Buildings account for up to 40% of energy use in many countries and are therefore, a primary target for achieving energy efficiency. Today building owners face the challenge of improving the financial return from their facilities while also trying to drive sustainability outcomes.
Data analytics is one of the most effective tools that building managers can use to improve a manufacturing facility’s efficiencies. By collecting data and effectively analyzing it in buildings, operators can reduce equipment and energy costs dramatically.
Heating, ventilating and cooling (HVAC) contributes up to 50% of a building’s total power usage so it is logical to optimize building efficiency and achieve sustainability outcomes.
Recent progressions in big data have allowed building managers to achieve improvements in plant and equipment efficiency, which has allowed them to reduce energy costs, equipment outages and tenant discomfort.
However, in order to achieve the maximum operational efficiencies from data analytics, facility managers must first derive the most comprehensive insights from their building’s data.
Using big data analytics
The use of big data analytics is fundamental to the maintenance and cost efficiency of running a facility.
For buildings that install the right big data analytics tools, it lets them to make educated decisions when it comes to addressing problems and repairing equipment before critical failure. Right now, problems such as pointless equipment operation, suboptimal strategies, faulty equipment or poorly tuned loops are going undiagnosed and in turn create energy wastage and comfort issues.
Using data analytics, facility managers can collect and analyze large volumes of building data and turn it into actionable information to target underlying problems, and opportunities for energy savings.
This type of management can save up to 20% per year on maintenance and energy costs as big data is leveraged to refine service programs and achieve optimal building performance and cost-effectiveness and can save up to a 35% - 45% reduction in downtime increasing building efficiency and productivity.
By utilizing this new technology, facility managers can proactively recognize operational problems such as equipment that needs to be repaired or replaced before critical failure.
Barriers to implementation are marginal, and outweigh the benefits. It is also highly scalable, when using cloud-based solutions for implementation. As a result, demand for big data will only increase with many more building operations actively preparing for adoption in the next couple of years
The most important step when deciding to deploy a data analytics solution is to identify the process that will be used to act on the information. No matter how good the analytics solution, executing the findings is what drives results.
Building insights from data
For several years now, organizations have been using dashboards to view performance and manually spot trends and patterns in data. While dashboards can be quite helpful in understanding building behavior, the data being returned from dashboards is often complex, especially for building managers with multiple responsibilities.
These challenges are amplified when you consider that many building managers are also trying to keep pace with increasingly complex building technologies. While most dashboards collect data and provide tools to analyze it, they rarely provide insights without the help of experts.
The ability to automatically identify problems and provide recommendations is referred to as automated fault detection and diagnostics (aFDD). The most advanced aFDD platforms can identify faults, conduct diagnostics on plant and determine the cost or savings of identified initiatives. Alarm systems only highlight when a condition exceeds a threshold. An aFDD on the other hand can identify latent faults and trends.
For time constrained building owners, a data analytics system must be straightforward, intuitive and provide intelligent, actionable information. Dashboards that simply spit out data often offer limited value. Key actions and ROIs on all initiatives are essential.
Smart data systems
The latest analytics technologies are often based on managed software as a service (MSaaS) solutions.
With managed services, external, third-party engineering analysts help aggregate and analyze diagnostic results, track progress, and consult with building stakeholders on more complex or challenging issues. This can provide a solution to limited staff resourcing.
The managed services aspect of data analytics technology ensures that data is used to keep buildings operating at peak performance for optimal return on investment. For example, a member of the managed services team can help direct the maintenance team, helping them choose the best course of action on a daily basis to optimize building operations.
The managed services team can also provide building owners and managers with advice on how to prioritize maintenance or actions to replace a particular energy system based on which action will provide the organization with the most significant savings. This proactive approach can also help identify equipment issues before there is a system failure, avoiding costly downtime and unexpected interruptions to operations.
Leveraging data analytics for effective vendor management
Data analytics also helps buildings managers derive greater value from their work with vendors.
Consolidating and integrating data while making it accessible to vendors – such as equipment maintenance specialists – giving them granular insights into a building’s operations and a deeper understanding of where and how their work can have the highest impact.
Likewise, vendors can use building analytics data to validate and verify improvements or upgrades. Data extracted and analyzed from equipment that has been upgraded or improved can easily provide building managers a clear ROI on investments they’ve made to their systems and equipment.
“This type of management can save up to 20% a year on maintenance and energy costs as big data is leveraged to refine service programs and achieve optimal building performance and cost-effectiveness and can save up to a 35% - 45% reduction in downtime increasing building productivity and efficiency.
This data can then help support the business case for future improvements and upgrades to drive additional savings.
In addition to improving vendor performance, building analytics technology can help procurement managers and business analysts quantifiably prioritize budget allocations based on data that identifies which upgrades and repairs will result in the highest direct cost savings.
Logistics of leveraging data
In order for building managers to maximize the value of their data analytics technology, there are some considerations that they should take into account while selecting solutions.
It is important to ensure the solution includes a robust diagnostic and fault detection library already written, as obtaining these essential functionalities at a later time may result in significant additional setup costs.
Another factor building managers should consider is the degree of virtualization they are willing to deploy in their data analytics solutions.
With this said, there are three general categories of data analytics technology with different advantages, as outlined below:
1) Embedded analytics: This is the simplest system and is embedded in the hardware and software within the building management system or BMS. Providing limited capability and insight, embedded analytics works best for new construction.
2) On-premise system: This option is hardware-based and is “bolted on” to a building’s BMS system, giving building managers maximum control as they have access to nearly all of the servers and tools. Limitations include lack of remote access, increased hardware maintenance requirements and the need to regularly update software to receive the latest features and functionality.
3) Cloud-based system: This option is built using cloud based and virtual systems and is by far the most powerful solution. The data is analyzed outside of the building system in a virtual cloud environment. This option allows for greater flexibility, remote access and control, easy upgrades and no maintenance. A key consideration for this category is that most cloud-based systems ensure that software is always up to date and the facility is benefiting from the latest development in analytic technologies.
Data analytics helps building owners and managers understand not only how a building is operating, but why.
The “why” emerges through a comprehensive view including snapshots of current operations, outlines of energy trending, alerts through the application of simplistic rules or algorithms, detailed diagnostic reports, and more.
Through proactively identifying operational problems that would not otherwise be detected, data analytics helps building managers gain a deeper understanding of the “why,” which in turn leads to more permanent and effective solutions.
Written by Cara Ryan
Originally posted on fmmagazine.com.au