There’s no shortage of data in the utility industry. What is in short supply are experts who have the insider knowledge to help utilities thrive in the future by unlocking the full potential of their data today.
As the only full-service SAP consulting and solutions company focused exclusively on utilities, Utegration can position your company to capitalize on advances in artificial intelligence, machine learning, smart grid technologies, IoT, and the future of customer experience.
May 4, 2019
This article was originally published in the Spring 2019 issue of What’s Next magazine, published by TMG Consulting.
May 4, 2019
Upgrading technology to align with corporate goals is imperative for forward-thinking utilities. In particular, utilities that implement an advanced metering infrastructure program are laying a strong foundation for their future, and in reality, making business transformation possible.
January 15, 2019
This article was originally published online on the Utility Analytics Institute site on January 15, 2019.
January 3, 2019
As a perfect wrap up to our first decade in business, Utegration has been recognized by CIO Applications magazine as one of the Top 10 SAP Solution Providers 2018.
August 8, 2018
Building energy forecasting has gained momentum with the increase of building energy efficiency research and solution development. Indeed, forecasting the global energy consumption of a building can play a pivotal role in the operations of the building. It provides an initial check for facility managers and building automation systems to mark any discrepancy between expected and actual energy use. Accurate energy consumption forecasts are also used by facility managers, utility companies and building commissioning projects to implement energy-saving policies and optimize the operations of chillers, boilers and energy storage systems.
August 2, 2018
Bad debt control can be considered a use case under the umbrella of revenue protection. It can result in hard dollar value, making it easier to show the benefits of the project. Different types of revenue protection projects, such as power theft, unaccounted energy, fraud, and bad debt require different methods to identify the issues. The majority of approaches are rule based detections, however, others use machine learning models. For example, a machine learning model can be used to generate credit risk scores for customers. The generated score can then be integrated to the operational process to help reduce bad debt. This use case demonstrates how machine learning can help a utilities company protect their revenue.