Optimising Parameters in Recurrence Quantification Analysis of Smart Energy Systems

Recurrence Quantification Analysis (RQA) can help to detect significant events and phase transitions of a dynamical system, but choosing a suitable set of parameters is crucial for the success. From recurrence plots different RQA variables can be obtained and analysed. Currently, most of the methods for RQA radius optimisation are focusing on a single RQA variable. In this work we are proposing two new methods for radius optimisation that look for an optimum in the higher dimensional space of the RQA variables, therefore synchronously optimising across several variables. We illustrate our approach using two case studies: a well known Lorenz dynamical system, and a time-series obtained from monitoring energy consumption of a small enterprise. Our case studies show that both methods result in plausible values and can be used to analyse energy data

Investigating Robustness of Energy Management Maps for SMEs

Using the data from three small businesses, we are investigating robustness of the recently proposed Recurrence Quantitive Analysis (RQA) based method for energy management of small and medium enterprises. The method consists of two phases, the training phase where the map or maps of ‘usual’ behaviour is obtained, and the operational phase where the new data is tested against the existing map(s). We measure how the output changes when there is a small change in input, with respect to the sampling rate, missing data and noise. Our results over three qualitatively different datasets show that the method is relatively robust and can be used for different SMEs

Electric vehicles and low-voltage grid: impact of uncontrolled demand side response

The authors are looking at the impact of electric vehicles (EV) charging from low-voltage (LV) networks. Based on the data obtained from two different pilot projects: (i) Mini-E trial where EV users were incentivised to charge during the night; (ii) My Electric Avenue trial, where there were no similar incentives, authors want to quantify the impact of EV charging, presuming that the number of home-charging EV users will increase significantly in the near future. By assuming that the current load at individual household level is known or inferred, simulations are performed to estimate the future load. The authors look at different percentages of EV uptake and model clustered scenarios, where the social networking effect is imposed – users adopt an EV with a higher probability if their neighbour already has one. Simulations demonstrate that incentivising night-time charging can create large new peaks during the night, which could have negative effects on LV networks. On the other hand, simulations based on the data with no incentives shows that naturally occurring diversity in charging behaviour does not automatically result in comparable network stress at the same penetrations

Centrality and spectral radius in dynamic communication networks

We explore the influence of the choice of attenuation factor on Katz centrality indices for evolving communication networks. For given snapshots of a network observed over a period of time, recently developed communicability indices aim to identify best broadcasters and listeners in the network. In this article, we looked into the sensitivity of communicability indices on the attenuation factor constraint, in relation to spectral radius (the largest eigenvalue) of the network at any point in time and its computation in the case of large networks. We proposed relaxed communicability measures where the spectral radius bound on attenuation factor is relaxed and the adjacency matrix is normalised in order to maintain the convergence of the measure. Using a vitality based measure of both standard and relaxed communicability indices we looked at the ways of establishing the most important individuals for broadcasting and receiving of messages related to community bridging roles. We illustrated our findings with two examples of real-life networks, MIT reality mining data set of daily communications between 106 individuals during one year and UK Twitter mentions network, direct messages on Twitter between 12.4k individuals during one week

On the radius of centrality in evolving communication networks

In this article, we investigate how the choice of the attenuation factor in an extended version of Katz centrality influences the centrality of the nodes in evolving communication networks. For given snapshots of a network, observed over a period of time, recently developed communicability indices aim to identify the best broadcasters and listeners (receivers) in the network. Here we explore the attenuation factor constraint, in relation to the spectral radius (the largest eigenvalue) of the network at any point in time and its computation in the case of large networks. We compare three different communicability measures: standard, exponential, and relaxed (where the spectral radius bound on the attenuation factor is relaxed and the adjacency matrix is normalised, in order to maintain the convergence of the measure). Furthermore, using a vitality-based measure of both standard and relaxed communicability indices, we look at the ways of establishing the most important individuals for broadcasting and receiving of messages related to community bridging roles. We compare those measures with the scores produced by an iterative version of the PageRank algorithm and illustrate our findings with two examples of real-life evolving networks: the MIT reality mining data set, consisting of daily communications between 106 individuals over the period of one year, a UK Twitter mentions network, constructed from the direct \emph{tweets} between 12.4k individuals during one week, and a subset the Enron email data set

Total Positive Influence Domination on Weighted Networks

We are proposing two greedy and a new linear programming based approximation algorithm for the total positive influence dominating set problem in weighted networks. Applications of this problem in weighted settings include finding: a minimum cost set of nodes to broadcast a message in social networks, such that each node has majority of neighbours broadcasting that message; a maximum trusted set in bitcoin network; an optimal set of hosts when running distributed apps etc. Extensive experiments on different generated and real networks highlightadvantages and potential issues for each algorithm

Storm naming and forecast communication: A case study of Storm Doris

On the 23rd February 2017, a significant low-pressure system named Storm Doris crossed the Republic of Ireland and the UK causing widespread disruption. As an early example of a storm named through the Met Office and Met Eireann ‘Name our Storms’ project, this provided an excellent opportunity to study how information about extreme weather in the UK spread through the media. In traditional media, the forecast of Storm Doris was widely reported upon on the 21st and 22nd February. On the 23rd February, newspaper coverage of the event rapidly switched to reporting the impact of the storm. Around three times the number of words and twice the number of articles were published about the impacts of Storm Doris in comparison to its forecast. Storm Doris rapidly became a broader cultural topic with an imprint on political news because of two by-elections that occurred by coincidence on the 23rd February. In the social media, rapid growth of the number of tweets about Storm Doris closely mirrored the growth of newspaper articles about the impacts of the storm. The network structure of the tweets associated with Storm Doris revealed the importance of both the Met Office official twitter account and newspaper and rail company accounts in disseminating information about the storm. Storm names, in addition to their benefit for forecast communication, also provide researchers with a useful and easily collected target to study the development and evolution of public understanding of extreme weather events

Towards the computer-aided diagnosis of dementia based on the geometric and network connectivity of structural MRI data

We present an intuitive geometric approach for analysing the structure and fragility of T1-weighted structural MRI scans of human brains. Apart from computing characteristics like the surface area and volume of regions of the brain that consist of highly active voxels, we also employ Network Theory in order to test how close these regions are to breaking apart. This analysis is used in an attempt to automatically classify subjects into three categories: Alzheimer’s disease, mild cognitive impairment and healthy controls, for the CADDementia Challenge

Review of Low Voltage Load Forecasting: Methods, Applications, and Recommendations

The increased digitalisation and monitoring of the energy system opens up numerous opportunities to decarbonise the energy system. Applications on low voltage, local networks, such as community energy markets and smart storage will facilitate decarbonisation, but they will require advanced control and management. Reliable forecasting will be a necessary component of many of these systems to anticipate key features and uncertainties. Despite this urgent need, there has not yet been an extensive investigation into the current state-of-the-art of low voltage level forecasts, other than at the smart meter level. This paper aims to provide a comprehensive overview of the landscape, current approaches, core applications, challenges and recommendations. Another aim of this paper is to facilitate the continued improvement and advancement in this area. To this end, the paper also surveys some of the most relevant and promising trends. It establishes an open, community-driven list of the known low voltage level open datasets to encourage further research and development.Comment: 37 pages, 6 figures, 2 tables, review pape

Tweeting Economists: Antisocial in the socials?

Economists have often been accused of adopting superior and distant attitudes (Fourcade, Ollion and Algan, 2015). This attributed stance has been variously linked to both poor understanding and traction of economics with the general public, the failure to generate realistic predictions and prescriptions (Coyle, 2012; Bresser-Pereira, 2014), and the lack of diversity in the profession (Crawford et al., 2018; Stevenson and Zlotnick, 2018; Bayer and Rouse, 2016). In this piece we focus specifically on Twitter communications by economists to investigate the ability of economists to fruitfully engage with the public in these networks and the attitudes their language use betrays. We compare economists to scientists, gathering data from the Twitter accounts of both the top 25 economists and 25 scientists as identified by IDEAS and sciencemag, who account for the lion’s share of the Twitter following, collecting a total of 127,593 tweets written between December 2008 and April 2017. Using both network and language analysis our paper finds that although both groups communicate mostly with people outside their profession, economists tweet less, mention fewer people and have fewer Twitter conversations with strangers than a comparable group of experts in the sciences, and sentiment analysis shows they are also more distant. The language analysis of differences in register (a higher register is generally less accessible and thus more distanced) finds that economists use a higher number of complex words, specific names and abbreviations than scientists, and differences in pronoun use reveal they are also less inclusive, all of which adds to distancing