{"schema_version":"1.0","canonical_url":"https://patentable.app/patents/US-11532056","patent":{"patent_number":"US-11532056","title":"Deep convolutional neural network based anomaly detection for transactive energy systems","assignee":null,"inventors":[],"filing_date":"2018-06-19T00:00:00.000Z","publication_date":"2022-12-20T00:00:00.000Z","cpc_codes":["G06Q","G01W","G06F","G06N","G06N","G06Q","G06Q","H02J","H02J","H02J","H02J","H02J","H02J"],"num_claims":20,"abstract":"A computer-implemented method for power grid anomaly detection using a convolutional neural network (CNN) trained to detect anomalies in electricity demand data and electricity supply data includes receiving (i) electricity demand data comprising time series measurements of consumption of electricity by a plurality of consumers, and (ii) electricity supply data comprising time series measurements of availability of electricity by one or more producers. An input matrix is generated that comprises the electricity demand data and the electricity supply data. The CNN is applied to the input matrix to yield a probability of anomaly in the electricity demand data and the electricity supply data. If the probability of anomaly is above a threshold value, an alert message is generated for one or more system operators."},"analysis":{"summary":null,"layman_explanation":null,"technical_analysis":null,"business_analysis":null,"faqs":null,"topics":[],"tech_cluster":null},"seo":{"title":"Deep convolutional neural network based anomaly detection for transactive energy systems","description":"A computer-implemented method for power grid anomaly detection using a convolutional neural network (CNN) trained to detect anomalies in electricity demand data and electricity supply data includes re","keywords":[]},"attribution":{"source":"Patentable","source_url":"https://patentable.app","canonical_url":"https://patentable.app/patents/US-11532056","license":"CC-BY-4.0-like","license_terms":"AI-generated analysis on this page (summary, layman_explanation, technical_analysis, business_analysis, faqs) may be reused with attribution and a visible link back to the canonical URL above. Patent abstracts, claims, and bibliographic data are USPTO public domain.","required_link":"https://patentable.app/patents/US-11532056","citation_suggestion":"Patentable. \"Deep convolutional neural network based anomaly detection for transactive energy systems\" (US-11532056). https://patentable.app/patents/US-11532056","copyright_holder":"Nomic Interactive Technology LLC"},"links":{"html":"https://patentable.app/patents/US-11532056","json":"https://patentable.app/api/llm-context/US-11532056","site":"https://patentable.app","llms_txt":"https://patentable.app/llms.txt"},"generated_at":"2026-05-30T15:00:23.334Z"}