This project is a comprehensive data intelligence system designed to optimize operational efficiency and tenant comfort for Home Tech Solution. By integrating Data Analytics, Business Intelligence, and AI driven automation, I created a solution that monitors 120 smart apartments in real time.
The system leverages IoT sensor data managed through Excel Pivot Tables and Power Query, visualized on an interactive dashboard, and made accessible via a natural language AI assistant.
Home Tech Solution monitors a network of 120 smart apartments. My analysis indicates a generally high average energy efficiency of 89% and a strong maintenance team performance with a 94% completion rate. However, significant opportunities exist to reduce energy costs by optimizing HVAC settings and shifting peak load times.
The analysis identified HVAC systems as the single largest driver of both energy consumption and maintenance tickets. By addressing high intensity usage patterns, we can significantly extend equipment lifespan and reduce operational costs.
Occupancy Impact: Occupied units account for 22.5% of energy data points versus 13.7% for unoccupied, confirming a direct correlation between occupancy and load.
Time of Day Trends: Energy consumption peaks in the Evening (25.63%), followed by Afternoon (17.78%). This suggests that energy saving measures should target post work hours when tenants return home.
HVAC Impact: There is a strong positive correlation between HVAC settings and energy use. Units with "High" settings consume an average of 23.65 units, compared to just 9.75 units for "Low" settings.
Top Issues: HVAC failures dominate the maintenance logs, accounting for 44% (153 cases) of all issues. This is followed by filter replacements (88 cases) and thermostat failures (54 cases).
Seasonal Trends: Maintenance requests spike in January to April and October to December. These periods coincide exactly with peak energy consumption, suggesting that heavy load strain is the primary cause of equipment failure.
Resolution: 82% of issues required part replacements, indicating that problems are mechanical failures rather than simple cleaning tasks.
I developed an AI Knowledge Base to democratize access to these insights. Staff can simply ask the system:
Action: Implement smart thermostat limits to discourage "High" settings, which consume 2.4x more energy than "Low" settings.
Action: Schedule pre emptive HVAC checks in September and December, just before the identified seasonal spikes.
Action: Educate tenants or automate non essential systems to reduce load during the peak Evening hours (25.63% of usage).
Action: Investigate Unit U493 (highest maintenance count) for potential equipment aging or insulation issues.
The data confirms that HVAC usage is the central lever for both cost and operational stability at Home Tech Solution. By shifting from reactive repairs to predictive maintenance based on seasonal energy spikes, and by using the AI assistant to monitor anomalies in real time, the company can significantly extend asset lifespans and improve tenant satisfaction.