The UN Climate Change Conference in Glasgow (COP26) saw 197 countries commit to increasing efforts to slow global warming. These industrially developed countries and transition economies have pledged to reduce greenhouse gases and achieve net zero carbon emissions. The first step to achieve this is to switch from traditional coal, oil, and gas to renewable energy sources.
India’s energy consumption was not even 1,000-Terawatt hour (TWh) in 1970. In 50 years, it has risen beyond 8,000 TWh. India’s rapid development as well as its exploding population are some of the reasons for this massive growth in demand.
Even though India’s energy production and consumption have skyrocketed, the share of low-carbon sources (nuclear, wind, solar, hydropower, geothermal, biomass) has reduced over the past 40 years. Their share was as high as 14.61 percent of total energy production in 1978. By 2019, it had fallen to just 8.96 percent. This is an anomaly as other countries have displayed a trend of a steadily increasing share of low-carbon sources.
The reasons why India is lagging in the use of renewable energy sources are –
(i) Power grids are weak and unstable with a low reserve margin
(ii) Intermittent nature of natural sources like wind and solar energy
(iii) Space is scarce; Wind turbines and solar plants take up lots of land
With the speed at which energy is consumed, our energy reservoirs are depleting fast. Soon our future generations will be left with little in store. Hence, it would be prudent to attain faster progress in producing, storing, and distributing renewable energy sources using Artificial Intelligence (AI) and Machine Learning (ML).
Using AI and ML, large amounts of data from energy systems can be consolidated to identify patterns and trends. ML algorithm analyses historical data, weather forecasts, and social factors to accurately determine trends and predict future energy demand. This is a job that is impossible for human engineers to detect.
(i) Accurate forecast of energy supply and demand facilitates the balance of energy load on the power grids, thereby preventing grid failures. Grid operators are given precise estimates of energy production and distribution. This helps stabilize and smoothly manage grids.
(ii) Automatic detection of anomalies in energy usage data helps avoid wastage and save energy.
(iii) The prediction of potential breakdown of equipment and machinery aids the companies in scheduling the maintenance of their systems, reducing repair costs, and protecting their assets. As a result, the reliability of the energy infrastructure will increase.
(iv) Artificial Intelligence tools can operate machines and monitor electricity plants. By reducing human error and without human interference, the safety of human life is enhanced.
(v) The amount of energy generated from wind and solar power can be forecast depending on the weather. This helps to maximize the use of clean energy.
(vi) In energy storage systems, AI and ML can forecast storage needs and optimize charge-discharge cycles. The algorithm determines the best time to store energy, when to release it, and how much to distribute. It balances energy load during peak and off-peak times based on the day, time, season, and weather
Energy Companies can develop the best strategies that meet SDGs to optimize the production, distribution, usage, and storage of energy, that mitigate the worst effects of climate change.
The Asia Pacific artificial intelligence (AI) in renewable energy market size reached USD 4.84 billion in 2023 and is projected to hit around USD 44.34 billion by 2032, expanding at a double-digit CAGR of 27.90% from 2023 to 2032.
Asia-Pacific dominated the market with the largest share in 2022. The surge in demand for electricity is driving the growth of artificial intelligence (AI) in the renewable energy market in the Asia-Pacific region.
AI is revolutionizing the energy sector by optimizing renewable energy production, storage, and distribution. It enables accurate demand forecasting, reduces waste, enhances grid stability, and improves asset management. As countries work towards net-zero emissions, AI stands as a key player in driving the energy transition and combating climate change.
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