據(jù)油價(jià)網(wǎng)1月9日消息稱，石油和天然氣的數(shù)字化已經(jīng)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(jī)器學(xué)習(xí)和其他創(chuàng)新技術(shù)來(lái)改善他們的運(yùn)營(yíng)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(lǐng)域一樣，人工智能也被用于風(fēng)能、太陽(yáng)能和其他綠色能源項(xiàng)目，通過(guò)提高自動(dòng)化程度來(lái)提高效率。隨著能源公司尋求更大程度上的數(shù)字化運(yùn)營(yíng)，人工智能可能會(huì)在未來(lái)的能源轉(zhuǎn)型中發(fā)揮主導(dǎo)作用。人工智能的使用可以支持整個(gè)能源行業(yè)的眾多活動(dòng)，支持從化石燃料到可再生能源的所有能源的運(yùn)營(yíng)。近年來(lái)，能源行業(yè)采用人工智能技術(shù)來(lái)支持自動(dòng)化決策和輔助決策。第一種是計(jì)算機(jī)系統(tǒng)自主處理信息，無(wú)需人工輸入。這通常意味著任務(wù)可以比需要人工決策時(shí)更快更有效地完成，因?yàn)闄C(jī)器可以立即做出改變。然而，有些問(wèn)題需要更多的人力投入來(lái)確定正確的反應(yīng);在這種情況下，輔助決策是有用的。機(jī)器可以提供有用的見(jiàn)解，為工作人員提供數(shù)據(jù)，以解釋和決定在任何給定的情況下采取正確的行動(dòng)。

人工智能在預(yù)測(cè)方面也發(fā)揮著重要作用。復(fù)雜算法的使用可以幫助投資者確定一個(gè)新的綠色能源項(xiàng)目所涉及的風(fēng)險(xiǎn)水平，預(yù)測(cè)不同條件下不同類(lèi)型的可再生能源的產(chǎn)量，并預(yù)測(cè)不同地點(diǎn)一天中不同時(shí)間的能源需求。技術(shù)提供持續(xù)的監(jiān)測(cè)和評(píng)估，通過(guò)預(yù)測(cè)潛在的挑戰(zhàn)并立即應(yīng)對(duì)，可以幫助公司防止故障或停止運(yùn)營(yíng)。例如，使用機(jī)器學(xué)習(xí)天氣模型、歷史數(shù)據(jù)集和實(shí)時(shí)本地天氣信息可以幫助公司預(yù)測(cè)風(fēng)暴或熱浪何時(shí)襲來(lái)，以調(diào)整其運(yùn)營(yíng)，為天氣變化做好準(zhǔn)備。

隨著數(shù)字化的普及，能源公司現(xiàn)在在日常運(yùn)營(yíng)中使用人工智能技術(shù)，這種類(lèi)型的設(shè)備幾乎肯定會(huì)成為改變能源未來(lái)的關(guān)鍵。人工智能支持從化石燃料向更環(huán)保替代品有效過(guò)渡的主要方式之一是通過(guò)網(wǎng)格管理。人工智能和機(jī)器學(xué)習(xí)使用數(shù)據(jù)分析來(lái)估計(jì)任何特定地區(qū)家庭的能源消耗水平。它考慮了各種因素，如一年中的時(shí)間、高峰和非高峰時(shí)間以及天氣條件。這可以幫助能源公司不斷了解未來(lái)幾天可能的用電量，相應(yīng)地管理電網(wǎng)，避免停電。生產(chǎn)也可以根據(jù)使用預(yù)測(cè)進(jìn)行調(diào)整，以滿足需求并避免浪費(fèi)。

人工智能技術(shù)在不同能源運(yùn)營(yíng)領(lǐng)域的推廣也可以顯著提高維護(hù)實(shí)踐。機(jī)器可以預(yù)測(cè)維護(hù)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(zhǔn)備，并通知消費(fèi)者，而不是突然出現(xiàn)故障，這意味著更長(zhǎng)的維修時(shí)間和客戶的意外停電。

在太陽(yáng)能發(fā)電方面，人工智能可以根據(jù)日照時(shí)間和強(qiáng)度來(lái)確定建造太陽(yáng)能發(fā)電場(chǎng)的最佳地點(diǎn)。它還可以幫助操作員規(guī)劃站點(diǎn)的布局，以便太陽(yáng)能系統(tǒng)捕捉到最多的陽(yáng)光。一旦投入使用，人工智能技術(shù)可以用于自動(dòng)化決策，以控制太陽(yáng)能電池板，因?yàn)樗鼈內(nèi)於荚诔?yáng)光旋轉(zhuǎn)。

就連太陽(yáng)能人工智能公司Glint solar的聯(lián)合創(chuàng)始人兼首席運(yùn)營(yíng)官J. Kvelland也解釋說(shuō)：“對(duì)我們來(lái)說(shuō)，令人驚訝的是，有這么多非常老練的太陽(yáng)能開(kāi)發(fā)商仍在使用舊的土地采購(gòu)方式——被動(dòng)地等待別人推薦一塊土地，或者通過(guò)觀察谷歌地球來(lái)猜測(cè)。”他補(bǔ)充說(shuō)：“考慮到幾乎所有開(kāi)發(fā)商都有雄心勃勃的計(jì)劃，他們?cè)絹?lái)越必須積極主動(dòng)地進(jìn)行網(wǎng)站篩選，我們很自豪最終為他們提供了這項(xiàng)重要任務(wù)的軟件。”

在風(fēng)力發(fā)電方面，丹麥可再生能源巨頭維斯塔斯風(fēng)力系統(tǒng)公司在風(fēng)電場(chǎng)數(shù)字化方面處于領(lǐng)先地位，利用機(jī)器學(xué)習(xí)不斷適應(yīng)和改進(jìn)運(yùn)營(yíng)。現(xiàn)場(chǎng)人工智能技術(shù)主要通過(guò)反復(fù)試驗(yàn)從環(huán)境中實(shí)時(shí)學(xué)習(xí)，以創(chuàng)造變化以提高風(fēng)能生產(chǎn)。

世界經(jīng)濟(jì)論壇能源和材料基準(zhǔn)測(cè)試項(xiàng)目負(fù)責(zé)Espen Mehlum表示：“你可以使用人工智能來(lái)優(yōu)化風(fēng)電場(chǎng)的建設(shè)、選址和運(yùn)營(yíng)，但更重要的是，你可以使用人工智能來(lái)優(yōu)化不同的系統(tǒng)，無(wú)論是在消費(fèi)方面還是在生產(chǎn)方面。”他補(bǔ)充說(shuō)：“這就是人工智能巨大的未開(kāi)發(fā)潛力所在——我們只是觸及了表面，看到了第一個(gè)用例。”

能源行業(yè)的數(shù)字化正在順利進(jìn)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(chuàng)新技術(shù)納入其運(yùn)營(yíng)中，以提高效率和生產(chǎn)穩(wěn)定性。人工智能技術(shù)使能源公司能夠預(yù)測(cè)一系列場(chǎng)景，確保消費(fèi)者的可靠能源輸出，支持電網(wǎng)效率，并適應(yīng)預(yù)期和實(shí)時(shí)變化，為生產(chǎn)創(chuàng)造最佳條件。

曹海斌 摘譯自 油價(jià)網(wǎng)

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.