Mohit Kumar Singh, Founder & CEO of Renewalytics Services Pvt. Ltd., is leveraging artificial intelligence to solve critical operational challenges in the renewable energy sector. His company builds AI-powered operational intelligence platforms that help solar and wind operators improve efficiency, grid reliability, and intelligent energy management while supporting India’s clean energy transition.
Singh’s journey in renewable energy began in December 2016 at Climate Connect, where he worked extensively on forecasting, scheduling, and grid compliance for renewable energy generators. Over the years, he collaborated with independent power producers, operations teams, regulators, and grid stakeholders, gaining first-hand exposure to the operational complexities of integrating renewable energy into the power system.
After Climate Connect was acquired by ReNew Power and later divested, Singh founded Renewalytics in 2023 with the belief that the industry needed an independent company focused entirely on intelligent software for renewable energy operations. Today, Renewalytics supports more than 3 GW of solar and wind assets across commercial and pilot engagements, offering AI-driven forecasting, scheduling, weather intelligence, DSM analytics, enterprise software, operational automation, and intelligent decision-support systems.
Solving Real-World Challenges
India’s renewable energy targets are ambitious, but operational complexity is growing rapidly. Renewable energy companies must manage weather uncertainty, grid regulations, scheduling obligations, battery integration, diverse data sources, operational risks, and increasingly complex market dynamics.
Renewalytics addresses these challenges by transforming fragmented operational data into actionable intelligence. As Singh explains, “Our objective is to help renewable energy companies improve operational visibility, automate routine workflows, strengthen decision-making, and optimize plant performance.”
How AI is Transforming Forecasting and Operations
Artificial Intelligence is fundamentally changing how renewable energy assets are managed. Machine learning enables continuous learning from historical generation patterns, weather behavior, plant performance, and operational outcomes to improve prediction accuracy and support better planning.
Beyond forecasting, AI is assisting operations teams through intelligent co-pilots that validate data, detect anomalies, recommend corrective actions, and automate repetitive tasks. At Renewalytics, the team is building agentic AI capabilities where specialized AI agents collaborate to support weather intelligence, operational analytics, forecast validation, data quality monitoring, and performance optimization.
“Our philosophy is that AI should augment human expertise rather than replace it,” Singh says. “We aim to enable engineers and operations teams to make faster and better-informed decisions.”
Differentiation from Traditional Solutions
Traditional energy management platforms primarily focus on monitoring assets and reporting operational data. Renewalytics goes beyond monitoring by combining AI, machine learning, operational intelligence, automation, weather analytics, scheduling, DSM analytics, and enterprise workflow management into a unified platform.
Singh notes that his team’s practical experience within the renewable energy ecosystem allows them to build solutions that address real operational challenges instead of simply presenting data on dashboards. “We believe the future lies in intelligent platforms that actively assist decision-making rather than simply displaying information,” he adds.
The Role of Data in Renewable Energy Performance
Data is the foundation of every intelligent renewable energy system, but the true value lies in its quality, reliability, and contextual understanding. Renewable energy operations depend on information from SCADA systems, weather services, meters, grid operators, market platforms, scheduling systems, and operational logs.
“At Renewalytics, significant emphasis is placed on data engineering, validation, automation, and integration because reliable AI models can only be built upon reliable data,” Singh explains. High-quality data ultimately leads to better operational decisions, improved efficiency, and stronger grid reliability.
Future Trends and Vision
Singh believes the next decade of renewable energy will extend far beyond capacity addition. The sector will increasingly be shaped by battery energy storage systems, hybrid renewable projects, AI-powered operational co-pilots, autonomous software agents, intelligent forecasting, grid optimization, and advanced machine learning.
“As renewable penetration continues to increase, maintaining grid stability will become just as important as adding new generation capacity,” he says. “Utilities, grid operators, and renewable energy companies will rely far more heavily on predictive analytics, flexible energy resources, and intelligent software platforms to maintain system reliability.”
Looking ahead, Singh envisions Renewalytics evolving into a digital operating layer that helps organizations improve efficiency, strengthen grid reliability, and accelerate the global clean energy transition. “Our long-term ambition is to build technology from India that serves renewable energy companies across international markets,” he concludes.
Leave a Reply