Projects
Projects: Projects for Investigator |
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| Reference Number | NIA_NGN_120 | |
| Title | Predictive Analytics Part Two | |
| Status | Completed | |
| Energy Categories | Fossil Fuels: Oil Gas and Coal(Oil and Gas, Refining, transport and storage of oil and gas) 100%; | |
| Research Types | Applied Research and Development 100% | |
| Science and Technology Fields | PHYSICAL SCIENCES AND MATHEMATICS (Applied Mathematics) 100% | |
| UKERC Cross Cutting Characterisation | Not Cross-cutting 100% | |
| Principal Investigator |
Project Contact No email address given Northern Gas Networks |
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| Award Type | Network Innovation Allowance | |
| Funding Source | Ofgem | |
| Start Date | 01 February 2015 | |
| End Date | 01 November 2016 | |
| Duration | 21 months | |
| Total Grant Value | £932,400 | |
| Industrial Sectors | Technical Consultancy | |
| Region | Yorkshire & Humberside | |
| Programme | Network Innovation Allowance | |
| Investigators | Principal Investigator | Project Contact , Northern Gas Networks (100.000%) |
| Web Site | http://www.smarternetworks.org/project/NIA_NGN_120 |
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| Objectives | The objectives of the project are as follows : -Development of analytical models covering a wide variety of business activitiesUnderstanding of the strengths and weaknesses of the available data and how this impacts on a variety of modelling approachesUnderstanding the potential improvements in output benefits that a structured analytical approach can deliver compared with a "traditional" approach across a wide variety of business areas (including developing an understanding of where a structured analytical approach is unlikely to deliver significant improvements)Compare and understand the strengths and weaknesses of a range of modelling approaches and techniquesDevelop understanding as to how to effectively and efficiently engage with specialist external providers of modelling solutionsTransfer knowledge, learning and experience The success of the project will be measured across Technical Success and Learning & Knowledge Transfer. Technical SuccessAcross the opportunities explored: -Were successful analytical models developed (i. e. a model developed using "training" data was able to model relationships using "test" data previously unseen by the model)?Was their "success" at providing forecasts and insights able to be measured?Were confidence intervals able to be produced for the models developed?Were a variety of technical approaches tested and were the advantages / disadvantages of these assessed?Learning & Knowledge TransferWas learning and knowledge successfully transferred to allow / improve the following: -Improved technical understanding of structured data analyticsAppreciation of the different techniques, approaches and methodologies that can be employedAbility to identify areas where structured analytical models and solutions could potentially deliver improvements compared with the traditional approachesAbility to select the approach that best matched the opportunity, data and potential benefit. Ability to engage with expert external providers in an informed, effective and efficient way. Successful transfer of knowledge and learning to other organisations through the NIA process | |
| Abstract | Advanced Data Analytics is a group of techniques that use data mining, modelling and statistical analysis of historical and / or near-current data to understand relationships, forecast future events and model "What if" scenarios. Data analytics has been used in a wide variety of industries but not extensively in the GDNs. Under a previous IFI / NIA project NGN has confirmed that advanced analytical techniques may have the opportunity to deliver benefits for the environment, customers, GDNs and other stakeholders. A strategy / roadmap has been developed, a group of potential opportunities covering a wide range of business activities has been identified and an exercise has been carried out to "deep dive" the available data and confirm, for the areas under consideration, that it is likely to be suitable for analytical techniques. The remaining problem to be addressed is how to engage with external organisations, identify and develop models using real data, assess their likely benefits and transfer knowledge and learning which can then be shared with the other GDNs to allow for the future beneficial application of structured data analytics within a GDN environment NGN will send detailed requests to a variety of external organisations and invite them to provide proposals detailing how they could work with us to develop analytical models, test their application and transfer knowledge & learning. The opportunities chosen cover a wide range of business activities and challenges common to all GDNs and include: -Vehicle replacement strategy (objective to develop an optimised asset investment strategy (opex / capex) taking account of criticality and required reliability, targeted at individual or groups of assets)Job scheduling (objective to develop geographically detailed short term forecasting of calls requiring 1 or 2 hour responses to optimise resource allocation between planned and emergency works)Leakage strategy (objective to develop an optimisation model that maximises leakage reduction (as calculated by the Ofgem model) given the balance of mandatory and discretionary investments in the network)Customer Complaints (purpose is to develop a model that objectively customer experience - both positive and negative - to specific drivers within and outside the control of the network and supports the development of strategies that maximise positive and minimise negative customer experiences)Pipe replacement (objective to develop specific asset performance and failure models to support the development of pipe replacement strategies)Note : Project Documents may be available via the ENA Smarter Networks Portal using the Website link above | |
| Publications | (none) |
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| Final Report | (none) |
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| Added to Database | 26/10/18 | |
