DeepMind 以機器學習 (Machine Learning) 預測未來36小時風力發電輸出，大幅提升風電產值20％
DeepMind 以機器學習 (Machine Learning) 預測未來36小時風力發電輸出，大幅提升風電產值20％
News from: iThome & DeepMin Blog.
DeepMind對Google在美國的風力發電廠應用機器學習技術，以天氣預測以及渦輪運轉資料，預測未來36小時的電力輸出，降低風力發電的不可預測性。
DeepMind和Google合作，將機器學習演算法應用在風力發電廠上，透過天氣預報以及歷史風機運轉資料訓練機器學習模型，預測未來36小時的發電量，以作出最佳每小時電力交付承諾，DeepMind提到，預測能源非常有價值，在使用機器學習之後，他們有效提高風能價值20％。
Web site:
https://deepmind.com/blog/machine-learning-can-boost-value-wind-energy/
在過去十年，由於渦輪機成本下降，風力發電採用率不斷上升，因此成為了無碳發電的重要替代來源。不過，風的變動性，使得風力發電成為一種不可預測的能源，比起其他可在需要的時候傳輸可靠電力的來源相比，風力發電就顯得不這麼有用。DeepMind和Google為了解決這個問題，去年在美國中部對700兆瓦風力發電應用機器學習演算法。而產生這700兆瓦電力的風力發電廠，為Google全球再生能源專案的一部分，共可產生供應中型城市所需要的電量。
DeepMind透過天氣預報資料以及渦輪運轉的歷史資料訓練神經網路，讓系統可以預估未來36小時的風力發電輸出，有了這些預測，DeepMind的模型可以最佳化每小時電力交付承諾，在前一天對輸出給電網的電力預測做出調整，DeepMind提到，預測的能力對於電力安排很有幫助，可以在設定的時間提供定量的電力，對電網來說非常有價值。
這樣研究到目前為止成效卓越，資料顯示，在電力交付承諾上使用機器學習，比起基準情境，整體風能價值大幅提升20％。DeepMind表示，雖然無法消除風的多變性，但這個研究初期的成果證明，他們可以透過機器學習讓風能更可預測並提升其價值，而且這種方法還有助於風力發電廠的營運，因為機器學習可以讓電廠，以更快更資料驅動的方法評估電力輸出以滿足電網電力需求。
DeepMind認為，使用機器學習方法可以增加風力發電的商業價值，進一步推動全球電網採用再生能源。
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Carbon-free technologies like renewable energy help combat climate change, but many of them have not reached their full potential. Consider wind power: over the past decade, wind farms have become an important source of carbon-free electricity as the cost of turbines has plummeted and adoption has surged. However, the variable nature of wind itself makes it an unpredictable energy source—less useful than one that can reliably deliver power at a set time.
In search of a solution to this problem, last year DeepMind and Google started applying machine learning algorithms to 700 megawatts of wind power capacity in the central United States. These wind farms—part of Google’s global fleet of renewable energy projects—collectively generate as much electricity as is needed by a medium-sized city.
Using a neural network trained on widely available weather forecasts and historical turbine data, we configured the DeepMind system to predict wind power output 36 hours ahead of actual generation. Based on these predictions, our model recommends how to make optimal hourly delivery commitments to the power grid a full day in advance. This is important, because energy sources that can be scheduled (i.e. can deliver a set amount of electricity at a set time) are often more valuable to the grid.
Although we continue to refine our algorithm, our use of machine learning across our wind farms has produced positive results. To date, machine learning has boosted the value of our wind energy by roughly 20 percent, compared to the baseline scenario of no time-based commitments to the grid.
We can’t eliminate the variability of the wind, but our early results suggest that we can use machine learning to make wind power sufficiently more predictable and valuable. This approach also helps bring greater data rigor to wind farm operations, as machine learning can help wind farm operators make smarter, faster and more data-driven assessments of how their power output can meet electricity demand
Our hope is that this kind of machine learning approach can strengthen the business case for wind power and drive further adoption of carbon-free energy on electric grids worldwide. Researchers and practitioners across the energy industry are developing novel ideas for how society can make the most of variable power sources like solar and wind. We’re eager to join them in exploring general availability of these cloud-based machine learning strategies.
Google recently achieved 100 percent renewable energy purchasingand is now striving to source carbon-free energy on a 24x7 basis. The partnership with DeepMind to make wind power more predictable and valuable is a concrete step toward that aspiration. While much remains to be done, this step is a meaningful one—for Google, and more importantly, for the environment.
News from: iThome & DeepMin Blog.
DeepMind對Google在美國的風力發電廠應用機器學習技術，以天氣預測以及渦輪運轉資料，預測未來36小時的電力輸出，降低風力發電的不可預測性。
DeepMind和Google合作，將機器學習演算法應用在風力發電廠上，透過天氣預報以及歷史風機運轉資料訓練機器學習模型，預測未來36小時的發電量，以作出最佳每小時電力交付承諾，DeepMind提到，預測能源非常有價值，在使用機器學習之後，他們有效提高風能價值20％。
Web site:
https://deepmind.com/blog/machine-learning-can-boost-value-wind-energy/
在過去十年，由於渦輪機成本下降，風力發電採用率不斷上升，因此成為了無碳發電的重要替代來源。不過，風的變動性，使得風力發電成為一種不可預測的能源，比起其他可在需要的時候傳輸可靠電力的來源相比，風力發電就顯得不這麼有用。DeepMind和Google為了解決這個問題，去年在美國中部對700兆瓦風力發電應用機器學習演算法。而產生這700兆瓦電力的風力發電廠，為Google全球再生能源專案的一部分，共可產生供應中型城市所需要的電量。
DeepMind透過天氣預報資料以及渦輪運轉的歷史資料訓練神經網路，讓系統可以預估未來36小時的風力發電輸出，有了這些預測，DeepMind的模型可以最佳化每小時電力交付承諾，在前一天對輸出給電網的電力預測做出調整，DeepMind提到，預測的能力對於電力安排很有幫助，可以在設定的時間提供定量的電力，對電網來說非常有價值。
這樣研究到目前為止成效卓越，資料顯示，在電力交付承諾上使用機器學習，比起基準情境，整體風能價值大幅提升20％。DeepMind表示，雖然無法消除風的多變性，但這個研究初期的成果證明，他們可以透過機器學習讓風能更可預測並提升其價值，而且這種方法還有助於風力發電廠的營運，因為機器學習可以讓電廠，以更快更資料驅動的方法評估電力輸出以滿足電網電力需求。
DeepMind認為，使用機器學習方法可以增加風力發電的商業價值，進一步推動全球電網採用再生能源。
------------------------------------------
Carbon-free technologies like renewable energy help combat climate change, but many of them have not reached their full potential. Consider wind power: over the past decade, wind farms have become an important source of carbon-free electricity as the cost of turbines has plummeted and adoption has surged. However, the variable nature of wind itself makes it an unpredictable energy source—less useful than one that can reliably deliver power at a set time.
In search of a solution to this problem, last year DeepMind and Google started applying machine learning algorithms to 700 megawatts of wind power capacity in the central United States. These wind farms—part of Google’s global fleet of renewable energy projects—collectively generate as much electricity as is needed by a medium-sized city.
Using a neural network trained on widely available weather forecasts and historical turbine data, we configured the DeepMind system to predict wind power output 36 hours ahead of actual generation. Based on these predictions, our model recommends how to make optimal hourly delivery commitments to the power grid a full day in advance. This is important, because energy sources that can be scheduled (i.e. can deliver a set amount of electricity at a set time) are often more valuable to the grid.
Although we continue to refine our algorithm, our use of machine learning across our wind farms has produced positive results. To date, machine learning has boosted the value of our wind energy by roughly 20 percent, compared to the baseline scenario of no time-based commitments to the grid.
We can’t eliminate the variability of the wind, but our early results suggest that we can use machine learning to make wind power sufficiently more predictable and valuable. This approach also helps bring greater data rigor to wind farm operations, as machine learning can help wind farm operators make smarter, faster and more data-driven assessments of how their power output can meet electricity demand
Our hope is that this kind of machine learning approach can strengthen the business case for wind power and drive further adoption of carbon-free energy on electric grids worldwide. Researchers and practitioners across the energy industry are developing novel ideas for how society can make the most of variable power sources like solar and wind. We’re eager to join them in exploring general availability of these cloud-based machine learning strategies.
Google recently achieved 100 percent renewable energy purchasingand is now striving to source carbon-free energy on a 24x7 basis. The partnership with DeepMind to make wind power more predictable and valuable is a concrete step toward that aspiration. While much remains to be done, this step is a meaningful one—for Google, and more importantly, for the environment.
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