AI-Driven SCADA Energy System
Energy Consumption Map
Power Consumption Graph
Web Application Interface
Project Overview
Developed for Openserve, South Africa's largest telecommunications provider, this project addresses the rising energy demands of its data centers and terrestrial network sites, which contribute significantly to operational costs and carbon emissions. Inspired by the SATNAC 2024 Industry Solutions Challenge, our solution leverages an AI-driven Supervisory Control and Data Acquisition (SCADA) system to optimize energy consumption, enhance sustainability, and maintain network performance.
The system employs Recurrent Neural Networks (RNNs) to analyze and predict energy usage across 36 Openserve sites, using real-time data from sensors like temperature, humidity, and power sensors. Integrated with a SCADA system, it enables proactive resource allocation, anomaly detection, and predictive maintenance, reducing energy costs and environmental impact. An interactive map, built with ArcGIS ArcMap, visualizes the energy consumption of Openserve buildings across South Africa.
View Model Training Code View Project ReportEnergy Optimization
Uses RNN-based AI models to predict and optimize energy consumption, reducing operational costs and improving power usage efficiency.
Real-Time Monitoring
Integrates with SCADA to provide real-time data from sensors, enabling precise control of energy usage in data centers and network sites.
Anomaly Detection
Detects equipment malfunctions early by analyzing temperature and power anomalies, facilitating predictive maintenance.
Sustainability Impact
Reduces carbon emissions by optimizing energy use, aligning with Openserve's green initiative and regulatory compliance.
Interactive Visualization
Features an ArcGIS ArcMap-based interactive map displaying energy consumption across Openserve's 36 South African sites.
Scalable Network Management
Forecasts network traffic to allocate resources proactively, ensuring reliable connectivity and enhanced customer experience.