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Big Data for the Greater Good pp 151–169Cite as

Green Neighbourhoods: The Role of Big Data in Low Voltage Networks’ Planning

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Part of the book series: Studies in Big Data ((SBD,volume 42))

Abstract

In this chapter, we aim to illustrate the benefits of data collection and analysis to the maintenance and planning of current and future low voltage networks. To start with, we present several recently developed methods based on graph theory and agent-based modelling for analysis and short- and long-term prediction of individual households electric energy demand. We show how maximum weighted perfect matching in bipartite graphs can be used for short-term forecasts, and then review recent research developments of this method that allow applications on very large datasets. Based on known individual profiles, we then review agent-based modelling techniques for uptake of low carbon technologies taking into account socio-demographic characteristics of local neighbourhoods. While these techniques are relatively easily scalable, measuring the uncertainty of their results is more challenging. We present confidence bounds that allow us to measure uncertainty of the uptake based on different scenarios. Finally, two case-studies are reported, describing applications of these techniques to energy modelling on a real low-voltage network in Bracknell, UK. These studies show how applying agent-based modelling to large collected datasets can create added value through more efficient energy usage. Big data analytics of supply and demand can contribute to a better use of renewable sources resulting in more reliable, cheaper energy and cut our carbon emissions at the same time.

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Notes

  1. 1.

    For example, for half-hourly data if predicted peaks are early/late up to one hour and a half that means that a permutation window is 3.

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Acknowledgements

This work was supported by Scottish and Southern Electricity Networks through the New Thames Valley Vision Project (SSET203 New Thames Valley Vision), and funded by the Low Carbon Network Fund established by Ofgem.

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Authors and Affiliations

  1. Department of Mathematics and Statistics, Centre for the Mathematics of Human Behaviour, University of Reading, Reading, RG6 6AX, UK

    Danica Vukadinović Greetham & Laura Hattam

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  1. Danica Vukadinović Greetham
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  2. Laura Hattam
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Correspondence to Danica Vukadinović Greetham .

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Editors and Affiliations

  1. Operations and Information Management, Aston Business School, Aston University, Birmingham, United Kingdom

    Ali Emrouznejad

  2. Buckingham Business School, University of Buckingham, Buckingham, United Kingdom

    Vincent Charles

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Greetham, D.V., Hattam, L. (2019). Green Neighbourhoods: The Role of Big Data in Low Voltage Networks’ Planning. In: Emrouznejad, A., Charles, V. (eds) Big Data for the Greater Good. Studies in Big Data, vol 42. Springer, Cham. https://doi.org/10.1007/978-3-319-93061-9_7

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