Background Story
A college operating multiple buildings on campus, faced rising energy costs and inefficiencies in managing its HVAC systems. The institution sought to optimize energy use, enhance sustainability efforts, and comply with carbon reporting regulations. In July 2019, a building management system (BMS) and system control specialist partnered with arkEMISto integrate a cloud-based energy analytics software with its existing LoRaWAN IoT sensor platform. This collaboration aimed to expand the institution’s analytical capabilities for better energy management and conservation.
Problems
The university faced several energy management challenges
- Unnecessary HVAC Operation: Air handling units (AHUs) were running on weekends due to overwritten control rules in the BMS
- Inefficient Cooling System: Cooling was activated even when outdoor temperatures were optimal, leading to excessive energy use
- Overworked Air Handling Units: The AHU supply fan was identified as a key contributor to high energy consumption
- Lack of Data-Driven Insights: Without real-time monitoring, inefficiencies went undetected, making cost savings difficult to achieve
Main Objective
The primary goal of the project was to implement a data-driven energy management system that could:- Provide real-time monitoring of energy consumption
- Identify inefficiencies and anomalies in HVAC operation
- Improve carbon footprint tracking and sustainability performance
- Optimize the performance of existing energy infrastructure to reduce costs
Approach
To achieve these objectives a comprehensive list of Energy Conservation Measures (ECMs) were deployed, and digitalization was installed to track, monitor, and audit these ECMs. The implementation involved several critical steps:
- ITOT Integration: Smart meters and sensors were installed were connected to the platform via API allowing facility managers to access detailed consumption patterns and trends
- Cloud-Based Data Integration: Energy data from sensors was transmitted to a secure, cloud-based platform, allowing facility managers to access detailed consumption patterns and trends
- Measurement and Verification: Comprehensive analysis to verify the actual savings after each ECM was implemented, as well as the forecasted annual savings (kWh and $) and payback period after both ECMs had been implemented
- AI-Driven Analytics and Alerts: Machine learning algorithms analyzed energy patterns, detecting anomalies and predicting equipment failures before they occurred
- Energy Dashboarding: The system provided targeted energy dashboards to keep track of relevant and problematic equipment
- Automated Carbon Reporting and Compliance: The platform automatically calculated and logged carbon emissions data, generating reports aligned with government and industry sustainability standards
- User Training: Facility operators and energy managers were trained to effectively use the system, interpret data insights, and implement energy-saving measures
Results
Within the first year of implementation, the company achieved substantial improvements in both energy efficiency and operational performance
- 10% Reduction in Energy Consumption: Tracked from the efficient HVAC operation and automation
- Improved Equipment Efficiency: Real-time alerts enabled proactive maintenance, preventing equipment failures
- Real-Time Monitoring & Forecasting: he system enabled facility managers to respond promptly to alerts, minimizing energy waste
- Streamlined Carbon Reporting: Automated energy tracking improved sustainability reporting and regulatory compliance