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Development of an Internal Analytics Dashboard for Renewable Energy Storage Systems
  1. case
  2. Development of an Internal Analytics Dashboard for Renewable Energy Storage Systems

Development of an Internal Analytics Dashboard for Renewable Energy Storage Systems

https://soltech.net
Energy & natural resources

Challenge of Monitoring and Optimizing Energy Storage Systems

The client faces difficulties in effectively monitoring the performance of their renewable energy storage units, such as flow batteries. They need a centralized solution to track critical metrics, enable real-time performance analysis, and quickly identify maintenance needs to maximize efficiency and reliability.

About the Client

A mid-sized energy solutions provider specializing in innovative energy storage technologies, aiming to optimize operational performance and fuel industry leadership.

Goals for Implementing an Advanced Performance Monitoring System

  • Develop a comprehensive dashboard that displays real-time key performance indicators for each energy storage unit.
  • Enable advanced analytics and predictive maintenance insights to improve system uptime and prolong asset lifespan.
  • Facilitate swift decision-making to optimize energy storage efficiency and reduce operational costs.
  • Support scalable integration of multiple storage units across various locations.

Core Functionalities and Features of the Monitoring Dashboard

  • Data aggregation from various energy storage units, including performance metrics and operational status.
  • Real-time data visualization dashboards with customizable views.
  • Automated alerts for abnormal performance or maintenance needs.
  • Predictive analytics for proactive maintenance and system optimization.
  • Role-based access controls for secure data management.
  • Historical data tracking for trend analysis and reporting.

Recommended Technologies and Architectural Approach

Advanced analytics platforms
Real-time data visualization tools
Cloud-based infrastructure for scalability
Predictive analytics and machine learning models

Systems and Data Sources for Integration

  • Data feeds from energy storage units (SCADA or IoT sensors)
  • Maintenance management systems
  • User authentication and role management systems

Critical Non-Functional System Requirements

  • System scalability to support growing number of storage units
  • High availability and reliability with 99.9% uptime
  • Real-time data processing with minimal latency
  • Robust security measures to protect sensitive operational data

Projected Business Outcomes and Benefits

The implementation of this analytics dashboard aims to enable the client to maximize energy storage efficiency, reduce maintenance costs through predictive insights, and improve system reliability. By centralizing and visualizing key performance data, the client expects quicker decision-making and enhanced operational performance, ultimately supporting their leadership in renewable energy solutions and enabling scalable growth.

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