 2018, 'Connecting to smart cities : analyzing energy times series to visualize monthly electricity peak load in residential buildings' , in: Proceedings of the Future Technologies Conference (FTC) 2018 , Advances in Intelligent Systems and Computing (AISC), 1 (880) , Springer, pp. 333-342.

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Abstract

Rapidly growing energy consumption rate is considered an alarming threat to economic stability and environmental sustainability. There is an urgent need of proposing novel solutions to mitigate the drastic impact of increased energy demand in urban cities to improve energy efficiency in smart buildings. It is commonly agreed that exploring, analyzing and visualizing energy consumption patterns in residential buildings can help to estimate their energy demands. Moreover, visualizing energy consumption patterns of residential buildings can also help to diagnose if there is any unpredictable increase in energy demand at a certain time period. However, visualizing and inferring energy consumption patterns from typical line graphs, bar charts, scatter plots is obsolete, less informative and do not provide deep and significant insight of the daily domestic demand of energy utilization. Moreover, these methods become less significant when high temporal resolution is required. In this research work, advanced data exploratory and data analytics techniques are applied on energy time series. Data exploration results are presented in the form of heatmap. Heatmap provides a significant insight of energy utilization behavior during different times of the day. Heatmap results are articulated from three analytical perspectives; descriptive analysis, diagnostic analysis and contextual analysis.

Item Type: Book Section
Editors: Arai, K, Bhatia, R and Kapoor, S
Schools: Schools > School of the Built Environment > Centre for Urban Processes, Resilient Infrastructures & Sustainable Environments (UPRISE)
Publisher: Springer
Series Name: Advances in Intelligent Systems and Computing (AISC)
ISBN: 9783030026851
Related URLs:
Depositing User: TP Fernando
Date Deposited: 13 Nov 2018 09:10
Last Modified: 30 Jul 2019 14:45
URI: http://usir.salford.ac.uk/id/eprint/48927

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