As the world confronts climate change, the COP28 declaration aims to triple renewable energy capacity and double energy efficiency by 2030. However, with over 80% of energy still sourced from fossil fuels in three of the largest global energy consuming and greenhouse gas emitting economies, this transition could be challenging. Generative AI offers a promising solution to enhance efficiency and scale renewable energy in these regions. While the AI-enabled pathway to clean energy is promising, it requires innovative solutions alongside a disciplined approach to ensure that potential is realized. By adopting this forward-thinking strategy, we can reshape clean energy delivery and make significant strides toward achieving our ambitious energy goals by 2030.
In light of the above background, KPMG in India released a report “ACED through AI”.
The report summarises that modern day AI, and especially the combination of AI, Gen AI and High-Performance Computing is transformational. It touches upon every aspect, from materials, equipment design, production and supply chain to project design and development, construction, and operations. Modern AI can transform the pace of deployment of RE and that gives us a reasonable chance to meet the CoP28 goals on clean energy. AI can radically pace up resource identification, land procurement, permitting, sizing and Interconnection management. Within a farm it can optimise solar and wind asset placement. It is enabling real-time performance improvement, enhancing predictive maintenance capabilities, improving energy yield forecasts, optimizing energy storage. AI is enhancing flexibility and resilience in energy transmission and distribution making integration of variable RE possible at scale.
This is bringing down overall costs of energy delivery and enhancing the dependability of RE. Together, these promise a rapid makeover of clean energy production and use. In addition to the technical functions, modern AI has the potential to bring radical efficiencies in a range of corporate functions, including financial planning, reporting, treasury operations, internal controls, procurement, supply chain management, contract management, learning and development, etc. These can bring in significant predictability, thus reducing the risks considerably. Together, these and other AI applications can substantially help reduce risks and attract finance at acceptable cost points.
Access the complete report here
