人工智能将成为可再生能源增长的关键

据油价网1月9日消息称，石油和天然气的数字化已经得到了很好的证明，几乎所有的能源巨头都采用了人工智能、机器学习和其他创新技术来改善他们的运营但人工智能在可再生能源中扮演什么角色呢?就像在石油和天然气领域一样，人工智能也被用于风能、太阳能和其他绿色能源项目，通过提高自动化程度来提高效率。随着能源公司寻求更大程度上的数字化运营，人工智能可能会在未来的能源转型中发挥主导作用。人工智能的使用可以支持整个能源行业的众多活动，支持从化石燃料到可再生能源的所有能源的运营。近年来，能源行业采用人工智能技术来支持自动化决策和辅助决策。第一种是计算机系统自主处理信息，无需人工输入。这通常意味着任务可以比需要人工决策时更快更有效地完成，因为机器可以立即做出改变。然而，有些问题需要更多的人力投入来确定正确的反应;在这种情况下，辅助决策是有用的。机器可以提供有用的见解，为工作人员提供数据，以解释和决定在任何给定的情况下采取正确的行动。

人工智能在预测方面也发挥着重要作用。复杂算法的使用可以帮助投资者确定一个新的绿色能源项目所涉及的风险水平，预测不同条件下不同类型的可再生能源的产量，并预测不同地点一天中不同时间的能源需求。技术提供持续的监测和评估，通过预测潜在的挑战并立即应对，可以帮助公司防止故障或停止运营。例如，使用机器学习天气模型、历史数据集和实时本地天气信息可以帮助公司预测风暴或热浪何时袭来，以调整其运营，为天气变化做好准备。

随着数字化的普及，能源公司现在在日常运营中使用人工智能技术，这种类型的设备几乎肯定会成为改变能源未来的关键。人工智能支持从化石燃料向更环保替代品有效过渡的主要方式之一是通过网格管理。人工智能和机器学习使用数据分析来估计任何特定地区家庭的能源消耗水平。它考虑了各种因素，如一年中的时间、高峰和非高峰时间以及天气条件。这可以帮助能源公司不断了解未来几天可能的用电量，相应地管理电网，避免停电。生产也可以根据使用预测进行调整，以满足需求并避免浪费。

人工智能技术在不同能源运营领域的推广也可以显著提高维护实践。机器可以预测维护需求，在停电之前安排维修，以避免不必要的电力损失。能源公司可以为维修做好准备，并通知消费者，而不是突然出现故障，这意味着更长的维修时间和客户的意外停电。

在太阳能发电方面，人工智能可以根据日照时间和强度来确定建造太阳能发电场的最佳地点。它还可以帮助操作员规划站点的布局，以便太阳能系统捕捉到最多的阳光。一旦投入使用，人工智能技术可以用于自动化决策，以控制太阳能电池板，因为它们全天都在朝着阳光旋转。

就连太阳能人工智能公司Glint solar的联合创始人兼首席运营官J. Kvelland也解释说：“对我们来说，令人惊讶的是，有这么多非常老练的太阳能开发商仍在使用旧的土地采购方式——被动地等待别人推荐一块土地，或者通过观察谷歌地球来猜测。”他补充说：“考虑到几乎所有开发商都有雄心勃勃的计划，他们越来越必须积极主动地进行网站筛选，我们很自豪最终为他们提供了这项重要任务的软件。”

在风力发电方面，丹麦可再生能源巨头维斯塔斯风力系统公司在风电场数字化方面处于领先地位，利用机器学习不断适应和改进运营。现场人工智能技术主要通过反复试验从环境中实时学习，以创造变化以提高风能生产。

世界经济论坛能源和材料基准测试项目负责Espen Mehlum表示：“你可以使用人工智能来优化风电场的建设、选址和运营，但更重要的是，你可以使用人工智能来优化不同的系统，无论是在消费方面还是在生产方面。”他补充说：“这就是人工智能巨大的未开发潜力所在——我们只是触及了表面，看到了第一个用例。”

能源行业的数字化正在顺利进行，几乎所有的石油和天然气以及可再生能源巨头都将广泛的创新技术纳入其运营中，以提高效率和生产稳定性。人工智能技术使能源公司能够预测一系列场景，确保消费者的可靠能源输出，支持电网效率，并适应预期和实时变化，为生产创造最佳条件。

曹海斌 摘译自 油价网

原文如下：

Artificial Intelligence Will Be Critical For Renewable Energy Growth

The digitalization of oil and gas has been well documented, with pretty much all energy majors adopting AI, machine learning, and other innovative technologies to improve their operations. But what role does artificial intelligence play in renewables? Just as in oil and gas, AI is being adopted for use in wind, solar, and other green energy projects to improve efficiency through greater automation. As energy firms look to digitalize their operations to a greater extent, AI will likely play a leading role in the energy transition of the future. The use of AI can support numerous activities across the energy industry, for operations across all energy sources, from fossil fuels to renewables. The energy industry has adopted AI technology in recent years to support automated decision-making and aided decision-making. The first is when computer systems process information autonomously, without human input. This often means that tasks can be completed faster and more efficiently than when a human decision is required, as the machine can make an immediate change. However, some issues require greater human input to determine the correct response; in this case, aided decision-making can be useful. Machines can provide useful insights by providing data for workers to interpret and decide on the right actions to take in any given situation. 

AI also plays a major role in prediction. The use of complex algorithms can help investors to determine the level of risk involved in a new green energy project, anticipate the energy production from different types of renewable sources in different conditions, and predict the energy demand at different times of the day in various locations. Technology providing constant monitoring and evaluation can help companies prevent failures or the need to halt operations, by anticipating potential challenges and responding to them immediately. For example, using machine learning weather models, historical datasets, and real-time local weather information can help companies to predict when a storm or heatwave is going to hit to adapt their operations to prepare for the change in weather.

With digitalization becoming commonplace, energy firms are now using AI technologies in their day-to-day operations, and this type of equipment will almost certainly be key to transforming the future of energy. One of the main ways in which AI will support an effective transition away from fossil fuels to greener alternatives is through grid management. AI and machine learning use data analytics to estimate the level of energy consumption across households in any given area. It considers a variety of factors such as time of year, peak and off-peak times, and weather conditions. This can help energy companies to be constantly aware of the likely electricity use in the coming days, manage the grid accordingly and avoid outages. Production can also be altered in response to usage predictions to meet demand and avoid waste.

The rollout of AI technology across different areas of energy operations can also significantly enhance maintenance practices. Machines can predict the need for maintenance to schedule a repair ahead of an outage, to avoid an unnecessary loss of power. Energy companies can prepare for maintenance and inform consumers, rather than be caught unexpectedly by something breaking, which would mean longer repair times and unexpected power cuts for customers. 

When it comes to solar power, AI can be used to determine the best sites to construct solar farms, based on the hours of sunlight and intensity. It can also help operators to plan the layout of the site so that solar systems catch the most sunlight. once operational, AI technology can be used for automated decision-making to control solar panels as they rotate toward the sunlight throughout the day. 

Even J. Kvelland, the co-founder and COO of solar AI company Glint Solar, explained: “To us, it’s surprising how many very sophisticated solar developers are still using the old way of sourcing land: reactively waiting for someone to recommend a piece of land or guessing by looking at Google Earth.” He added, “Given how ambitious plans virtually all developers have, they increasingly must be proactive about site screening and we’re proud to finally offer them software for this important task.”

In terms of wind power, Danish renewable energy major Vestas Wind Systems has led the way in the digitalization of wind farms, using machine learning to constantly adapt and improve operations. On-site AI technology learns from the environment in real-time, mainly through trial and error, to create changes to enhance wind energy production. 

Espen Mehlum, the head of the energy and materials program on benchmarking at the World Economic Forum stated, “You can use AI to both optimize the construction, siting and the operations of a wind farm, but more importantly, you can use AI to optimize across different systems, both when it comes to consumption but also production.” He added, “That’s where the huge untapped potential is for AI – we’re just scratching the surface and seeing the first use cases.”

The digitalization of the energy sector is well underway, with almost all oil and gas and renewables majors incorporating a wide range of innovative technologies into their operations, for greater efficiency and production stability. AI technologies allow energy companies to predict a range of scenarios, ensure a reliable energy output for consumers, support grid efficiency, and adapt to anticipated and real-time changes to establish optimal conditions for production.