By Deepesh Nanda, CEO and Managing Director, Tata Power Renewable Energy Limited
Renewable energy has moved from being an alternative power source to a central pillar of global energy infrastructure. With ambitious targets for carbon neutrality and rapid technological advancements, renewable sources like solar and wind are reshaping power grids worldwide. However, unlike conventional energy sources, renewable energy is intermittent-dependent on weather conditions, time of day, and geographic location.
To address this challenge, forecasting and scheduling (F&S) have become indispensable tools in energy management. Accurate predictions allow grid operators to balance supply and demand, prevent power shortages, and optimise energy trading. The evolution of renewable energy forecasting has been marked by substantial improvements in data analytics, machine learning, and policy regulations, making it an essential component in achieving a sustainable and reliable energy future.
The early days: A shot in the dark
In the early 2000s, forecasting renewable energy generation was a difficult task due to limited technological capabilities. Predicting solar and wind output relied on rudimentary historical data analysis and basic weather models, often leading to significant forecasting errors of over 30 per cent. This inaccuracy resulted in grid instability, energy curtailments, and financial losses for energy producers and utilities.
Why was it so difficult?
- Lack of real-time weather data: Renewable energy output fluctuates based on weather patterns, and early models lacked real-time meteorological input.
- Inefficient grid management: Without accurate predictions, grid operators struggled to maintain stability, leading to power fluctuations and inefficiencies.
- High curtailment rates: Excess energy from wind and solar farms often went unused due to limited energy storage solutions.
Despite these challenges, governments and researchers recognised the need for accurate forecasting and began investing in technological advancements and regulatory frameworks to improve prediction accuracy.
Where we are today? Smarter, faster and more accurate
Today, renewable energy forecasting has significantly improved due to advancements in artificial intelligence (AI), machine learning (ML), and big data analytics. These technologies have reduced forecasting errors to below 10 per cent, allowing for better grid integration and energy efficiency.
This has been possible due to use of the following modern forecasting models:
- Numerical weather prediction models that use meteorological data to estimate cloud cover, wind speed, and solar radiation.
- Statistical models that analyse past energy generation patterns to predict future output.
- AI and machine learning algorithms that continuously refine forecasts by learning from real-time energy production data.
- IoT sensors and satellite imaging that collect live data from wind turbines, solar panels, and grid infrastructure to improve accuracy.
A breakthrough example is Google DeepMind’s AI-driven wind power forecasting, which has improved wind energy predictability by 36 hours in advance, increasing grid efficiency by 20 per cent. Similarly, in India, the National Load Dispatch (NLDC) has implemented AI-powered forecasting, leading to a 15 per cent reduction in energy wastage.
India’s push for advanced F&S
India, one of the world’s largest renewable energy markets, has been aggressively expanding its F&S regulations. The Central Electricity Regulatory Commission (CERC) mandates that renewable energy generators submit day-ahead and intra-day forecasts to grid operators, enhancing grid reliability. The green day-ahead market (GDAM), launched in 2022, facilitates real-time energy trading, leading to better integration of renewable energy, with daily trading volumes exceeding 5 GWh. Furthermore, the renewable energy management centers, set up across India, now handle real-time monitoring and forecasting, reducing grid imbalances by 25 per cent.
Several state governments have also floated F&S regulations for renewable energy projects. In fact, Kerala’s 2024 draft F&S regulation aims to improve forecasting accuracy for wind-solar hybrid projects as well to ensure better grid stability.
Despite regulations in place, challenges remain. India issued 73 GW of renewable energy tenders in 2024, but 8.5 GW went unclaimed due to policy and forecasting uncertainties. Still, overall, improved forecasting techniques have helped India reduce energy balancing costs by 20 per cent, leading to millions in savings. With India targeting 500 GW of non-fossil fuel capacity by 2030, improved forecasting methods will play a crucial role in ensuring a reliable and resilient energy grid.
Future outlook: Innovations in renewable energy forecasting
As renewable energy adoption continues to rise, next-generation F&S technologies will be crucial in optimising energy efficiency. Future developments are expected to bring forecasting errors down to below 5 per cent, making renewable sources as reliable as conventional energy. Following trends will define the future of F&S of renewable energy:
AI-driven forecasting precision: AI-based models will integrate hyper-local weather predictions with real-time energy monitoring. Self-learning algorithms will adapt to unexpected weather changes, reducing prediction errors further.
Smarter energy storage solutions: Advanced battery energy storage systems (BESS) will store excess solar and wind power for later use. India has set a 10 GW battery storage capacity target by 2030 to support grid stability. Hybrid energy systems combining solar, wind, and battery storage will ensure uninterrupted power supply.
Decentralised energy forecasting: Rooftop solar and community-based microgrids will require localised forecasting models to optimise power distribution. Blockchain-based energy trading platforms will enable users to sell excess renewable energy in real-time. Pilot projects in Delhi and Gujarat are already exploring peer-to-peer energy trading using blockchain, expected to scale nationwide by 2026.
Enhanced market mechanisms for renewables: Governments will introduce stricter accuracy requirements for energy forecasting, encouraging further technological innovation. Renewable energy forecasting will become a profit-driven market, where companies investing in precise predictions receive financial incentives. AI-powered trading platforms will enable real-time pricing adjustments, improving efficiency in the Green Day-Ahead Market (GDAM).
Integration of quantum computing in forecasting: Quantum computing is expected to revolutionise forecasting models, processing vast amounts of meteorological and energy data 100x faster than today’s systems. Early trials indicate that quantum algorithms could increase forecasting accuracy by 40 per cent, drastically reducing grid imbalances.
Net, net, F&S is the backbone of modern renewable energy integration. The transition from basic weather models to AI-powered predictive analytics has drastically improved forecasting accuracy, reducing grid disruptions and optimising energy use. With governments setting aggressive carbon neutrality targets, accurate forecasting is more critical than ever. India’s continued investment in AI, battery storage, and decentralised grid management signals a promising future for renewable energy reliability. As innovations like quantum computing, blockchain energy trading, and real-time AI forecasting take shape, the dream of a 100 per cent renewable-powered world is becoming a data-driven reality.
