{"id":33313,"date":"2023-01-19T15:35:09","date_gmt":"2023-01-19T14:35:09","guid":{"rendered":"https:\/\/cesam.community\/?p=33313"},"modified":"2023-01-19T15:35:09","modified_gmt":"2023-01-19T14:35:09","slug":"industrial-big-data-practice-for-power-generation-plant-energy-efficiency-optimization-2","status":"publish","type":"post","link":"https:\/\/cesam.community\/fr\/2023\/01\/19\/industrial-big-data-practice-for-power-generation-plant-energy-efficiency-optimization-2\/","title":{"rendered":"Industrial Big Data Practice for Power Generation Plant Energy Efficiency Optimization"},"content":{"rendered":"

Industrial Big Data Practice for Power Generation Plant Energy Efficiency Optimization<\/h1>

Speakers:
\n<\/strong>Hui Shen & Fang Hou – Accenture<\/strong><\/p>\n

Abstract:
\n<\/strong>In recently practice for an aged China coal-fired power plant, a systematic framework applying big data methods for energy efficiency optimization was proposed. To discover how big data methods could add unique and differentiated value by comparison to traditional analytic methods used for years in power generation industry, three specific topics were identified as pilot showcase. The business results proved that big data analytic methods could be applied not only in discrete consumer products industry, but also in complex process industry and have great potential to bring huge value. However, the availability of high quality data, capability of extended data capturing and continuous accumulation of historical data are key barriers to impact success of industrial big data analytics.<\/p>\n<\/div><\/div><\/div><\/div><\/section>

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To scroll through the slides use the toolbar at the bottom of the presentation.<\/em><\/span><\/p>\n

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In recently practice for an aged China coal-fired power plant, a systematic framework applying big data methods for energy efficiency optimization was proposed. To discover how big data methods could add unique and differentiated value by comparison to traditional analytic methods used for years in power generation industry, three specific topics were identified as pilot showcase. The business results proved that big data analytic methods could be applied not only in discrete consumer products industry, but also in complex process industry and have great potential to bring huge value. However, the availability of high quality data, capability of extended data capturing and continuous accumulation of historical data are key barriers to impact success of industrial big data analytics…<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[231,195],"tags":[181],"yoast_head":"\nPower Generation Plant Energy Efficiency Optimization<\/title>\n<meta name=\"description\" content=\"For an aged China coal-fired power plant, a systematic framework applying big data methods for energy efficiency optimization was proposed...\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/cesam.community\/fr\/2023\/01\/19\/industrial-big-data-practice-for-power-generation-plant-energy-efficiency-optimization-2\/\" \/>\n<meta property=\"og:locale\" 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