{"id":1178,"date":"2020-09-23T13:19:24","date_gmt":"2020-09-23T12:19:24","guid":{"rendered":"https:\/\/research.reading.ac.uk\/sbe-news-and-events\/?p=1178"},"modified":"2020-09-23T13:19:24","modified_gmt":"2020-09-23T12:19:24","slug":"using-machine-learning-and-deep-learning-for-energy-forecasting-with-matlab-free-seminar","status":"publish","type":"post","link":"https:\/\/research.reading.ac.uk\/sbe-news-and-events\/using-machine-learning-and-deep-learning-for-energy-forecasting-with-matlab-free-seminar\/","title":{"rendered":"Using Machine Learning and Deep Learning for Energy Forecasting with MATLAB – free seminar"},"content":{"rendered":"

Using Machine Learning and Deep Learning for Energy Forecasting with MATLAB<\/p>\n

29th September 2020 from 14:00-15:00 CEST<\/p>\n

Overview
\n<\/strong>AI, or Artificial Intelligence, is powering a massive shift in technical organizations\u00a0that\u00a0expect to gain or strengthen their competitive advantage. AI workflows such as deep learning and machine learning are transforming industries with high impact; the power and\u00a0utilities industries are not exceptional\u00a0in this regard. The legacy power grid is\u00a0adopting the concept of smart grid\u00a0technology,\u00a0where the role of AI is crucial\u00a0in multiple aspects.\u00a0\u00a0Grid analytics is one of\u00a0the\u00a0key focus areas of smart grid infrastructure, where load forecasting is highly pronounced. Forecasting the load on grid helps power and utility companies to plan their resources and effectively service consumer demands in a profitable way.\u00a0Medium-term forecasting and long-term forecasting are a key focus of the energy production and utilities industry,\u00a0as\u00a0this\u00a0helps to decide on multiple strategies such as generation planning and demand side management services.<\/p>\n

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The load on the grid depends on multiple external factors,\u00a0making the data highly complex in nature. AI can be thought of as a tool to develop the forecast models for this complex data. At\u00a0present, domain experts spend valuable efforts in cleaning data, searching\u00a0for the\u00a0right choice of predictive algorithms and fixing\u00a0syntax\u00a0of the code. Manual deployment of developed models is also a cumbersome process and additionally requires IT expertise.<\/p>\n

In this webinar, you will learn:<\/p>\n