GreenPulse AI is designed to transform renewable-energy operational data into meaningful information for monitoring, analysis, forecasting and operational decision-making.
GreenPulse AI is an intelligent renewable-energy operations platform designed to support the monitoring and analysis of hybrid solar and wind energy systems. The platform centralizes operational information from renewable assets, generation systems and grid conditions into a unified interface.
The system combines operational dashboards, renewable-energy monitoring, short-term forecasting, alert management, asset monitoring, maintenance-risk analysis, simulation capabilities and an AI conversational assistant. The platform can operate with simulated data, allowing development, testing and demonstration without requiring direct access to physical renewable-energy infrastructure.
Renewable-energy operators may need to monitor multiple assets and operational parameters simultaneously. Identifying underperforming assets, generation deficits, maintenance risks and grid conditions can become difficult when information is distributed across different systems.
GreenPulse AI addresses this challenge by providing a centralized operational platform that combines monitoring, visualization, forecasting, alerts, simulation and AI-assisted analysis. This approach helps transform operational data into information that can support faster and more informed decisions.
The main objective of GreenPulse AI is to develop an intelligent renewable-energy operations platform capable of monitoring hybrid solar and wind systems, analysing operational conditions and assisting users in making better operational decisions.
The operational dashboard provides a centralized view of the renewable-energy system. It displays key performance indicators, generation information, operational status, alerts, maintenance risks, forecasts and grid conditions.
The Solar Monitoring module provides visibility into solar-energy generation and connected solar assets. It includes information such as solar generation, irradiance, ambient temperature, inverter activity, efficiency and asset status.
The Wind Monitoring module provides operational visibility into wind generation and turbine activity. It allows users to analyse wind contribution to the hybrid renewable-energy system and monitor turbine-related information.
The Forecasting module provides short-term renewable-generation predictions. Forecast information can be used to understand expected generation levels, possible variations and potential generation deficits or surpluses.
The Grid Monitoring module analyses renewable generation in relation to grid demand. It helps identify generation surplus or deficit conditions and provides information that can support operational planning.
The platform detects and presents operational alerts according to their severity. Users can identify active conditions and distinguish between normal, warning, high and critical operational situations.
GreenPulse AI provides centralized visibility of renewable-energy assets, including solar inverters and wind turbines. Asset information can include operational status, generation, efficiency and other relevant parameters.
The maintenance module helps identify assets associated with potential maintenance risks. High and critical conditions can be highlighted to support proactive operational management.
The AI Assistant allows users to interact with the platform using natural-language questions. It provides AI-assisted operational information, analysis and recommendations based on available system information.
The project integrates IBM AI services to support intelligent interaction and operational decision support.
GreenPulse AI includes simulation capabilities that generate operational data for testing, development, demonstration and analysis.
Simulation makes it possible to demonstrate the platform without requiring direct connection to physical solar panels, inverters, wind turbines or other renewable-energy infrastructure.
Operational information is presented using dashboards, charts, KPI cards, tables, indicators and status components to make complex renewable-energy data easier to understand.
The platform is designed to adapt to different screen sizes, including desktop, tablet and mobile devices.
GreenPulse AI follows a modular architecture in which the user interface, backend services, data processing and AI capabilities work together.
The main architectural components include:
- Frontend application
- Backend services
- Operational data processing
- Renewable-energy simulation
- Forecasting services
- Alert and risk analysis
- AI Assistant
- IBM AI services
- REST-based communication
- Data visualization layer
The architecture allows the platform to separate presentation, processing, AI capabilities and operational logic.
- TypeScript
- HTML
- CSS
- Component-based frontend architecture
- Recharts
- Lucide React
- Python
- REST APIs
- Data processing
- Forecasting logic
- Simulation services
- Operational analysis
- IBM watsonx.ai
- IBM Granite models
- IBM Cloud services
- AI-assisted operational analysis
- Natural-language interaction
- Git
- GitHub
- npm
- Sigma
- Canva
- Figma
- Browser Developer Tools
GreenPulse-AI/
│
├── backend/
│ ├── ...
│ └── ...
│
├── frontend/
│ ├── public/
│ └── src/
│ ├── assets/
│ │ ├── dashboard.png
│ │ ├── forecasting.png
│ │ ├── greenpulse-cover.jpg
│ │ ├── solar-monitoring.png
│ │ └── wind-monitoring.png
│ │
│ ├── components/
│ ├── hooks/
│ ├── pages/
│ ├── services/
│ └── ...
│
├── logo.png
├── README.md
└── .gitignore