India has set an ambitious goal of achieving 500GW of renewable energy capacity by 2030 has catalysed rapid growth in the country’s solar market. However, solar developers are facing several challenges towards effectively contributing to this national clean energy target – particularly in evaluating solar performance amid increasingly volatile weather and climatic conditions.
Changing weather and a long-term decline in solar irradiation
Solargis’ 2024 global solar irradiance analysis revealed that many regions in India experienced a decline in solar radiation, with Global Horizontal Irradiation (GHI) decreasing by 3% to 10% in 2024 compared to long-term averages.
Several factors contributed to this decline. In 2024, India’s experienced one of its wettest monsoon seasons, with rainfall nearly 8% above the long-term average. While this was beneficial for agriculture and water reservoirs, it posed significant challenges for solar energy production.
Persistent cloud cover, alongside the prolonged monsoon, also reduced sunlight in Central India and the Western Ghats, particularly in Gujarat and Maharashtra – India’s key hubs of solar power generation. Increased air pollution, especially in Northern India, also resulted in high aerosol levels that significantly reduced solar irradiation.
Why existing solar software falls short
To manage such climate variability, Indian solar developers need more advanced tools for asset performance evaluation. Unfortunately, most currently available solar simulation software is based on Typical Meteorological Year (TMY) data, which averages historical conditions into a single “typical” year. While useful for capturing long-term patterns, TMY lacks the granularity required to represent year-to-year weather fluctuations and rare, extreme events.
This gap between simulation and reality creates risks in project forecasting and design. Without the ability to reflect atypical conditions, TMY-based models may lead to underperformance and missed financial expectations.
The case for high-resolution Time Series data
High-resolution time series data offer a more accurate and reliable alternative. Unlike TMY, time series datasets represent each individual year – often over a 30-year span – broken into 15-minute intervals. This level of detail captures variations in solar irradiance more effectively, making energy yield simulations significantly more precise.
However, most software simulation tools today are unable to process such granular data due to computational or data-handling limitations. As software evolves, integrating 15-minute time series data into simulations will be key for optimising PV plant design, improving yield forecasting, and enhancing system resilience to weather-related disruptions.
Protecting and empowering India’s solar developers and investors for the future
In an increasingly unpredictable climate, solar developers in India need robust evaluation methods that reflect real-world conditions. High-resolution meteorological models not only improve solar resource assessments and power forecasts, but also build confidence for investors by providing more reliable expectations of project performance.
As India accelerates toward its 2030 renewable energy target, combining advanced algorithms with granular solar data will be essential for unlocking the full potential of its solar industry.
BY Marcel Suri, CEO, Solargis
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