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Data Analytics & AI to Optimize Renewable Operations and Asset Performance

Anticipating risks, detecting inefficiencies and improving decisions across development, construction and operations through advanced analytics and AI

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Renewable asset engineering, construction, and operational processes remain largely reactive and often underuse the vast amount of available data. Limited integration across asset, site, and enterprise sources, weak predictive capabilities, and manual decision-making reduce efficiency, increase risk, and limit the ability to anticipate issues across the asset lifecycle.
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

WHY DATA ANALYTICS & AI TO OPTIMIZE ASSET PERFORMANCE?

Data Analytics and AI help renewable companies connect operational, engineering, market and environmental data to identify deviations earlier, prioritize actions and optimize performance

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

Key benefits

Enhancing asset and process performance through predictive insights, automation, and data-driven decision-making

Earlier detection of performance deviations

Identifies anomalies in production, availability, curtailment, losses and operational behaviour before they become recurring underperformance issues

Better planning and prioritization of actions

Supports maintenance, construction follow-up and operational planning by prioritizing interventions based on impact, risk and business value

Reduced operational and execution risks

Anticipates bottlenecks, failures, delays and critical events by combining historical patterns, real-time data and predictive models

Scalable analytics across portfolios

Industrializes analytical capabilities across technologies, geographies and business units, enabling consistent decisions at asset and portfolio level

How NTT DATA helps

01

Analytics use case identification and value roadmap

Prioritizes high-impact use cases across development, engineering, construction and operations, linking each initiative to measurable business value

02

Data integration across renewable systems

Connects SCADA, CMMS, ERP, GIS, BIM, IoT, meteorological and market data to create a reliable foundation for analytics and AI

03

Predictive models, forecasting and anomaly detection

Designs and implements models for production forecasting, asset underperformance, failure prediction, curtailment analysis, construction delays and operational risks

04

Process mining and performance intelligence

Analyzes real process execution to identify bottlenecks, rework, cycle-time deviations and automation opportunities

05

AI governance, explainability and industrialization

Defines data governance, model monitoring, adoption frameworks and scalable platforms to move from pilots to operational use

From Fragmented Systems to a Future-Ready Architecture

Proven impact

From fragmented data to predictive, scalable operations

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

Results that matter

Renewable energies organizations achieve measurable improvements across asset-intensive operations:

Improved production forecasting and planning accuracy

Better short-, medium- and long-term forecasts to support operations, market decisions and resource planning

Reduced process cycle times and operational inefficiencies

Identification of bottlenecks, rework and manual activities through process mining and advanced analytics

Lower unplanned downtime and faster issue detection

Earlier identification of anomalies, failures and recurring underperformance patterns

Optimized maintenance and resource allocation

Prioritization of interventions based on risk, asset criticality, expected impact and cost efficiency

Turn renewable data into operational advantage

Anticipate deviations, optimize decisions and scale AI across your portfolio

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