Buildings are central to modern life. Considering they account for up to 40 percent of energy use in most countries, buildings are a prime target for cutting waste, saving money and using resources more effectively, Artur Socha, products application engineer: Schneider Electric South Africa.
Data analytics – the practice of collecting and analysing data to extract insights – is one of the most effective tools that building managers can use to improve the operation of their facilities. Effective use of data analytics in buildings can reduce major equipment spend and increase energy savings by up to 30 percent.
The use of data analytics in buildings helps proactively identify and solve inefficiencies in building systems. Lighting, HVAC, security and building automation systems all generate massive volumes of data.
With the right tools, building managers can pull data from these systems and run it against algorithms to compare current operations to an optimal range for a system or piece of equipment. This process allows building managers to easily see when a system or piece of equipment deviates from optimal operating conditions.
In addition to spotting deviations, the data can also be used to proactively optimise a building’s operations from an entire plant to a single terminal unit.
Data analytics is often associated with creating operational efficiencies, but building managers may be surprised to learn it is also a powerful tool to identify waste and undetected problems.
For example, through collecting and analysing data, facility managers can uncover issues like simultaneous heating and cooling, suboptimal economiser controls, leaking valves, broken dampers, manual overrides, poor occupancy scheduling, excessive zone temperature set points, and much more. Without data analytics, these significant sources of waste would go unnoticed, weighing on the bottom line and eating up resources.
Drive action through intelligent, informed insights
In order to achieve the maximum operational efficiencies, cost savings and competitive advantage from data analytics, facility managers must first derive the most comprehensive insights from their building’s performance data. For several years now, organisations have been using data visualisation dashboards to view performance, and manually spot trends and insights.
While dashboards can be quite helpful in understanding building behaviour, the data being returned from dashboards is often complex, especially for building managers and owners, who manage a multitude of challenges in optimising their operations.
These challenges often include trying to keep pace with increasingly complex building operation technologies, especially when some building managers may not fully understand the newer, IP-enabled systems with complex, IT-reliant interfaces. To make matters worse, these IP-enabled systems are often left unsupported by the facility’s IT staff since they are specialised facilities technologies.
Additionally, many facility managers are working to keep their building running smoothly while balancing budgetary pressures to reduce costs and meet corporate social responsibility goals with fewer resources. All of these competing challenges leave little time for them to sit and analyse large amounts of data in order to identify otherwise hidden problems.
While most dashboards excel at aggregating data and providing tools to visually analyse the data, they usually lack the ability to provide insights without the help of experienced building engineers. The capacity to automatically identify problems and provide recommendations for savings opportunities is a process referred to as automated fault detection and diagnostics (aFDD).
The most advanced aFDD platforms can identify faults, conduct diagnostics on mechanical systems and determine the cost or savings incurred through making repairs, improvements or upgrades to a building’s systems or operations. Unlike alarms, which highlight when conditions exceed a threshold, aFDD can identify when conditions may be trending toward a future problem prior to issues occurring.
aFDD can also identify issues like simultaneous heating and cooling, which may not lead to an alarm because space conditions are always within tolerances, helping spot waste and savings opportunities that would otherwise go unnoticed.
For building owners and managers under high pressure and short on time, a data analytics system must be straightforward, intuitive and provide intelligent, actionable information. Dashboards that simply spit out data often offer limited value if building managers cannot leverage the information because they lack the time or the technical background to translate it into specific actions that will result in highest efficiency and return on investment (ROI).
To solve this problem and help building managers effectively implement insights from their systems’ data, some analytics technologies also include managed software as a service (MSaaS) solutions (sometimes also referred to as managed services), which can help optimise a facility’s operations.
With managed services, external, third-party engineering analysts help aggregate and analyse diagnostic results, track progress, and consult with building stakeholders on more complex or challenging issues.
Managed services can help reduce or eliminate the need for businesses to bring on additional full-time resources, allowing existing internal teams to continue to focus on their core work while benefitting from the expertise of building optimisation expert business partners.
For example, according to Navigant Research, only 20 percent of personnel currently using a building energy management system use up to 80 percent of its functionality, while the other 80 percent use a limited amount of the functionality, or they use it in way that was not intended.
Managed services teams, which are made up of experienced energy experts, can help building owners and managers use their data analytics and building management systems (BMS) more effectively.
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 optimise building operations.
The managed services team can also provide building owners and managers with advice on how to prioritise maintenance or actions to replace a particular energy system based on which action will provide the organisation 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.
Managed service partners can also validate corrective actions and can often remotely resolve issues.
Leveraging data analytics for effective vendor management
Data analytics 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.
By leveraging data analytics, a vendor can extract insights from day to day operations and easily identify a repair or tweak that would drive the greatest value based on the priority of the building manager. Vendors can initiate their assessments remotely or from mobile devices and focus their efforts on a specific task or piece of equipment, allowing them maximise proactive maintenance and more easily assess how a particular piece of equipment is performing based on the building manager’s priorities.
Additionally, vendors can use building analytics data to validate and verify improvements or upgrades. Data pulled and analysed 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 data can 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 prioritise budget allocations based on data that identifies which upgrades and repairs will result in the highest direct cost savings. For example, Schneider Electric recently worked with the City of Henderson, Nevada, to implement a building analytics and proactive maintenance solution across its 13 municipal buildings to identify, prioritise and execute repairs based on cost, comfort, energy and maintenance needs. By leveraging building analytics technology, the City will be able to perform targeted maintenance for all of its HVAC equipment. This proactive maintenance is expected to lower annual operating costs and generate an anticipated positive return expected to exceed US $364,000 over 10 years.
Logistics of leveraging data
In order for building managers to maximise 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 virtualisation they are willing to deploy in their data analytics solutions. There are three general categories of data analytics technology with different advantages, as outlined below:
* On-premise system: This option is hardware-based and is “bolted on” to a building’s systems. This gives building managers maximum control as they have access to nearly all of the servers and tools. The limitations of this system include lack of remote access, increased hardware maintenance needs and the need to regularly update software to receive the latest features and functionality.
* Cloud-based system: This option is built using mostly cloud based and virtual systems, where data is pulled from building systems and analysed in a virtual cloud environment with limited on-premise systems. This option allows for greater flexibility, remote access and control, easy upgrades and less 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 most current set of analytic diagnostics.
* Embedded analytics: This system is fully embedded into hardware and software with deep integration. Embedded analytics works best for new construction and is more challenging to accomplish with retrofits and upgrades. The available embedded analytics today are still in their infancy and thus are limited in functionality and availability.
Driving value
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.