论文标题
部分可观测时空混沌系统的无模型预测
Sustainability using Renewable Electricity (SuRE) towards NetZero Emissions
论文作者
论文摘要
由于人口和经济增长的增加，全球对能源的需求大大增加。能源需求的增长会对环境构成严重威胁，因为大多数能源都是不可再生的，并且基于化石燃料，这导致有害温室气体的排放。世界各地的组织在从基于化石燃料的来源过渡到更绿色的来源以减少其碳足迹方面面临着挑战。作为实现净零排放目标的一步，我们提出了一个基于AI的可扩展解决方案，该解决方案可以被组织使用，以增加其总体可再生电力份额。我们的解决方案为设施提供了准确的能源需求预测，建议采购可再生电力，以优化成本和碳偏移建议，以补偿温室气体（GHG）排放。该解决方案已在生产中用于四个设施超过一年，并大大增加了可再生电力份额。
Demand for energy has increased significantly across the globe due to increase in population and economic growth. Growth in energy demand poses serious threat to the environment since majority of the energy sources are non-renewable and based on fossil fuels, which leads to emission of harmful greenhouse gases. Organizations across the world are facing challenges in transitioning from fossil fuels-based sources to greener sources to reduce their carbon footprint. As a step towards achieving Net-Zero emission target, we present a scalable AI based solution that can be used by organizations to increase their overall renewable electricity share in total energy consumption. Our solution provides facilities with accurate energy demand forecast, recommendation for procurement of renewable electricity to optimize cost and carbon offset recommendations to compensate for Greenhouse Gas (GHG) emissions. This solution has been used in production for more than a year for four facilities and has increased their renewable electricity share significantly.
