Maximizing Energy Efficiency: Tips for Using Building Analytics Software

Organizations today need to reduce wasted energy to cut costs and meet green goals. Facility managers can use building analytics software. This software will optimize energy use in their buildings. This article gives facility managers various tips. In order to gain the most out of analytics software to improve efficiency. It covers best practices like setting up the software, tracking important metrics, finding waste, prioritizing upgrades, and keeping occupants comfortable.

Following these tips will help organizations reduce energy use and expenses. 

Understanding the Potential of Building Analytics Software

Building analytics software uses data and tech to optimize operations and cutting down energy wastage. These tools have become priceless for commercial, industrial, and institutional buildings.

The software uses IoT sensors, smart meters, and data analytics to give deep insight into how buildings perform. It gathers, analyzes, and acts on building data nonstop to improve energy efficiency. Commercial building energy management systems can provide facility managers with the data and insights needed to minimize wasted energy.

Key benefits provided by building analytics software include:

• Lower maintenance costs – Predictive algorithms spot equipment faults early. This allows preventive maintenance and avoids failures.

• Improved occupant comfort – Constant monitoring helps in maintaining ideal temperatures.

• Sustainability goals – Building analytics is critical for achieving environmental targets like net zero carbon.

• Financial savings – Energy is a massive operating expense. Analytics helps curb utility bills.

• Regulatory compliance – Software allows easier compliance with energy benchmarking laws.

Harnessing the Power of Data Collection

At the heart of any building analytics implementation is a robust data collection infrastructure:

Sensors and IoT Devices: Buildings need to be equipped with hundreds of sensors and internet-connected devices for monitoring:

• Smart thermostats record localized temperature data across zones. Occupancy sensors track the usage of rooms.

• Lighting sensors measure and log the exact lighting needs. Motion detectors supplement this.

• Equipment sensors monitor chiller electricity use, server load, elevator runtime, and other critical systems.

• Air quality monitors track CO2, particulate matter, VOCs, and humidity levels.

• Leak and flood detectors alert about pipe bursts and water damage.

• And many more – fire detection, parking utilization, etc.

Interval Metering: Energy meters must capture granular data at frequent intervals, ideally every 15 minutes. This is a significant upgrade from monthly manual readings. Interval metering reveals usage patterns.

Normalized Data: Data from disparate sources is normalized into consistent formats for analysis. This involves data validation, cleansing, and interpolation to handle missing readings.

Cloud Connectivity: Sensors and meters connect to the cloud to transmit their data securely to the analytics software in real-time or near real-time.

Here’s a pie chart showcasing the total commercial building expenditures by major fuel:

Critical Analytic Features to Optimize Energy Use

Powerful analytics convert the wealth of collected data into valuable insights:

• Energy Pattern Analysis: The software assesses energy usage profiles across weeks and seasons. It surfaces opportunities to improve schedules, sequences, and setpoints to cut waste.

• Peak Load Forecasting: Analyzing trends forecast the timing and sizing of peak demand. This knowledge helps optimize load balancing and avoid capacity charges.

• Anomaly Detection: monitoring performance data spots anomalies suggesting potential faults. These issues are bad sensors. Early anomaly detection enables preventive maintenance.

• Fault Detection and Diagnostics (FDD): This critical functionality identifies faults at the equipment level. FDD spots issues like leaky valves, stuck dampers, and failing actuators.

• Benchmarking and Scorecards: The building’s current performance is compared against past performance, simulated models, or other peer buildings. Performance is scored on key efficiency metrics.

• Predictive Maintenance: Machine learning algorithms predict equipment failures before they occur. This enables proactive maintenance. This minimizes downtime.

• Automated Notifications: Analytics systems generate and send alerts to facility teams. Alerts like urgent issues, anomalies, or optimizations. This enables rapid response.

Integrating with Building Management Systems

Tight integration between building analytics software and BMS unlocks greater efficiency:

• Automated Control: The BMS can install changes prescribed by the analytics system. Changes like resetting temperatures or dimming lights in vacant spaces.

• Operational Insights: Combining BMS control capabilities with analytics provides complete system-level visibility and control.

For instance, the analytics may detect an anomaly in the chiller plant. The BMS can then be used to gather sensor diagnostics. This will help to tune operations for peak efficiency.

Single Pane of Glass: A unified BMS + analytics dashboard provides facility managers with a glass view. No more switching between multiple applications.

Seamless Data Sharing: Tight coupling allows seamless data sharing between the two systems. The analytics leverage BMS data and the BMS responds to analytic insights.

According to JLL, BMS + analytics integration realizes 5-20% greater energy savings than using either solution alone. The combined power is greater than the sum of parts.

Utilizing Advanced Algorithms and Machine Learning

Sophisticated algorithms amplify the power of building analytics:

Predictive Modeling: Machine learning models are trained to forecast energy consumption, equipment failures, maintenance needs, etc. This facilitates planning, maintenance, and demand management.

Optimization: Simulations and mathematical optimization help determine optimal operating parameters. This enhances efficiency across different conditions.

Pattern Recognition: Machine learning algorithms can detect subtle patterns in usage, performance, and faults that humans may miss. They surface hidden insights.

For instance, AI may identify that a certain chiller functions at lower loads. Operators can adjust their sequencing.

Automatic Model Training: As more data comes in, ML models re-train themselves to become more precise. They learn the unique characteristics of the building.

Cognitive Learning: With deep learning, systems can even leverage vision and speech. That will lead to smarter human-machine collaboration.

Strategies for Effective Data Visualization

Visualization turns abstract data into powerful insights:

• Intuitive dashboards tailored to various stakeholders – corporate owners, facility managers, sustainability heads, etc.

• Use of color coding to quickly highlight metrics of interest against key thresholds.

• Charts like heat maps, scatter plots, and Sankey diagrams to uncover usage patterns.

• Interactive graphs that allow drilling down from facility to equipment level views.

• Mobile access to dashboards via smart tablets and phones for on-the-go visibility.

• Custom reporting to match the specific analytics needs of the organization.

Good visuals promote engagement across teams. They quickly convey performance issues and opportunities. Focus visualization on critical KPIs and anomalies for maximum impact.

Ensuring Data Accuracy and Integrity

Bad data leads to bad analytics. Maintaining integrity is crucial:

• Sensor calibration and meter configuration should be done regularly using commissioning processes.

• Data validation to catch outliers, stuck values, gaps, or limits exceeded.

• Data cleaning procedures like smoothing and interpolation to handle missing readings.

• Cybersecurity protections against false data injection attacks.

• Quality management of provided data services if relying on external parties.

Data validation checks should occur before analysis, preferably at data ingestion. This avoids corrupting downstream systems and decisions.

Regular Review and Continuous Improvement

Don’t deploy building analytics in “set and forget” mode:

• Tune analytical models regularly as more data provides insights into areas of improvement.

• Expand metering and monitoring to new building areas over time to optimize further. Consider submetering at the equipment level.

• Review utility rates and tariffs annually and adjust load management strategies accordingly.

• Update software to leverage emerging technology like augmented reality, drones, and 3D mapping.

• Refine energy policies and procedures based on software findings – for instance, updating temperature setpoints.

Achieving progress requires iterative review and enhancement of data practices, software features, and energy strategies.

Final Thoughts

Building analytics software has huge potential. It cuts down wasted energy in buildings while keeping occupants comfy. To get the best results, building owners need to invest in solid data collection and integrate analytics. Along with building and equipment controls.

Prioritizing predictive features like machine learning and anomaly detection is crucial. And it’s essential to keep reviewing and refining analytics setups to keep improving them. The promise of data-driven efficiency is clearly here.

Frequently Asked Questions

1. How frequently should the software be updated?

Building analytics systems should be updated quarterly. Regular upgrades ensure the software has the latest features. It also improves cybersecurity. Both are crucial for effectiveness.

2. What are the risks of solely relying on the software?

Blind trust in any technology is risky. Software may miss certain patterns or produce inaccurate insights. Thus, human oversight of data and recommendations is essential. Analytics provides support, not complete automation.

3. How can I ensure data security?

Use cybersecurity protections like data encryption, access controls, network security, and physical data storage. To safeguard privacy, anonymize and aggregate data before analysis. Setting clear data security policies is also important.