India's renewable‑energy sector is surging. Official figures show that by June 2025, the country's installed capacity reached 476 GW, nearly half of it from non‑fossil sources. Solar power expanded from a few gigawatts in 2014 to 110.9 GW, and wind capacity rose to 51.3 GW, placing India among the world's leaders. Meeting future targets will require more than hardware; it demands sophisticated forecasting, automation, and cyber‑secure analytics.
Few practitioners marry those skills like Drumil Joshi, a 25‑year‑old monitoring & diagnostics analyst II at Southern Power. He manages a multi‑technology renewable portfolio worth nearly half a billion dollars and runs real‑time diagnostics for dozens of wind and solar plants. Southern Power Company, a subsidiary of The Southern Company (NYSE: SO), operates over 12 GW of generation capacity across the U.S. Joshi has authored 5+ research papers and holds design grants for energy‑forecasting systems and AI‑enabled sensors. Outside experts call his algorithms "new benchmarks for outage efficiency." In this interview, he explains how his 2025 research can support India's transition and align with national initiatives such as the National Green Hydrogen Mission.
What drew you to data science for renewable energy, and why does it matter for India?
Drumil Joshi: Growing up in Mumbai and Gujarat, I experienced frequent power cuts despite living near factories. In graduate school, I discovered that data can make generation more reliable. At Southern Power, I monitor dozens of plants and see daily how unpredictable weather, latent faults, and messy data affect output. By marrying meteorology, machine learning, and power engineering, we can anticipate problems and schedule maintenance strategically. India is adding record solar and wind capacity, but many operators still rely on intuition. Techniques like ours can help them maximise output and support government programmes, including the Green Hydrogen Mission.
Your open‑source outage‑scheduling paper shows a dramatic reduction in lost energy. How does it work?
Joshi: Solar plants must shut down for maintenance, but picking the wrong day wastes energy. We built a tool that consumes free seven‑day weather forecasts from ECMWF via the pvlib library and chooses the day with the lowest predicted irradiance. In simulations using ERCOT data, random scheduling lost 5.2 % more energy than a perfect schedule, whereas our forecast‑driven method lost only 2.3 %, recovering 56 % of avoidable losses. Andrew Riley, performance monitoring & diagnostics manager overseeing more than 13 GW of generation, praised the focus on scalability and integration with real‑time PI data servers. His open‑source scheduler has since been referenced by academic groups in the U.S., India, and Europe.
You also developed a Deep Self‑Organizing Map for IoT devices. What problem does that solve?
Joshi: Not all energy innovation is about megawatts. Sensors on farms, in homes, or on medical devices harvest solar power and must decide when to operate. We created an extended Deep Self‑Organizing Map (DSOM) to forecast the energy these devices will harvest and to manage their duty cycles. The algorithm achieved 91.37 % prediction accuracy, beating other machine‑learning models. In practice, a soil‑moisture sensor in rural Maharashtra can sleep during low sunlight and transmit when it has enough energy, extending battery life and reducing e‑waste. The project included researchers from Indian and American universities, showing how collaboration can accelerate adoption.
Your third project, VIBRIS, monitors wind‑turbine vibrations. Why is it important?
Joshi: Wind turbines produce continuous vibration data, and simple threshold‑based systems often miss early signs of trouble. VIBRIS combines four algorithms: Isolation Forest, Local Outlier Factor, one‑class SVM, and clustering to generate a single anomaly score. In trials, it flagged turbine a turbine named K210 with a score of 2.84 and a 17.59 % deviation and turbine K206 with a score of 4.0 and a 3.53 % deviation, signalling transient and persistent faults respectively. Experts emphasise that integrating outputs with maintenance teams made VIBRIS scalable across fleets.
Closing Thoughts
Drumil Joshi personifies the next generation of energy scientists who bridge research and practice. His open‑source scheduler recovers the majority of maintenance‑related losses; his DSOM algorithm delivers 91 % accuracy for micro‑devices; and his VIBRIS system spots subtle turbine anomalies. The tools are open source and scalable, saving engineers hours of manual work and demonstrating that automation frees human talent. Independent experts praise the focus on integration and fleet‑wide deployment. As India races toward its 2030 targets and pursues a hydrogen‑powered economy, adopting analytics of this calibre could ensure that every watt of clean energy counts and that investments translate into measurable reliability.
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