Internet traffic volumes characterization and forecasting

Internet usage increases every year and the need to estimate the growth of the generated traffic has become a major topic. Forecasting actual figures in advance is essential for bandwidth allocation, networking design and investment planning. In this thesis novel mathematical equations are presented to model and to predict long-term Internet traffic in terms of total aggregating volume, globally and more locally. Historical traffic data from consecutive years have revealed hidden numerical patterns as the values progress year over year and this trend can be well represented with appropriate mathematical relations. The proposed formulae have excellent fitting properties over long-history measurements and can indicate forthcoming traffic for the next years with an exceptionally low prediction error. In cases where pending traffic data have already become available, the suggested equations provide more successful results than the respective projections that come from worldwide leading research. The studies also imply that future traffic strongly depends on the past activity and on the growth of Internet users, provided that a big and representative sample of pertinent data exists from large geographical areas. To the best of my knowledge this work is the first to introduce effective prediction methods that exclusively rely on the static attributes and the progression properties of historical values

Investigating the Ability of Smart Electricity Meters to Provide Accurate Low Voltage Network Information to the UK Distribution Network Operators

This research presents a picture of the current status and the future developments of the LV electricity grid and the capabilities of the smart metering programme in the UK as well as investigating the major research trends and priorities in the field of Smart Grid. This work also extensively examines the literature on the crucial LV network performance indicators such as losses, voltage levels, and cable capacity percentages and the ways in which DNOs have been acquiring this knowledge as well the ways in which various LV network applications are carried out and rely on various sources of data. This work combines 2 new smart meter data sets with 5 established methods to predict a proportion of consumer’s data is not available using historical smart meter data from neighbouring smart meters. Our work shows that half-hourly smart meter data can successfully predict the missing general load shapes, but the prediction of peak demands proves to be a more challenging task. This work then investigates the impact of smart meter time resolution intervals and data aggregation levels in balanced and unbalanced three phase LV network models on the accuracy of critical LV network performance indicators and the way in which these inaccuracies affect major smart LV network application of the DNOs in the UK. This is a novel work that has not been carried out before and shows that using low time resolution and aggregated smart meter data in load flow analysis models can negatively affect the accuracy of critical low voltage network estimates