{"id":29036,"date":"2018-11-13T19:54:02","date_gmt":"2018-11-14T00:54:02","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/the-democratization-of-energy-how-machine-learning-is-giving-power-back-to-the-consumer\/"},"modified":"2018-11-13T19:54:02","modified_gmt":"2018-11-14T00:54:02","slug":"the-democratization-of-energy-how-machine-learning-is-empowering-both-the-consumer-and-the-utility","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/the-democratization-of-energy-how-machine-learning-is-empowering-both-the-consumer-and-the-utility\/","title":{"rendered":"The democratization of energy: How machine learning is empowering both the consumer and the utility"},"content":{"rendered":"

The call for innovation<\/strong><\/p>\n

For more than 100 years, the way we produce and consume electricity has largely remained the same. The need to innovate the power industry is being driven by multiple macroeconomic and social trends. The first is the call to a more sustainable and zero-carbon future, driven by the increased use of renewable energy, an intermittent resource. The second is the need to electrify more than one billion consumers globally who live without electricity [1]. Lastly, an increasingly digital economy will drive energy demand, which is expected to grow by 28% by 2040 [2]. Each of these trends requires energy providers to create a more balanced generation portfolio, ensure reliable power, and improve asset performance.<\/p>\n

These challenges also present unique opportunities. The convergence of technology with physical assets gives utilities unprecedented access to big data, creating an interconnected system that is in constant communication. The figure below represents the information flow diagram of the ecosystem described above.<\/p>\n

 <\/p>\n

\"\"<\/a>\u00a0Source: GE Power<\/em><\/p>\n

By combining digital and physical assets, utilities are able to monitor load profiles in the system in real-time, while simultaneously adjusting grid operations without any human intervention [3].<\/p>\n

Machine learning and digitization at Enel\u00a0<\/strong><\/p>\n

Machine learning allows for greater predictability, which helps Enel in two broad categories. The first is process improvement, primarily through an improved ability to monitor large industrial assets, which reduces operating costs. While Enel has created innovations in this regard on multiple fronts, some examples include:<\/p>\n