C3.ai has been granted a patent for a method to detect non-technical energy loss using machine learning. The method involves generating multidimensional representations of energy use conditions, performing clustering, and outputting information on meters associated with non-technical loss. GlobalData’s report on C3.ai gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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According to GlobalData’s company profile on C3.ai, AI for workflow management was a key innovation area identified from patents. C3.ai's grant share as of January 2024 was 22%. Grant share is based on the ratio of number of grants to total number of patents.
Detecting non-technical energy loss using machine learning clustering
A recently granted patent (Publication Number: US11886843B2) outlines a method for detecting non-technical energy loss through the use of machine learning algorithms. The method involves generating multidimensional representations of energy use conditions based on feature values extracted from energy data associated with meters. By performing an unsupervised clustering process, the system can identify clusters of energy use conditions and predict meters that may be associated with non-technical energy loss. The system can also rank meters based on the likelihood of energy loss, providing a user interface with valuable information for energy providers.
Furthermore, the patent describes a system that includes memory and processors to execute the method for detecting non-technical energy loss. The system can analyze data from various sources such as AMI systems, customer information, billing data, and more to identify irregular consumption patterns indicative of energy loss. By utilizing machine learning classifiers like neural networks or decision trees, the system can continuously adapt to new energy conditions and provide real-time insights to energy providers. The system's ability to identify outlier clusters and analyze feature values related to energy consumption, disconnections, and malfunctions enhances its capability to detect non-technical energy loss effectively, offering a comprehensive solution for energy management.
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