Live Demo / Screenshots: Available at thunderenergy.ai (Screenshots included in this repo illustrate key components you've seen.)

Thunder is an intelligent energy management platform built for telecom and infrastructure operators. It enables real-time monitoring, data‑driven analysis, autonomous optimization, and automated field actions to reduce OPEX and carbon footprint :contentReference[oaicite:2]{index=2}.

Visualize live metrics from grid, solar, diesel generators, batteries, rectifiers, and transformers.

Provide on-demand control and adjustments across distributed assets :contentReference[oaicite:3]{index=3}.

Interactive dashboards displaying energy consumption, performance trends, and health insights.

Enables root cause analysis through deep analytics :contentReference[oaicite:4]{index=4}.

Automated suggestions to reduce peak-hour grid usage and generator runtime.

Forecast-driven asset management—battery charging, generator usage, and load balancing :contentReference[oaicite:5]{index=5}.

Telecom case study (Telenor Pakistan): saved up to 5 MWh/day energy, reduced CO₂ emissions by 2,430 tonnes, and achieved USD 5.67 M OPEX savings :contentReference[oaicite:6]{index=6}.

System triggers control changes such as switching energy sources or disabling generators when thresholds are crossed :contentReference[oaicite:7]{index=7}.

Ingests high-frequency data from IoT gateways across thousands of sites.

Uses ML/AI algorithms to model trends, predict outages, and recommend operations :contentReference[oaicite:8]{index=8}.

Layer Technologies/Methods Frontend Next, TypeScript, Tailwind CSS Visualization D3.js / ECharts / Chart.js State Mgmt Redux / Context API API REST endpoints (data ingestion, control commands) Mapping Integrated with Mapbox / other geo tools Automation RxJS / event-driven control handlers ML / AI Predictive models executed server-side, surfaced in UI Deployment Docker, CI/CD, performance & security optimizations

All configuration forms and parameter inputs are defined via JSON schemas.

The UI dynamically renders fields with validation, defaults, and conditional logic—no manual frontend updates needed.

Enables rapid updates and new feature rollout by simply updating JSON definitions.

This platform was built in a professional environment and uses proprietary protocols and data. Public screenshots are sanitized and shared with permission. The live app is not publicly accessible without credentials.

Ahmad Anis Frontend Engineer 📍 Germany 📫 theahmdanis1@gmail.com | LinkedIn

I led the front‑end, focusing on modular design, interactive data visualizations, dynamic UIs, and performance tuning.

Collaboration with backend and ML teams was key to surface accurate, actionable intelligence.

Continuous integration, end‑to‑end testing, and CSS-in-JS approaches were used to ensure maintainability and UX consistency.