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AI-Powered Renewable Energy Asset Management Platform

acropolium

Energy & natural resources

Environmental Services

Utilities

Operational Challenges in Renewable Asset Management

The company faces performance variability across wind turbines and solar farms, leading to inconsistent energy output and revenue loss. Reactive maintenance practices cause unplanned downtime and increased repair costs. Fragmented data sources hinder real-time decision-making, while regulatory compliance requirements add operational complexity.

About the Client

Growing renewable energy company managing wind turbines and solar farms requiring scalable asset optimization solutions

Strategic Objectives

Implement predictive maintenance to reduce unplanned downtime by 25%
Extend asset lifespan through condition-based maintenance strategies
Centralize multi-source asset data for real-time performance analytics
Build a scalable platform supporting future renewable energy expansions

Core System Capabilities

IoT sensor data integration for wind turbines and solar panels
Machine learning models for failure prediction and maintenance scheduling
Unified dashboard with real-time performance KPIs
Automated compliance reporting and alert system
Scalable architecture for adding new asset types

Technology Stack

Node.js/TypeScript with NestJS framework

React/Next.js with Tailwind CSS

PostgreSQL with Redis caching

AWS cloud services (Lambda, S3, DynamoDB)

GraphQL/WebSockets for real-time data

System Integrations

Weather data APIs for environmental impact analysis
SCADA systems for industrial control
Third-party maintenance scheduling platforms
Regulatory compliance databases

Quality Attributes

99.99% system uptime with Kubernetes orchestration
Real-time data processing under 100ms latency
Role-based access control with GDPR compliance
Horizontal scalability to support 10x asset growth

Expected Business Outcomes

Predictive maintenance implementation will reduce emergency repairs by 40% while extending asset lifecycle by 15%. Centralized monitoring will improve energy production efficiency by 20%, with scalable architecture supporting rapid expansion into new renewable energy markets.