The Infosys Utilities Blog seeks to discuss and answer the industry’s burning Smart Grid questions through the commentary of the industry’s leading Smart Grid and Sustainability experts. This blogging community offers a rich source of fresh new ideas on the planning, design and implementation of solutions for the utility industry of tomorrow.

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The Utilities Data Dilemma

Increasingly utilities are being directed to big data, and all the benefits that appears to offer. However such calls miss a fundamental issue, in that asset data is an expensive element for utilities, both to obtain and to maintain. Most utility physical assets are geographically widely spaced, sometimes in locations difficult to access. Costs can be quite high, for example a manhole survey can average >$100. The EPA estimates 12 million municipal manholes in the US, so a 5% validation survey would cost circa $60 million! Surveys can also have complex health and safety risks that need to be managed. For these reasons asset data is often limited, and of dubious quality. Sensors and instrumentation are improving, being both cheaper to install, run and maintain, and more robust, nonetheless they are still relatively expensive items.

With asset data being limited, suspect, and costly to improve, and sensors and instrumentation expensive to deploy, smarter utilities are looking to make better use of the information they already hold. By using a combination of engineering knowledge coupled with effective analytics, trends can be mapped and normal asset behaviour determined. Where data is readily available such analysis is relatively simple, however where asset data is limited engineering knowledge and understanding can be used to define relationships between the seemingly unrelated data sets. The key is in understanding how data sources can be meaningfully linked.

Large Business Information systems may thus be of limited value to utilities in terms of managing their assets. Of more value is the effective linking of dispersed data sources, coupled with an effective, easily configurable analytics engine. Such tools have already been used to answer many asset related questions, such as the viability of rainwater harvesting in differing regions and climates. It is indeed possible to answer many of the asset related questions posed by utilities, even with the limited asset data many hold. Each question is however individual to the specific situation, so only those who can understand both the engineering and system elements will be able to successfully deliver beneficial results.

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