{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/convolutional-neural-networks-cnns-for-power","title":"Convolutional Neural Networks (CNNs) for power system big data analysis","arxiv_id":null,"date":"2017-09-17","proceeding":"2017 North American Power Symposium (NAPS) 2017 9","authors":["Siby Jose Plathottam","Hossein Salehfar","Prakash Ranganathan"],"abstract":"The concept of automated power system data analysis using Deep Neural Networks (as part of the routine tasks normally performed by Independent System Operators) is explored and developed in this paper. Specifically, we propose to use the widely-used Deep neural network architecture known as Convolutional Neural Networks (CNNs). To this end, a 2-D representation of power system data is developed and proposed. To show the relevance of the proposed concept, a multi-class multi-label classification problem is presented as an application example. Midcontinent ISO (MISO) data sets on wind power and load is used for this purpose. TensorFlow, an open source machine learning platform is used to construct the CNN and train the network. The results are discussed and compared with those from standard Feed Forward Networks for the same data.","url_abs":"https://ieeexplore.ieee.org/abstract/document/8107202","url_pdf":"https://ieeexplore.ieee.org/abstract/document/8107202","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"convolutional-neural-networks-cnns-for-power","repo_url":"https://github.com/sibyjackgrove/CNN-on-Wind-Power-Data","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"multi-label-classification-2","task_name":"MUlTI-LABEL-ClASSIFICATION"},{"task_slug":"multi-label-classification","task_name":"Multi-Label Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}