Data Analytics & AI to Optimize Renewable Operations and Asset Performance
Renewable energy companies are managing increasingly large, distributed and complex portfolios, while many engineering, construction and operational decisions still depend on fragmented data, manual analysis, and reactive processes. So the main challenge here is to turn data into reliable, timely and actionable decisions across the asset lifecycle
By combining SCADA, CMMS, ERP, GIS, BIM, meteorological, market and historical data, organizations can improve forecasting, detect anomalies, simulate scenarios and make better decisions before issues become costly
Identifies anomalies in production, availability, curtailment, losses and operational behaviour before they become recurring underperformance issues
Supports maintenance, construction follow-up and operational planning by prioritizing interventions based on impact, risk and business value
Anticipates bottlenecks, failures, delays and critical events by combining historical patterns, real-time data and predictive models
Industrializes analytical capabilities across technologies, geographies and business units, enabling consistent decisions at asset and portfolio level
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Prioritizes high-impact use cases across development, engineering, construction and operations, linking each initiative to measurable business value
Connects SCADA, CMMS, ERP, GIS, BIM, IoT, meteorological and market data to create a reliable foundation for analytics and AI
Designs and implements models for production forecasting, asset underperformance, failure prediction, curtailment analysis, construction delays and operational risks
Analyzes real process execution to identify bottlenecks, rework, cycle-time deviations and automation opportunities
Defines data governance, model monitoring, adoption frameworks and scalable platforms to move from pilots to operational use
NTT DATA helps renewable energy organizations turn operational and process data into actionable intelligence, improving visibility, anticipation and performance across engineering, construction and operations. The result is a more proactive operating model, where teams can detect deviations earlier, prioritize actions better and scale analytics across the portfolio
Better short-, medium- and long-term forecasts to support operations, market decisions and resource planning
Identification of bottlenecks, rework and manual activities through process mining and advanced analytics
Earlier identification of anomalies, failures and recurring underperformance patterns
Prioritization of interventions based on risk, asset criticality, expected impact and cost efficiency
Anticipate deviations, optimize decisions and scale AI across your portfolio
