How to use BEMS Data effectively
Using BEMS Data to Identify Inefficiencies and Plan Upgrades
A structured approach to analyzing BEMS data can reveal inefficiencies, enhance building performance, and guide system upgrades. Follow these strategies to make the most of your data:
Collect Relevant Data
Start by identifying and monitoring key performance indicators like energy consumption, HVAC runtime, temperature deviations, and system faults. For example, tracking AHU energy use, supply air temperature, and runtime can help uncover inefficiencies in operation. Use trend logs to capture these data points over time, ensuring high-frequency collection for critical metrics like temperature and humidity in sensitive zones.
Visualize Data Effectively
Data visualization is crucial for making insights actionable. Use dashboards and graphical interfaces to present building performance in a clear and interpretable format. A heat map of HVAC energy usage across zones, for instance, can quickly highlight areas of disproportionate consumption. Segment data by zones, time periods, or system types to pinpoint inefficiencies, such as comparing weekday and weekend energy use to verify appropriate scaling during unoccupied periods.
Analyze Energy Consumption
Breaking down energy data by system type—such as HVAC, pumps, and fans—helps identify the highest contributors to overall usage. Analyze trends for unusual spikes, which may indicate faults or inefficiencies. For instance, a sudden increase in boiler energy use might be traced back to a faulty valve actuator causing constant heating. This level of analysis ensures energy-saving opportunities are not overlooked.
Evaluate Equipment Performance
Assess equipment runtime to identify components operating beyond expected hours or capacities. A pump running continuously despite low demand could signal a stuck valve or faulty control logic. Additionally, check system cycling frequencies to ensure equipment isn’t short cycling, which increases wear and wastes energy. Fan start/stop logs, for example, can confirm consistent airflow without excessive cycling.
Identify System Imbalances
Compare data such as temperature, humidity, or pressure readings across zones to detect imbalances. Persistent temperature differences between adjacent zones might point to damper issues or airflow distribution problems. Cross-referencing occupancy data with system performance can also highlight inefficiencies, like high ventilation rates in unoccupied meeting rooms, which indicate a need for better demand-based controls.
Detect Anomalies and Faults
Set thresholds for key metrics to trigger alerts when performance deviates from the expected range. Monitoring CO2 levels in occupied spaces, for instance, ensures proper ventilation and prevents inefficient over-ventilation. Establish performance baselines using historical data to help identify anomalies over time, such as a chiller’s energy use increasing compared to the same season last year, indicating declining efficiency.
Plan and Prioritize Upgrades
Use insights from your analysis to create a ranked list of upgrade opportunities based on ROI and operational impact. If data shows that outdated VSDs are a major contributor to energy waste, prioritize their replacement. Additionally, identify areas for system improvements such as widening deadbands, optimizing setpoints, or automating manual processes. For example, adjusting HVAC schedules and widening temperature deadbands in low-occupancy zones can significantly reduce over-conditioning.
Incorporate Advanced Analytics
Leverage advanced analytics tools or machine learning to identify hidden inefficiencies and predict system failures. Predictive maintenance, such as analyzing motor current trends to detect early signs of pump bearing wear, can help avoid costly breakdowns. Similarly, use energy modeling tools to simulate potential upgrades like demand-controlled ventilation systems, assessing their impact before implementation.
Engage Stakeholders with Data
Share key insights with facilities management teams to gather input and operational feedback. For instance, presenting how optimizing HVAC start/stop times reduces energy costs while maintaining comfort can drive collaborative improvements. Summarize data findings for decision-makers to justify capital investments, such as a report showing how replacing aging actuators could cut energy use by 10% in specific zones.
Continuously Monitor and Improve
Data analysis isn’t a one-time activity. Set up regular reviews to track progress and adapt to changes in building use. Monthly energy performance reviews, for example, ensure that upgrades and adjustments achieve the desired results. Keep configurations updated based on new insights and technologies, such as integrating occupancy sensors to refine demand-based HVAC control in underutilized areas.
