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Green neighbourhoods: the role of big data in low voltage networks’ planning

Vukadinovic Greetham, Danica and Hattam, Laura (2019). Green neighbourhoods: the role of big data in low voltage networks’ planning. In: Emrouznejad, Ali and Charles, Vincent eds. Big Data for the Greater Good. Studies in Big Data, 42. Springer, pp. 151–169.

DOI (Digital Object Identifier) Link: https://doi.org/10.1007/978-3-319-93061-9
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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 net- works. 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 net- work 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.

Item Type: Book Section
Copyright Holders: 2019 Springer
ISBN: 3-319-93061-3, 978-3-319-93061-9
Keywords: low voltage networks; electric demand forecast; renewables
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Item ID: 55835
Depositing User: Danica Vukadinovic Greetham
Date Deposited: 23 Jul 2018 12:42
Last Modified: 23 Jul 2018 12:42
URI: http://oro.open.ac.uk/id/eprint/55835
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