In recent years, the importance of sustainability has risen to the top of the public agenda. People are becoming more conscious of their environmental impact, and businesses are being held accountable for their contribution to climate change. With this increased awareness, companies are striving to meet increasing innovation demands in product development and customer service, while carefully juggling resources and striving for the lowest possible environmental impact. Maintaining a business that is simultaneously environmentally and commercially sustainable, across the entire supply chain, has never been more difficult nor more important.
One of the major causes for concern in the building sector is energy consumption. Heating, ventilation, air-conditioning and refrigeration systems (HVAC or HVACR) are responsible for up to 60% of a commercial building’s energy consumption. This not only puts a strain on resources, but it also results in higher energy costs and a greater carbon footprint. Fortunately, there is a solution: IoT technologies that enable data-driven energy efficiency initiatives.
The following are three major considerations when starting a data-driven energy efficiency initiative.
1. Integrated Data
Real-time data collection across disparate building systems is critical. In order to achieve optimal energy efficiency, data must be collected from all the different HVAC systems in a building. However, it's not just about collecting data — it's also about making sure that data is integrated into a data processing platform that is open, flexible, and enables easy integration across multiple vendors, communication protocols, or equipment brands. Buildings generate data from multiple sources, not only machines but also humans and IT systems. Therefore, it's important to ensure that there is a way for contextual information to reach the right systems.
By integrating data from multiple sources, it becomes possible to gain a comprehensive view of a building's energy consumption patterns. This enables facility managers to identify which systems are using too much energy or which need to be maintained or upgraded. With real-time data, building managers can make informed decisions about energy consumption and optimize systems to reduce energy waste.
2. Predictive Analytics
Analyzing data for consumption patterns that are wasteful and for optimization opportunities is critical. Predictive analytics uses historical data to create models of how systems should be running. By comparing current data to these models, it becomes possible to identify abnormal behavior and predict equipment failure before it happens. This allows for planned maintenance instead of reactive maintenance.
For instance, if the data shows that a particular HVAC system is starting to consume more energy than usual, this could indicate a potential problem. By identifying this issue early on, facility managers can proactively schedule maintenance to address the issue, rather than waiting for a catastrophic failure that requires costly repairs.
3. Workflow Automation
Setting up logic controls to automate actions across building systems is necessary. Automated controls can reduce energy consumption by turning off lights or adjusting HVAC settings when spaces are unoccupied. They can also provide alerts when systems are not functioning properly and trigger work orders for service technicians.
Workflow automation is critical because it enables facility managers and field engineers to intervene and troubleshoot issues quickly. This can help prevent equipment failure and reduce downtime. With automated controls, building managers can easily monitor energy consumption in real-time and make adjustments as needed to ensure optimal energy efficiency.
In conclusion, data-driven energy management initiatives can help commercial buildings become sustainable by optimizing equipment operations while still maintaining overall comfort for building occupants and keeping business-critical systems in check. By implementing these three considerations, building owners and facility managers can significantly reduce energy costs, improve operational efficiency, and meet their sustainability goals. The integration of data, predictive analytics, and workflow automation can lead to smarter, more efficient buildings that benefit both the environment and the bottom line.