Transform Your BMS into a Data-Driven Energy Saver with LL97 Insights

James W.
Sep 23, 2025
3 min read

Building management systems (BMS) can feel complicated, especially when navigating the intricacies of energy management. With increasing pressures from regulations like Local Law 97 (LL97), many facility managers and building owners may believe their systems are not functioning properly. In reality, it’s about harnessing the data your BMS collects. By using data smartly, you can turn the challenges of LL97 into a strategic advantage, leading to real energy savings through effective sensor normalization, anomaly detection, and setpoint adjustments.

Understanding LL97 and Its Implications

Local Law 97 is an important law aimed at cutting greenhouse gas emissions from buildings in New York City. If you own or manage a building larger than 25,000 square feet, you must meet specific emissions limits. Failing to comply can result in fines as high as $268 per ton of greenhouse gas emissions over the specified limit. While this regulation might seem intimidating, it also provides a unique opportunity to improve energy performance in your building.

To tackle LL97 effectively, start by examining your building's energy usage. Many BMS systems capture vast datasets, but without careful analysis, this information is often wasted. By normalizing your sensor data and pinpointing anomalies, you can uncover valuable insights into your energy consumption and discover areas for enhancement.

Normalizing Sensors for Accurate Data

Inconsistent sensor data is a significant challenge when managing a BMS. Over time, sensors may drift or fail, leading to skewed energy consumption readings. For instance, a temperature sensor that consistently reads 5 degrees higher than it should could lead to unnecessary heating, inflating energy costs.

To tackle this issue, perform a comprehensive audit of your sensor network. Identify sensors that underperform or present inconsistent data. For example, if multiple sensors show drastic variations in temperature readings, it’s a sign to investigate further. Implement calibration procedures to correct inaccurate readings, which might involve adjusting setpoints or replacing faulty sensors.

By normalizing your sensors, you establish a reliable foundation for data analysis, enabling informed energy management decisions.

Surfacing Anomalies for Better Insights

Once your sensors are normalized, the next step is to surface anomalies in your data. Anomalies reflect inefficiencies or malfunctioning systems that could cause excessive energy use. For instance, if one HVAC unit is consuming 30% more energy than others in similar conditions, there's likely an underlying issue that must be addressed.

Utilizing advanced analytics tools can help you identify these anomalies swiftly. By analyzing historical data patterns, these tools can highlight irregularities. Real-time detection means you can take action before problems escalate, improving energy efficiency and ensuring compliance with LL97.

Auto-Tuning Setpoints for Optimal Performance

After normalizing your sensors and identifying anomalies, it's crucial to focus on auto-tuning your setpoints. Setpoints are the target values for various systems in your building, like temperature and humidity. If setpoints are not optimized, they can lead to inflated energy bills.

Auto-tuning involves using real-time data to adjust these setpoints actively. By employing machine learning algorithms, you can develop a system that adapts dynamically to optimize energy use. This approach not only enhances occupant comfort but can also lead to energy savings of 5-12% even before implementing physical upgrades to your systems.

The Path Forward: Data-Driven Energy Management

Transforming your BMS into a data-driven energy saver requires commitment. By normalizing sensors, identifying anomalies, and auto-tuning setpoints, you can address the challenges posed by LL97 head-on.

Here are some actionable steps to take:

Conduct a Sensor Audit: Normalize your sensors to ensure accurate data collection and reliable energy management decisions.
Implement Advanced Analytics: Use tools for real-time anomaly detection to facilitate proactive maintenance.
Adopt Auto-Tuning Technologies: Invest in machine learning solutions that dynamically adjust your building’s setpoints.
Monitor and Adjust: Keep an eye on your building's performance and make continuous adjustments to maintain efficiency and compliance.

By following these steps, not only can you meet the requirements of LL97, but you also pave the way for a more sustainable and efficient building environment.

A New Era of Energy Efficiency Awaits

It's essential to recognize that your BMS isn't malfunctioning; it's simply lacking direction. By embracing a data-driven approach to energy management, you can convert the challenges associated with LL97 into opportunities for genuine energy savings. Thanks to proper tools and smart strategies, you might achieve 5-12% savings even before considering physical improvements to your facility.

Are you prepared to start your BMS transformation journey? Join the movement towards a more energy-efficient and compliant building today!