據油價網1月9日消息稱，石油和天然氣的數字化已經得到了很好的證明，幾乎所有的能源巨頭都采用了人工智能、機器學習和其他創新技術來改善他們的運營但人工智能在可再生能源中扮演什么角色呢?就像在石油和天然氣領域一樣，人工智能也被用于風能、太陽能和其他綠色能源項目，通過提高自動化程度來提高效率。隨著能源公司尋求更大程度上的數字化運營，人工智能可能會在未來的能源轉型中發揮主導作用。人工智能的使用可以支持整個能源行業的眾多活動，支持從化石燃料到可再生能源的所有能源的運營。近年來，能源行業采用人工智能技術來支持自動化決策和輔助決策。第一種是計算機系統自主處理信息，無需人工輸入。這通常意味著任務可以比需要人工決策時更快更有效地完成，因為機器可以立即做出改變。然而，有些問題需要更多的人力投入來確定正確的反應;在這種情況下，輔助決策是有用的。機器可以提供有用的見解，為工作人員提供數據，以解釋和決定在任何給定的情況下采取正確的行動。

人工智能在預測方面也發揮著重要作用。復雜算法的使用可以幫助投資者確定一個新的綠色能源項目所涉及的風險水平，預測不同條件下不同類型的可再生能源的產量，并預測不同地點一天中不同時間的能源需求。技術提供持續的監測和評估，通過預測潛在的挑戰并立即應對，可以幫助公司防止故障或停止運營。例如，使用機器學習天氣模型、歷史數據集和實時本地天氣信息可以幫助公司預測風暴或熱浪何時襲來，以調整其運營，為天氣變化做好準備。

隨著數字化的普及，能源公司現在在日常運營中使用人工智能技術，這種類型的設備幾乎肯定會成為改變能源未來的關鍵。人工智能支持從化石燃料向更環保替代品有效過渡的主要方式之一是通過網格管理。人工智能和機器學習使用數據分析來估計任何特定地區家庭的能源消耗水平。它考慮了各種因素，如一年中的時間、高峰和非高峰時間以及天氣條件。這可以幫助能源公司不斷了解未來幾天可能的用電量，相應地管理電網，避免停電。生產也可以根據使用預測進行調整，以滿足需求并避免浪費。

人工智能技術在不同能源運營領域的推廣也可以顯著提高維護實踐。機器可以預測維護需求，在停電之前安排維修，以避免不必要的電力損失。能源公司可以為維修做好準備，并通知消費者，而不是突然出現故障，這意味著更長的維修時間和客戶的意外停電。

在太陽能發電方面，人工智能可以根據日照時間和強度來確定建造太陽能發電場的最佳地點。它還可以幫助操作員規劃站點的布局，以便太陽能系統捕捉到最多的陽光。一旦投入使用，人工智能技術可以用于自動化決策，以控制太陽能電池板，因為它們全天都在朝著陽光旋轉。

就連太陽能人工智能公司Glint solar的聯合創始人兼首席運營官J. Kvelland也解釋說：“對我們來說，令人驚訝的是，有這么多非常老練的太陽能開發商仍在使用舊的土地采購方式——被動地等待別人推薦一塊土地，或者通過觀察谷歌地球來猜測。”他補充說：“考慮到幾乎所有開發商都有雄心勃勃的計劃，他們越來越必須積極主動地進行網站篩選，我們很自豪最終為他們提供了這項重要任務的軟件。”

在風力發電方面，丹麥可再生能源巨頭維斯塔斯風力系統公司在風電場數字化方面處于領先地位，利用機器學習不斷適應和改進運營。現場人工智能技術主要通過反復試驗從環境中實時學習，以創造變化以提高風能生產。

世界經濟論壇能源和材料基準測試項目負責Espen Mehlum表示：“你可以使用人工智能來優化風電場的建設、選址和運營，但更重要的是，你可以使用人工智能來優化不同的系統，無論是在消費方面還是在生產方面。”他補充說：“這就是人工智能巨大的未開發潛力所在——我們只是觸及了表面，看到了第一個用例。”

能源行業的數字化正在順利進行，幾乎所有的石油和天然氣以及可再生能源巨頭都將廣泛的創新技術納入其運營中，以提高效率和生產穩定性。人工智能技術使能源公司能夠預測一系列場景，確保消費者的可靠能源輸出，支持電網效率，并適應預期和實時變化，為生產創造最佳條件。

曹海斌 摘譯自 油價網

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.