Forecasting weather impacts on the United Kingdom telecommunication network
The telecommunications network is a crucial part of the national infrastructure of the United Kingdom, with its reliable service becoming an increasingly vital feature of commercial activities and daily life. As with other national infrastructure, weather poses a risk to its operation by causing di...
| Main Author: | |
|---|---|
| Format: | Thesis |
| Language: | English |
| Published: |
2019
|
| Subjects: | |
| Online Access: | https://centaur.reading.ac.uk/85058/ https://centaur.reading.ac.uk/85058/1/22826594_Halford_form.pdf.PDF |
| Summary: | The telecommunications network is a crucial part of the national infrastructure of the United Kingdom, with its reliable service becoming an increasingly vital feature of commercial activities and daily life. As with other national infrastructure, weather poses a risk to its operation by causing disruptions in service through faults in the physical network, though the precise relationships between weather and faults are poorly understood. This thesis explores the effect that weather has on the network and demonstrates that by using modem weather forecasting techniques, the negative effects of weather on the network can be minimised. The organisation used for this study is Openreach, a subsidiary of BT and the operator of 87% per cent of UK broadband infrastructure. Using the ERA-Interim reanalysis product, statistical relationships between weather and faults in the telecommunications network are identified in the construction of an impact model, with precipitation identified as the primary historical weather driver for faults. The effects of weather on the network are quantified for the first time using long-term weather data (36 years) and the statistical impact model to produce a consistent "synthetic" fault dataset, demonstrating that weather has a wide range of effects on the network. A multiple linear regression model explains 62% of variability in the fault data, highlighting the importance of weather. Until now, only short-term records (less than 5 years), have been used to quantify weather variability, leading to an underestimation of the variability. Specifically, a clear seasonality is observed in the fault variability, with winter being the worst performing season, leading to a focus on forecasting this season in this study. Forecasting of faults is performed for weeks two to four in the business planning cycle, aligning with decision timescales in network operations. Large-scale atmospheric patterns are used in the analysis, namely; the North Atlantic Oscillation, as it is shown to have forecast ... |
|---|