Development of an AI-Driven Energy Optimization and Visualization System for Microgrid Management

The client faces significant challenges in monitoring, forecasting, and optimizing energy consumption and production in decentralized, renewable-based energy systems. Limited visibility and predictive capabilities hinder efficient utilization and scalability of microgrid infrastructure, leading to suboptimal operation and higher costs in remote and underserved areas.

A technology-driven energy provider focused on implementing renewable, decentralized energy systems and microgrid management in remote and underserved regions.

• Design and implement a comprehensive internal analytics dashboard to visualize real-time and historical energy data from multiple microgrid sites.
• Develop advanced energy consumption and production forecasting models using machine learning techniques tailored to regional seasonality and patterns.
• Create an interactive prototype demonstrating predictive analytics to support decision-making and operational optimization.
• Ensure the solution provides actionable insights, cost savings, and supports scalable deployment in diverse geographic locations.
• Establish a foundation for iterative improvement with capability to incorporate additional data sources and enhance predictive accuracy over time.

• Measurement and visualization of energy consumption and production data through interactive dashboards.
• Integration of smart meters and external weather data APIs for contextual insights.
• Implementation of machine learning models to generate 36-hour energy consumption forecasts with updates based on recent data.
• Simulation capabilities to demonstrate forecast accuracy and seasonal variation patterns.
• Real-time updates of forecasts and actual consumption comparisons within the dashboard.
• Notification system for relevant alerts and device status updates.
• Deployment of scalable, secure cloud infrastructure supporting the system’s accessibility and performance.

• Weather data APIs for contextual forecasting
• Smart meter data feeds from energy production sites
• Notification and alert services connected to connected devices
• Cloud storage and processing services for data management

• System scalability to handle increasing data volume from multiple sites and future expansion
• High availability and reliability to ensure continuous monitoring and forecasting
• Data security and user privacy compliance
• Performance benchmarks ensuring real-time updates and forecast generation within acceptable timeframes (e.g., under 1 minute for updates)
• Usability with an intuitive UI suitable for technical and non-technical stakeholders

This system will enhance microgrid management by enabling accurate energy consumption predictions, improving operational efficiency, reducing costs through optimized resource utilization, and supporting scalable deployment of renewable energy solutions in remote regions. It will establish a data-driven decision-making platform that aligns with sustainable energy access goals, similar to proven successful pilot demonstrations and technological advancements in the industry.