Utility Machine Learning & AI Solutions
Mosaic has deployed AI, ML, and advanced analytics for the following utilities use cases
Featured Utilities Clients
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AI changes everything from utilities’ customer engagement to streamlining operations
Artificial intelligence and machine learning are beginning to demonstrate some of their most impactful effects on every aspect of a utility’s business. Power, water, wind, and natural gas operators are increasingly leaning on AI to inform their operational & strategic decisions.
The Utility Analytics Institute conducted a survey of 42 major utilities focused on an assessment of the analytics maturity taking place within the utility. UAI asked the utilities to rate their company’s ability to process data with the tools and technologies available to them. After reviewing the results, UAI said, “In terms of whether analytics toolsets meet enterprise needs, most responses fell in the poor/limited and good categories.” Clearly, there is a need for user-friendly methods for managing data.
Mosaic can be that data science partner to help you ascend the analytics maturity curve quickly. Whether you need support identifying quick win opportunities, executing on those opportunities, or raising the organizational level of comfort around AI or ML – Mosaic has you covered. After working for several customers in the utility world, we feel very confident in being able to design & deploy effective utility machine learning solutions & that bring value to any area of the business.
Our data science team is adept at developing machine learning models across the advanced analytics spectrum, especially for utilities and energy companies. We have worked multiple client engagements where we develop a suite of tools and capabilities that facilitate ongoing learning from data to bring continuous value through machine learning and artificial intelligence. Additionally, Mosaic excels at innovation, working collegially and interactively with our utility clients to build innovative solutions. This expertise and comfort with thoughtfully applying the tools and techniques of data science to varied and unique business needs set us apart.
Working together, we have succeeded in developing and disseminating new and advanced approaches, tools and models across large utilities and oil & gas firms. Some of our energy and utility clients include Exelon, Alliant Energy, LG&E, PPL, Spire Energy, Suez, Suburban Propane, and so we demonstrate breadth and depth across electricity, water, and natural gas distribution.
Utilities Solution Sheet
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Utility Success Stories
Automating Asset Recognition & Inspection with Computer Vision
Computer vision offers significant cost savings by way of scalability and automation. Collecting a large set of image data is only the first step. The true art of any AI application comes down to how a data scientist can tune AI to accurately label what is in the image set. Utility organizations can tune AI to automatically identify infrastructure defects, security threats or weather events as it relates to their operations.
Reducing Greenhouse Gas Emissions with Data Science
Recent initiatives by leaders across all industries have brought numerous new commitments to zero carbon emission goals and an accompanying surge in construction of wind and solar generation over the next few decades. Not surprisingly, machine learning & advanced analytics can play a large role in assisting utilities and their customers with insights and recommendations on meeting these targets.
Probabilistic Electricity Consumption Forecasting
Machine learning provides an excellent avenue for predicting future energy consumption. Accurate insights can provide critical insights into variables affecting the demand, providing decision-makers with an opportunity to address these levers. Forecasts also provide a benchmark to identify anomalous behavior, either high/low consumption, and alert managers to faults within the building.
Voltage Anomaly Detection
Like any energy company, they work to keep their electricity grid running optimally while meeting sustainability and customer satisfaction objectives. They wanted to leverage data generated by their smart meters to identify voltage anomalies occurring in their network to efficiently route the correct resources to address these problems. The company had collected quite a bit of sensor information but required a data science partner to turn this raw data into actionable information that their distribution engineering team could consume and respond to in real time.