Clustering methods are increasingly being applied to residential smart meter data, providing a number of important opportunities for distribution network operators (DNOs) to manage and plan the low voltage networks. Clustering has a number of potential advantages for DNOs including, identifying suitable candidates for demand response and improving energy prole modelling. However, due to the high stochasticity and irregularity of household level demand, detailed analytics are required to dene appropriate attributes to cluster. In this paper we present indepth analysis of customer smart meter data to better understand peak demand and major sources of variability in their behaviour. We nd four key time periods in which the data should be analysed and use this to form relevant attributes for our clustering. We present a nite mixture model based clustering where we discover 10 distinct behaviour groups describing customers based on their demand and their variability. Finally, using an existing bootstrapping technique we show that the clustering is reliable. To the authors knowledge this is the rst time in the power systems literature that the sample robustness of the clustering has been tested.

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We present a refined parametric model for forecasting electricity demand, that performed particularly well in the recent Global Energy Forecasting Competition (GEFCom 2012). We begin by motivating and presenting a simple parametric model, treating electricity demand as a function of temperature and day of the year. We then set out a series of refinements to the model, explaining the rationale for each, and using competition scores to demonstrate that each successive refinement step increases the accuracy of the model's predictions. These refinements include combining models from multiple weather stations, removing outliers from the historical data and special treatment of public holidays.

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Abstract. While social networks can provide an ideal platform for upto-date information from individuals across the world, it has also proved to be a place where rumours fester and accidental or deliberate misinformation often emerges. In this article, we aim to support the task of making sense from social media data, and specifically, seek to build an autonomous message-classifier that filters relevant and trustworthy information from Twitter. For our work, we collected about 100 million public tweets, including users' past tweets, from which we identified 72 rumours (41 true, 31 false). We considered over 80 trustworthiness measures including the authors' profile and past behaviour, the social network connections (graphs), and the content of tweets themselves. We ran modern machine-learning classifiers over those measures to produce trustworthiness scores at various time windows from the outbreak of the rumour. Such time-windows were key as they allowed useful insight into the progression of the rumours. From our findings, we identified that our model was significantly more accurate than similar studies in the literature. We also identified critical attributes of the data that give rise to the trustworthiness scores assigned. Finally we developed a software demonstration that provides a visual user interface to allow the user to examine the analysis.