Electricite de France has patented a method using convolutional neural networks to estimate energy production indicators in solar systems. The process involves analyzing wide-angle sky images captured by a device to predict energy output. The innovative approach combines convolutional and non-convolutional neural networks for accurate estimations. GlobalData’s report on Electricite de France 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 Electricite de France, Battery management systems was a key innovation area identified from patents. Electricite de France's grant share as of May 2024 was 66%. Grant share is based on the ratio of number of grants to total number of patents.
Estimating solar energy production using wide-angle image capture device
A recently granted patent (Publication Number: US12001938B2) outlines a method for estimating energy production indicators in a solar energy system using a combination of convolutional neural networks. The method involves receiving hemispherical sky images from a wide-angle image capture device, processing them through a series of convolutional and non-convolutional neural networks to estimate energy production indicators. The system utilizes recurrent neural networks, specifically Long Short-Term Memory (LSTM) networks, to predict energy production indicators based on processed images captured at different times.
Furthermore, the patent describes an estimation device equipped with a reception interface and a processor configured to process images through convolutional neural networks to estimate energy production indicators. The device utilizes recurrent neural networks to analyze image series and predict energy production indicators accurately. Additionally, the patent includes a system comprising a wide-angle image capture device that can communicate with the estimation device, enhancing the accuracy and efficiency of energy production estimation in solar energy systems. The method and device described in the patent aim to improve the monitoring and optimization of solar energy production by leveraging advanced neural network technologies for accurate estimation of energy production indicators.
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GlobalData Patent Analytics tracks bibliographic data, legal events data, point in time patent ownerships, and backward and forward citations from global patenting offices. Textual analysis and official patent classifications are used to group patents into key thematic areas and link them to specific companies across the world’s largest industries.
