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Utility Analytics with Data Quality
Data analytics is relatively not new to the electric utility world. Advanced analytics drive the organizations to new frontiers of efficiencies, and with the right approach, it can deliver enormous value to the utilities. Utilities are relying on a wealth of opportunities from data analytics, with huge data flowing from smart meters and other sensors along with traditional sources of data. Across many industries, including energy and utilities, data is considered as the new renewable resource.
Technologies like Phasor Measurement Units (PMUs) help in estimating the magnitude and phase angle of an electrical phasor using a common time source of synchronization. Distribution automation device deployment is rapidly growing and bringing huge amounts of data into the utilities.
This is a graphical method of organizing and displaying the necessary information. There are many types of data distributions like dot plots, histograms, box plots, and tally charts. The customer consumption data—produces more than 35000 data points a year based on a 15-minute reading breaks per customer. Asset data utilities produce large amounts of health data commercially into core systems like asset health centers. Utilities are utilizing GIS (Global information system) to store and correlate the asset characteristics along with geospatial rendering. However, for the utility industry, the analytics are still nascent and need to define where the sources of value are and what services to provide to the customers, regulators, and shareholders.
Apart from the analytics like the customer (meter and beyond meter), operational (real time, near real time. and historical), and asset (financial, health, and device-experience data), there is other performance data that improve supply-chain decisions and restoration predictions. This is an inefficient approach as it restricts the users to a subset of analytics applications and mitigates the crossing analytics from extracting greater value from the related information.
Predictive asset management, asset utilization, and power-quality issues are the analytics that drastically impacts utility transformation. Data analytics provide the basis for probabilistic models and scenario-based planning approaches that fundamentally change the nature of planning. For example, in asset investment planning, by integrating data sources automatically and embedding the institutional knowledge into analytic models, the underutilized assets are more easily identified. This provides a more detailed basis for ranking investment projects and also makes planning easier by automating asset planning analysis.
Successful use of analytics requires great focus, having a clear view of what business processes they impact and improve. This focus helps to identify and prioritize the analytics most relevant to a utility’s goals.