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Utilities Business Review | Wednesday, April 16, 2025
FREMONT, CA: All decisions are made based on data in theutility industry. People in charge require good data to make decisions that impact reliability, efficiency, and safety. Utility asset data is important for making decisions regarding maintaining and working networks in emergencies. Artificial intelligence is an important technology as the company develops fresh solutions for the utility industry.
The fresh data quality program model is an innovative technology solution that supports utilities with huge asset datasets to get better data quality. The model enhances the quality of data and the speed with which it is examined. In addition, it lets utility companies build on their data foundations. Utility analytics focuses the dataset on training and testing the model as per clustering. Data clustering is the procedure of grouping or clustering records about their similarities.
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Provides specific information
The primary machine learning solution is first to find an issue to solve, gather data, use feature engineering, pick an algorithm, train, test, revise, and apply the solution. Its purpose is to change the data into a language the computer can understand while keeping the information in the data. Easy ways to do feature engineering are to regulate and clean the data with tools for data manipulation. Coding and storing the data in the spatial part of asset datasets is a challenge unique to the utility space. Information on geographic information systems (GIS) to deal with the issues of encoding spatial information is beneficial. It includes more dimensions to the dataset and provides the model with more information to learn from.
Future developments in computer vision applications
Using computer vision techniques to support solving problems with spatial analysis is essential. Computer vision is the process of teaching a model to identify objects or areas in image data and to do the same or better job than a person given the same task. In the utility industry, this is usually done with the help of satellite and aerial imagery. These computer vision techniques could support operations support, risk assessments, load forecasting, asset maintenance, and putting service areas into groups.
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