Clir和SINAI科技利用大数据对抗气候变化

来源:至顶网CIO与CTO频道    2021-07-30 09:30:35

关键字: 人工智能 大数据

没有多少人会想到“大数据”或“人工智能”,而最近的几次谈话让我相信大数据和人工智能领域在这方面的重要性,二者可以帮助我们的文明蓬勃发展及帮助其在下一个世纪得以生存。

When you think about solving the climate crisis, what springs to mind?

你想到要解决气候危机时会想到什么呢?

Most people’s knee-jerk reaction is along the lines of “electrification,” “carbon sequestration,” “recycling,” or “renewable agriculture.”

大多数人的本能反应是类似“电气化”、“碳封存”、”回收”或“可再生农业”等等。

While not many think of phrases like “big data” or “artificial intelligence,” several recent conversations have convinced me how important these fields are to helping our civilization thrive and survive into the next century.

没有多少人会想到“大数据”或“人工智能”,而最近的几次谈话让我相信大数据和人工智能领域在这方面的重要性,二者可以帮助我们的文明蓬勃发展及帮助其在下一个世纪得以生存。

The two founder / CEOs with whom I have had the pleasure to speak recently use AI in very different ways and in completely different fields, but it is clear that the ubiquity of cheap computing power, combined with smart engineers and focused, visionary entrepreneurs represents a formidable force in helping us mitigate and adapt to today’s harsher, more challenging post-climate world.

笔者最近有幸与两位创始人、CEO交谈,两位以非常不同的方式在完全不同的领域应用人工智能,但很明显,廉价的计算能力无处不在,再加上聪明的工程师以及专注、有远见的企业家,这些因素代表了一种强大的力量,可以帮助我们缓解气候挑战和适应当今更严酷、更具挑战性的后气候世界。

The companies featured in this article are Clir and SINAI Technologies.

本文要介绍的公司是Clir和SINAI科技。

Clir

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PALM SPRINGS, CALIFORNIA - FEBRUARY 27, 2019: Wind turbines generate electricity at the San Gorgonio ... [+] Pass Wind Farm near Palm Springs, California, as a dust storm blows through the area. Located in the windy gap between Southern California's two highest mountains, the facility is one of three major wind farms in California. (Photo by Robert Alexander/Getty Images)

Everyone knows that one big downside of renewable energy (RE) generation is intermittency.

大家都知道可再生能源(RE)发电的一个大缺点是其间歇性。

While the grid can cushion some of the ill-effects of intermittency through large-scale battery installations, varying production levels inevitably add uncertainty to the operation of RE facilities.

虽然电网可以利用大规模电池装置减缓间歇性的一些不良影响,但不同的生产水平不可避免地会给可再生能源设施的运行增加不确定性。

Uncertainty has negative consequences for plant operators and grid managers (who must instantaneously match power supply to power demand), but it also has negative consequences for financiers of renewable energy projects.

不确定性对电厂经营者及电网管理者(他们必须实时匹配电力供应和电力需求)有一些负面影响,但不确定性对可再生能源项目的金融家也会有负面影响。

Owners are hesitant to take on leverage (i.e., borrow money) which would otherwise improve equity returns. Banks tend to charge higher interest rates and insurers higher premiums to make up for added operating risk. M&A deal flow slows because potential RE asset acquirers are not sure how stable the facility’s cash flows will be.

这会导致资产所有者在面对杠杆作用(即借钱)时犹豫不决,会影响股权收益的提高。银行则会倾向于收取更高的利率,保险公司倾向于收取更高的保险费,借以弥补增加的经营风险。并购交易流会放缓,因为潜在的可再生能源资产收购者不确定该设施的现金流能有多稳定。

One start-up, Clir, has been using innovations in data science and artificial intelligence to understand operational drivers at renewable energy plants, then leveraging that understanding to improve plant efficiency and decrease uncertainty. By doing so, Clir can bring down the cost of capital for clean electricity generators — making RE facilities more attractive investment assets.

初创公司Clir一直都在利用数据科学和人工智能的创新了解可再生能源工厂的运营驱动因素,Clir就是要利用获得的专业知识提高工厂的效率并减少不确定性。通过这种手段,Clir可以降低清洁发电商的资本成本,令可再生能源设施成为更具吸引力的投资资产。

Clir consolidates data from all the units in a wind farm or solar array – a truly enormous amount of data – and runs that data through machine learning algorithms to get a picture of how the facility operates over time and in different environmental conditions.

Clir整合了来自风电场或太阳能阵列各单元的数据——真的是海量数据,再用机器学习算法处理这些数据,达到了解设施在不同时间和环境条件下运行情况的目的。

Clir’s AI then identifies ways to maximize the overall efficiency of the facility in ways that CEO Gareth Brown says might sometimes seem counter intuitive.

Clir人工智能接着会找出如何使设施的整体效率最大化的各种方法,首席执行官Gareth Brown表示这些方法有时可能看起来有点反直觉。

For example, for offshore wind farms or those in the middle of large US deserts, there is not much mixing between air at different elevations. In these low-mix cases, efficiency for the farm overall increases when the leading turbines are set to operate at _less_ than peak efficiency.

例如,对于一些海上风电场或位于美国大沙漠中间的风电场而言,不同海拔的空气之间并没有太多的混合。在这样的低混合情况下,领头的涡轮机应设置为以低于峰值的效率运行,如此可提高风电场的整体效率。

The wind left un-churned by the leading, wake-creating turbines hits the blades of the trailing, wake-affected turbines with greater force, generating more power. The power generated by the wake-affected turbines more than offsets the reduction in power from the wake-creating ones. According to academic research, this process, known as “wake steering,” can increase the power generated by around 10% and, more importantly, decreases the variability of the power generated by around 70%.

由于那些没被领头的、产生尾流的涡轮机搅动的风能够以更大的力量击打后面的受尾流影响涡轮机的叶片上,进而能产生更多的功率。受尾流影响的涡轮机所产生的功率会超出产生尾流的涡轮机减少的功率。学术研究称这个过程为 “尾流转向”,尾流转向可以增加约10%的发电量,更重要的是,尾流转向可以减少约70%的发电量不确定性。

A 70% decrease of uncertainty represents a big win for asset owners and the financiers that back them. With operational uncertainty decreased, banks and insurers can better assess the potential risks and returns, and price their financial products more appropriately. Investors looking to acquire renewable energy assets also have a better idea what a fair price to pay is.

减少70%的不确定性对资产所有者和支持他们的金融家来说是一个巨大的胜利。运营不确定性的减少了,银行和保险公司就可以更好地评估潜在的风险和回报并为自己的金融产品进行更适当的定价。而寻求收购可再生能源资产的投资者也就可以更好地知道什么是公平的价格。

The main premise behind this column is that — insofar as it represents the economic manifestation of humanity’s ability to adapt — capitalism is an irreplaceable tool for fighting climate change. Capitalism routes money toward the most successful ideas, so to the extent that Clir’s AI is helping investors make sound capital allocation decisions, it is at the tip of the spear in civilization’s transition to a renewable energy future.

本专栏的主要前提:资本主义既然代表了人类适应能力的经济表现,那它就是对抗气候变化的一个不可替代的工具。资本主义可以将资金导向最成功的想法,因此,由于Clir人工智能够帮助投资者做出合理的资本分配决策,从这一点出发,可以说Clir就是文明向可再生能源未来过渡的尖兵。

SINAI科技

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For years, company managers had to concern themselves with a single, straightforward task: maximize profits for the company’s owners. While not simple, this task was made a lot less tricky because governments did not know they should be charging companies for spewing greenhouse gases (GHG) into the atmosphere.

公司经理们多年来必须关注的任务颇为直接明了:为公司的所有者实现利润最大化。虽然这个任务不那么简单,但由于政府过去不知道本应该向公司征收温室气体(GHG)排放费用,这个任务相应就不那么地棘手。

Governments finally began to announce and implement carbon taxes over the last decade, but there is still no universal solution. Different country-level or regional carbon tax schemes force some companies in some industries to pay for some GHG emissions; coverage is spotty, and enforcement differs from one jurisdiction to another.

而在过去十年中,各国政府终于开始宣布和实施碳税,但仍然没有普遍的解决方案。不同的国家层面或地区层面的碳税计划迫使一些行业的公司要为温室气体排放付费;各地的实施参差不齐,而且各司法管辖区的执行情况也不同。

With recent announcements about border adjustment taxes in both the US and Europe, it looks like the two largest trading blocks are finally on the verge of forcing companies to “internalize” the costs associated with GHG waste.

美国和欧洲最近宣布了征收边境调节税,两个最大的贸易区看起来终于快要逼着公司去“内部消化”与温室气体排放有关的成本了。

While carbon border taxes would be undiluted good news for enthusiasts of life as we know it, the imposition of these taxes is forcing companies to completely rethink their capital spending and operational planning. Large companies are moving toward instituting internal carbon pricing (e.g., Microsoft, Danone), by which divisional and firm-wide profits are adjusted by an assumed cost of GHG emissions.

我们知道,碳边境税对于生活爱好者来说是不折不扣的好消息,但征收这些税项则会迫使公司完全重新考虑他们的资本支出和运营规划。大公司正朝着建立内部碳定价的方向发展(例如微软、达能),在内部碳定价的模式下,各部门和公司范围内的利润将根据设定的温室气体排放成本进行调整。

Corporate planning centered on internal carbon pricing is not a trivial task. Corporate accounting and planning systems were not set up to measure or report these costs, so just gathering the data is challenging and highly manual. Companies spend big bucks on armies of consultants to pull together ad hoc spreadsheets to try to gather the required data so that simple go / no-go kinds of decisions can be made.

以内部碳定价为中心的公司规划并非一项小任务。公司的财会和规划系统本来不是为衡量或报告这些成本而设立的,所以仅仅要收集数据都具有挑战性,收集数据而且还是高度手动的。公司花巨资聘请顾问,将临时性的电子表格整到一起,目的是试图收集所需的数据,以便做出简单的“放行”或“不放行”的决定。

One start-up – San Francisco-based SINAI Technologies, founded by Maria Fujihara (CEO) and Alain Rodriguez (CTO) – is aiming to change these manual processes and bring the field of de-carbonization into the 21st century.

旧金山的初创公司SINAI科技公司是由Maria Fujihara (CEO) 和Alain Rodriguez (CTO)创立的。 SINAI科技旨在改变这些人工流程以及引领去碳化领域进入21世纪。

SINAI designs automation routines to compile emissions data from different corporate divisions into a common data store, so that the firm’s overall emission profile can be accurately assessed.

SINAI设计的自动化程序可以将来自不同公司部门的排放数据进行编译后储存到一个共同的数据存储中,进而准确地评估公司的整体排放状况。

In addition to internal company data, SINAI gathers regional electrical generation data and information from suppliers to make estimates of a company’s baseline Scope I, II, and II GHG emissions.* This allows SINAI’s clients a holistic view of their overall GHG footprint and insight into what part of the supply chain needs the most mitigation work.

SINAI除了公司的内部数据以外还收集区域发电数据和供应商的信息,用于估计公司的基准范围I、II和II温室气体(Scope I, II, and II GHG emissions.)排放量(有关范围I、II和II温室气体排放量的定义请参看相应的资料)。SINAI的客户因此能够全面了解自己的整体温室气体足迹及更深入地了解供应链的哪一部分最需要进行减排。

After data is collected and the baseline created, SINAI’s systems use artificial intelligence to forecast emission levels under different mitigation scenarios. Decarbonization-related capital spending plans can be made by comparing the mitigation effects of different technology implementations along different parts of the supply chain.

SINAI的系统在收集数据和创建基准后接着会利用人工智能预测不同减排方案下的排放水平。通过比较供应链上不同部分及不同技术实施的减排效果就可以制定与脱碳相关的资本支出计划。

The planning process made possible by SINAI’s technology allows companies to make intelligent strategic decisions regarding what decarbonization projects they should focus on to get the biggest bang for the buck. Leveraging tools like this is vital if companies are to make the enormous changes necessary to adapt to our post-warming world.

SINAI的技术用于规划过程使得公司可以了解应该关注哪些脱碳项目以获得最大的收益,进而令公司作出明智的战略决策。各公司要适应我们的后暖化世界就得进行必要的巨大变革,而有效地利用这样的工具则是至关重要的。

Clir’s Brown and SINAI’s Fujihara and Rodriguez know, as I know, that in order to meet the challenges of the 21st century, we need to pull out all the stops and use all the tools in our toolkit — including big data analysis and AI.

Clir公司的Brown和SINAI公司的Fujihara和Rodriguez以及笔者都知道,为了迎接21世纪的挑战,我们需要使出浑身解数及用上我们工具包中的所有工具——包括大数据分析和人工智能。

Intelligent investors take note.

聪明的投资者务必注意。

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