{"id":35756,"date":"2018-11-13T19:33:45","date_gmt":"2018-11-14T00:33:45","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/the-future-of-energy-forecasting-the-weather\/"},"modified":"2018-11-13T19:45:16","modified_gmt":"2018-11-14T00:45:16","slug":"the-future-of-energy-forecasting-the-weather","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/the-future-of-energy-forecasting-the-weather\/","title":{"rendered":"The future of energy: forecasting the weather?"},"content":{"rendered":"

By 2050, the United States\u2019 electricity demand will be 40% greater than today\u2019s.\u00a0 This growth will rely heavily on the production of wind and solar power, which is expected to make up over 64%[i]<\/a> of the electrical grid supply.\u00a0\u00a0 To meet this demand, machine learning will be needed to optimally integrate power sources into the grid, predicting demand and source capacity, and to limit downtime through predictive maintenance.<\/p>\n

NextEra Energy Resources, is the world\u2019s largest supplier of wind and solar energy and has the largest battery storage capability[ii]<\/a> compared to other providers. Their subsidiary, Florida Power and Light (FPL), currently serves 10 million people at a cost 30% lower to consumers than the national average[iii]<\/a>.\u00a0 Part of this is through machine learning employed by their predictive analytics team that leverages real time equipment data to predict when reliability issues may occur.\u00a0 The machine learning algorithms detect anomalies in their critical equipment, such as combustion turbines, and schedules preventative maintenance in advance of potential failures, increasing production reliability[iv]<\/a>. Since 2006, they have installed over 5 million smart meters resulting in the meters being used by 99% of current customers[v]<\/a>. The data allowed FPL to proactively pinpoint which transformers need to be replaced, a 25% cost savings in comparison to unplanned replacements[vi]<\/a>.\u00a0 In addition, it gave customers the ability to view their consumption in real time and adjust their demand to optimize their usage when rates were cheaper[vii]<\/a>.\u00a0 In 2016, NextEra Energy Services partnered with Autogrid to offer the ControlComm platform. The platform uses machine learning algorithms to forecast demand and allows customers to opt into allowing automatic adjustment of their consumption rates to level demand[viii]<\/a>. \u00a0\u00a0Adjusting demand and raising reliability are not the whole picture when it comes to optimizing the grid.\u00a0 Demand will never equal full electrical production capacity and investments will need to be made to increase power supply capability.<\/p>\n

One of the challenges facing renewables is the inconsistency of their ability to supply power to the grid, due to changes in weather such as wind or cloud coverage and the lack of storage capability when weather conditions produce a surplus to demand.\u00a0 Currently the difference between renewable energy supply and consumption requirements is made up by conventional sources such as natural gas.\u00a0 Being able to predict patterns in weather to know when to turn on or off these reserve plants will allow NextEra to close the gap between reserve power production and demand.\u00a0 NextEra should consider expanding their energy demand predictive analytics to include data from \u201csatellites, climate models, and weather stations[ix]<\/a>\u201dto forecast energy supply from solar and wind power. \u00a0In addition, these predictions can be utilized to understand what areas may benefit from battery storage and utilize machine learning to limit cycling of the batteries to extend their useful lifetime[x]<\/a>.<\/p>\n

Going forward, the question is what will have the biggest impact on meeting energy demand through renewable energy: efforts focused on utilizing predictive analytics to modify customers\u2019 usage behaviors or efforts focused on improving energy storage options to store excess renewable energy?<\/p>\n

[i]<\/a> U.S. Energy Information Administration, \u201cAnnual Energy Outlook 2018 with projections to 2050,\u201d https:\/\/www.eia.gov\/outlooks\/aeo\/pdf\/AEO2018.pdf, accessed November 2018<\/p>\n

[ii]<\/a> NextEra Energy, Inc, \u201cRenewable Energy,\u201d http:\/\/www.nexteraenergy.com\/sustainability\/environment\/renewable-energy.html<\/a>, accessed November 2018<\/p>\n

[iii]<\/a>Florida Power and Light, \u201cCompany Pofile\u201d https:\/\/www.fpl.com\/about\/company-profile.html<\/a>, accessed Novemeber 2018<\/p>\n

[iv]<\/a> NextEra Energy, Inc, Energy Now \u201cPredicting the future of generation\u201d http:\/\/www.nexteraenergy.com\/energynow\/2017\/0117\/0117_PredictiveAnalytics.shtml<\/a>, January 31, 2017<\/p>\n

[v]<\/a> NextEra Energy, Inc,\u00a0 \u201c2018 EEI ESG Sustainability Reporting Template vF.pdf\u201d http:\/\/www.investor.nexteraenergy.com\/~\/media\/Files\/N\/NEE-IR\/reports-and-fillings\/sustainability-reports\/2018%20EEI%20ESG%20Sustainability%20Reporting%20Template%20vF.pdf<\/a>, accessed November 2018<\/p>\n

[vi]<\/a> LaMargo Sweezer-Fischer, \u201cBig Data: Using Smart Grid to Improve Operations and Reliability\u201d\u00a0 July 27-31 2014, IEEE Power & Energy Society General Meeting; Presentation, IEEE-PES website https:\/\/www.ieee-pes.org\/presentations\/gm2014\/FPL-IEEE-Presentation-Big-Data-July-2014.pdf<\/a>, accessed November 2018<\/p>\n

[vii]<\/a> [vii] NextEra Energy, Inc, \u201cEnergy Efficiency\u201d\u00a0 http:\/\/www.nexteraenergy.com\/sustainability\/customers\/energy-efficiency.html<\/a>, accessed November 2018<\/p>\n

[viii]<\/a> \u201cNextEra Energy Services Teams Up with AutoGrid to offer New Demand Response Programs in PJM\u201d press release, June 21, 2016, on Business Wire website, https:\/\/www.businesswire.com\/news\/home\/20160621006080\/en\/NextEra-Energy-Services-Teams-AutoGrid-offer-New<\/a> , accessed November 2018.<\/p>\n

[ix]<\/a> \u00a0Frost & Sullivan, Global Energy & Environment Research Team \u201cImpact of Artificial Intelligence (AI) on Energy and Utilities, 2018\u201d ME1F-14, September 2018<\/p>\n

[x]<\/a> Yongli Wang, Yujing Huang* , Yudong Wang, Ming Zeng, Fang Li, Yunlu Wang, Yuangyuan Zhang, \u201cEnergy management of smart micro-grid with response loads and distributed generation considering demand response,\u201d Journal of Cleaner Production, nos. 197, (2008): 1069-1083, Elsevier via Science Direct, accessed November 2018<\/p>\n","protected":false},"excerpt":{"rendered":"

NextEra:utilizing machine learning to increase reliability and integrate renewables into the grid<\/p>\n","protected":false},"author":11886,"featured_media":35783,"comment_status":"open","ping_status":"closed","template":"","categories":[955,86,3609,2677,2629,2112,1847],"class_list":["post-35756","hck-submission","type-hck-submission","status-publish","has-post-thumbnail","hentry","category-alternative-energy","category-electricity","category-electricity-usage","category-predictive-analytics","category-predictive-maintenance","category-solar-power","category-wind-power","hck-taxonomy-organization-nextera","hck-taxonomy-industry-utilities","hck-taxonomy-country-united-states"],"connected_submission_link":"https:\/\/d3.harvard.edu\/platform-rctom\/assignment\/rc-tom-challenge-2018\/","yoast_head":"\nThe future of energy: forecasting the weather? - Technology and Operations Management<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/the-future-of-energy-forecasting-the-weather\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"The future of energy: forecasting the weather? 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