Can AI power India’s renewable energy ambitions?

India’s renewable energy sector, ranked fourth globally, can greatly benefit from artificial intelligence (AI) adoption. With the fastest growth in renewable electricity and the potential to double capacity additions by 2026, AI’s transformative power enhances accessibility and affordability in clean energy. IBT looks at the potential upsides and current teething troubles for the sector in its march towards AI integration.

Image Source: Pexels

India is ranked as the fourth most attractive renewable energy market in the world. It was ranked fourth in wind power, fifth in solar power and fourth in installed renewable energy capacity as of 2020. Installed renewable capacity has gained pace over the past few years, posting a CAGR of 15.92% between FY16-22. The country has seen the fastest growth in renewable electricity, and by 2026, new capacity additions are expected to double.

AI (artificial intelligence) is rapidly transforming various sectors. Its adoption in clean energy can have multiple benefits. The potential impact of AI on renewable energy is immense, driving us towards a cleaner and sustainable future and give a strong push to India’s efforts towards a net zero carbon economy.

Role of AI in renewable energy

The global AI market is expected to reach US$ 422.37 billion by 2028, growing at a CAGR of 39.4%. In India, the AI market is projected to reach US$ 7.8 billion by 2025, growing at a CAGR of 20.2%. The renewable energy sector can greatly benefit from the adoption of  AI, which is rapidly transforming various aspects of our lives. With endless potential applications, AI offers promising solutions for optimizing renewable energy systems. By leveraging AI, the renewable energy sector can enhance efficiency, improve energy production forecasting, optimize maintenance schedules, and minimize operational costs. 

AI benefits renewable energy companies by optimizing production, predicting demand, and improving performance. It assists in site selection, design, and resource allocation for clean energy projects. It fine-tunes system performance, optimizes energy production, and forecasts future output. It enhances safety by predicting issues and facilitating timely maintenance. Below are some interesting use cases where AI can play a pivotal role:

Optimizing Production
AI revolutionizes renewable energy companies by optimizing production processes. By analyzing historical weather data, AI enables solar and wind farms to predict ideal conditions for power generation. This information guides adjustments in production levels, maximizing power output during peak demand periods.

Predicting Consumer Demand
AI empowers utilities to accurately predict consumer electricity demand. By leveraging AI’s forecasting capabilities, utilities can proactively manage the grid and ensure sufficient power generation to meet anticipated demand. This prevents potential blackouts or brownouts and promotes a reliable energy supply.

Transforming the Energy Landscape
The integration of AI into the renewable energy sector has the potential to revolutionize energy production and consumption. AI’s optimization and demand prediction abilities enhance the efficiency and reliability of renewable energy sources, driving a sustainable and resilient energy ecosystem.

Harnessing Data for Decision-Making
AI enables renewable energy companies to tap into valuable insights within data. By utilizing AI, companies can make intelligent decisions and allocate resources effectively. This empowers them to optimize operations, enhance productivity, and contribute to a greener future.

Waste Management
By enabling predictive maintenance and optimizing the lifecycle of renewable energy infrastructure, AI can contribute to waste reduction. Through real-time analysis of operational data and performance metrics, AI algorithms can identify potential faults or inefficiencies, allowing for proactive maintenance and minimizing downtime. This, in turn, reduces equipment waste and extends the lifespan of renewable energy assets.

Innovation and Future Prospects
As AI continues to advance, its applications in the renewable energy sector will expand further. The synergy between AI and renewable energy holds immense promise for a future where clean, sustainable power is harnessed with unprecedented efficiency, propelling us towards a greener and more sustainable world.

AI also streamlines waste management, identifying valuable materials and improving recycling processes. The adoption of AI in renewable energy has the potential to revolutionize energy production and consumption, making it more efficient and reliable.

Challenges faced by the Industry

When we asked Ravi Chaudhary, Co-founder and CEO of solarad.ai, about the challenges hindering the adoption of AI in the renewable energy sector in India, he highlighted several key points.

According to him, one of the significant challenges is the availability and quality of data. Ravi emphasized that accurate forecasting and optimization rely on robust and diverse datasets capturing the complexities of renewable energy generation, weather patterns, and other influencing factors. Limited access to reliable data sources and data gaps can impede the development and effectiveness of AI solutions.

Another challenge is the need for technical expertise and awareness. Integrating AI technologies requires specialized skills in data science, machine learning, and AI algorithm development. There is a severe shortage of professionals with these necessary skills in the renewable energy sector. Moreover, there is a lack of awareness about the potential benefits and applications of AI in the industry could further hinder its adoption.

Ravi also highlighted the cost and infrastructure challenges associated with AI adoption. Deploying AI infrastructure and systems often involves significant upfront costs, including expenses for hardware, software, and data storage. This financial burden can be particularly challenging for smaller companies and startups in the renewable energy sector. Additionally, he emphasized on the importance of having adequate infrastructure such as high-performance computing resources and cloud platforms to handle the computational requirements of AI algorithms.

Furthermore, the regulatory and policy framework is a hindrance to the adoption of AI in the renewable energy sector. He stressed that clear guidelines and policies are necessary to address data privacy, cybersecurity, and ethical considerations in order to build trust and ensure responsible AI implementation. Ravi expressed his concern that the absence of a comprehensive regulatory framework specifically tailored to AI in the renewable energy context creates uncertainty and hampers adoption in the industry.

Conclusion 

The integration of AI in India’s renewable energy sector holds immense potential for revolutionizing the industry. AI can enhance efficiency, optimize performance, and improve waste management, leading to a more sustainable and reliable energy future. However, to fully unlock the benefits, addressing challenges related to data availability, technical expertise, infrastructure, and regulatory frameworks is crucial. Encouraging investments in AI infrastructure, promoting skill development, and establishing supportive policies will pave the way for accelerated AI adoption, propelling India towards a cleaner and more efficient renewable energy landscape.