stilhere4huniid/Mall-of-Zimbabwe-Energy-Digital-Twin
Domain:
environment and energyRecord type:
project
Creator:
sti
Host:
A physics-based Digital Twin simulating the operational energy future of the 90,000m² Mall of Zimbabwe. Uses Python & XGBoost to forecast demand, test solar microgrids, and identify $1.66M in annual savings (Pre-Construction Feasibility Study).
# 🇿🇼 Mall of Zimbabwe: Predictive Energy Digital Twin
> **Strategic Feasibility Study:** Predicting the operational energy future of the 90,000m² Mall of Zimbabwe before construction begins in 2026.
## 🏙️ Executive Summary
WestProp Holdings is developing the Mall of Zimbabwe, a $100M+ "Smart City" asset in Harare. To secure anchor tenants and optimize Capital Expenditure (CapEx), we built a **Digital Twin** to simulate the mall's energy consumption, costs, and grid resilience.
**The Result:** We identified **$1.66 Million** in annual savings by integrating a 4MW Solar Microgrid and AI-driven HVAC controls into the pre-construction blueprints.
## 📊 Key Results
| Metric | Baseline (Standard Build) | Optimized (Smart Build) | Impact |
| :--- | :--- | :--- | :--- |
| **Annual Demand** | 26.6 GWh | 14.5 GWh | **-45%** |
| **Annual Bill** | $3.7 Million | $2.0 Million | **$1.66M Saved** |
| **Grid Reliance** | 94% (High Risk) | 60% (Resilient) | **Energy Security** |
## 🧠 The AI Model Showdown
We benchmarked three forecasting approaches to validate our financial projections.
* **🏆 XGBoost (Winner):** 97.9% Accuracy (MAPE: 2.07%). Handling ZESA load shedding patterns best.
* **❌ LSTM (Deep Learning):** 94.6% Accuracy. Slower to adapt to sudden grid outages.
* **❌ SARIMA (Statistical):** 88.2% Accuracy. Good for seasonality but failed to capture holiday-specific spikes.
* **❌ Prophet (Baseline):** 69.0% Accuracy. Failed to capture complex operational hours.
## 📂 Project Structure
├── Data/ # Raw & Calibrated Simulation Data
├── Visualizations/ # EDA & Model Performance Charts
├── Deliverables/ # Client-Facing Reports
│ ├── Mall_of_Zimbabwe_Strategic_Brief.pdf (Executive Memo)
│ └── strategic_energy_dashboard.html (Interactive Dashboard)
└── Project_Notebook.ipynb # Full Source Code
## ⚡ Quick Start
To reproduce the results or run the simulation on your local machine, follow these steps:
# 1. Clone the Repository
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