The role of AI in accelerating positive climate action

Written By :

Corporate Communication


Published On :

20 May 2024


Published By :

ReNew


Climate change is one of the greatest challenges humankind is facing in the 21st century. According to the World Health Organization, around 3.6 billion people* live in areas that may face the brunt of climate change in the future. Given the enormity of the situation, one needs immediate curbs in emissions to limit the impact of climate change. This is where Artificial intelligence (AI) can play a decisive role in enabling positive climate action.

From accessing the pace of climate change to facilitating the decarbonization of industries to enabling the development of early warning systems and resilient infrastructure, AI can offer innovative solutions to all climate-change-related challenges. Climate scientists, policymakers, and other stakeholders can collect, analyze, and interpret enormous emissions datasets and model smart systems for climate research using artificial intelligence (AI). Here is a detailed account of how AI can help address the causes of global warming and mitigate the effects of climate change.


AI for Mitigation Efforts

Measurement and Monitoring

While we create our mitigation strategies, measuring the glacial and sea-level-rise, deforestation, and pollution levels are critical to provide us with the needed direction. AI-enabled study of remote sensing data from satellites can enable us to monitor all impact areas of climate change which may be unimaginable with human observation. By tracking the GHG emissions through different spectral channels obtained from satellite images, focused action can be taken for mitigation.


Decarbonization of Industries

Utilities, Metal, Mining, oil and gas, construction materials, packaging and various other carbon intensive industries emit tons of CO2 & other greenhouse gases into the atmosphere every year. AI can help in decarbonization of these industries through the optimization of their processes and their transition to renewable energy sources through technologies such as Digital Twins. Research & development driven by AI can boost innovation and sustainable product development in multiple industries. Simultaneously, these industries stand to gain in terms of cost savings and improved efficiencies.


Promoting Renewable Energy

AI can prove to be instrumental in achieving SDG7 – affordable and clean energy for all by 2030. Historically, renewable power inherently has been unstable for integration into the national grids. AI models can provide far more accurate generation forecasting than statistical forecasting, thereby helping in optimizing grids, providing stability for renewable power integration, and improving renewable energy production efficiencies. Furthermore, by leveraging AI models, the power sector can significantly reduce their greenhouse emissions. 


Recommended Reading:


Greenwashing – A major pitfall on theroad to Net Zero

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AI For Climate Adaptation and Resilience

Hazard Forecasting & Infrastructure Resilience

With the escalation of global warming, climate-related disasters have multiplied over the years. With the help of AI, the loss of lives and property during natural disasters can be largely avoided through the development of early warning systems and building resilient infrastructure.


AI in Climate Research and Modelling

Data-driven insights

Processing complex datasets on emissions and analyzing the same to find patterns and trends can provide actionable insights and direction for our risk mitigation efforts. It can provide policy-makers and stakeholders with a more informed and data-driven approach to combat climate change.


The case of ReNew: Leveraging AI to enhance renewable energy offerings

With a clean energy portfolio of ~15.6GW, on a gross basis as of May 09, 2024, ReNew is one of the largest renewable energy companies globally. The company operates more than 150+ utility-scale projects spread across 9 states in India. Furthermore, it also provides decarbonization solutions through a mix of green hydrogen, intelligent (data-driven) solutions, energy storage, manufacturing, and carbon markets.


ReNew also has been successfully leveraging AI in generation forecasting, preventive maintenance, power trading, price forecasting, hybrid RE project sizing, and scenario modeling.


Generation Forecasting

·       At ReNew, we employ machine learning models built on large number of machine parameters and weather forecasts to predict the wind/solar generation for the next day. This helps in stabilizing the grid, enhances revenue certainty and reduces penalties on account of schedule deviations.


Preventive Maintenance

·       We have trained machine learning models on certain machine parameters such as vibration and temperature, to access the likelihood of critical equipment failure. Based on the observed values of these parameters, alerts are issued to the Operations & Maintenance (O&M) team to inspect the equipment at site so that pro-active action can be taken before their failure/malfunction.


Hybrid RE Scenario modelling

·       Hybrid RE projects are complex contracts hitherto unseen by Indian power sector. With complex contractual obligations, diverse asset scheduling, and multiple project beneficiaries, they demand meticulous planning. The team at ReNew has built a digital twin of these contracts so that any kind of scenario can be analysed. Impact of changes in regulations, capacity and regulatory charges on the project can be studied in a much-expedited manner.


Price forecasting and Power trading

·       Prediction of short-term electricity prices is possible using machine learning models based on historical market data. By incorporating such models and employing other advanced analytics techniques in our power trading strategy, we are able to extract a much better price from the market than a plain vanilla bidding.  


Operations and Maintenance

·       At ReNew, advanced analytics driven by AI have led to a transformation in the way operations and maintenance is handled at the 150+ sites at ReNew resulting in significant benefits in yield, reliability, human-resource efficiency, and cost. To take some examples of the positive results, the annual yield in productivity rose by 1-1.55%, and so did the site employees’ efficiency in maintenance activities, which went by a whopping 31%!

ReNew has also started utilizing cutting edge technologies like Computer Vision, Generative AI, and NLP which is leading to better quality, improved customer experience and better employee efficiency.

 

ReNew’s digital transformation driven by IR 4.0 technologies has been awarded by World Economic Forum with the prestigious Global Lighthouse Award – not just once, but twice. This is an unparalleled feat in the global renewable energy sector.


Conclusion

While AI technologies are widely available in developed regions, many developing and less-developed countries, particularly in the Global South, lack access to this crucial tech infrastructure. Ensuring equal access to data and AI technologies is essential for harnessing AI’s potential in climate action on a global scale. At the same time, the continued efforts of nations’ s in cultivating awareness among all stakeholders - policymakers, corporate decision-makers, civil servants, and the broader public and building expertise in AI are also required to address the climate crisis we face today.


Sources:

https://blog.google/outreach-initiatives/sustainability/how-satellites-algorithms-and-ai-can-help-map-and-trace-methane-sources/

https://www.bcg.com/publications/2022/how-ai-can-help-climate-change

https://www.weforum.org/agenda/2024/02/ai-combat-climate-change/#:~:text=The%20use%20of%20artificial%20intelligence,the%20World%20Economic%20Forum%20says.

https://news.un.org/en/story/2023/11/1143187

https://hub.jhu.edu/2023/03/07/artificial-intelligence-combat-climate-change/

https://blog.google/outreach-initiatives/sustainability/report-ai-sustainability-google-cop28/

https://web-assets.bcg.com/72/cf/b609ac3d4ac6829bae6fa88b8329/bcg-accelerating-climate-action-with-ai-nov-2023-rev.pdf