Choosing the right approach to leverage big data for energy savings

Energy efficiency has been in the spotlight for decades, particularly in regard to buildings, which account for 20% of Australia’s energy use. Facility managers rely on building management systems (BMS) to gather data about building performance and energy usage to reduce operating and maintenance costs, improve building comfort and save energy.

The volume of building data available has risen rapidly in the last decade as facility monitoring systems become more complex and thorough. But harnessing this ‘big data’ to leverage BMS potential requires significant training and in-depth knowledge of a facility and its history, along with an investment in IT and dashboards or automated analytics. Ageing infrastructure, reduced budgets, sustainability demands and lost expertise through personnel turnover are just some of the factors also contributing to this huge challenge for facility managers.

To help take advantage of big data, facility managers in Australia are considering the pros and cons of their approaches to BMS and what methods are the most compatible. Best-in-class software automatically trends energy and equipment use, identifies faults, provides root-cause analysis and prioritises opportunities for improvement based on cost, comfort and maintenance impact.

Custom-built systems, software-as-a-service (SaaS) and managed software-as-a-service (MSaaS) are some of the most popular analytics approaches when dealing with big data in buildings.

Some facility managers choose the custom-built approach and create their own on-site building data analytics system designed specifically for, and integrated into, their building’s systems. This gives building managers the greatest flexibility with the system as they have exclusive access to all the servers, software and tools. To utilise big data, however, storage capacity and processing power can require significant, and therefore costly, IT infrastructure to provide the level of data confidence required.

Not only does a custom-built solution require a substantial investment in the IT infrastructure, it also requires highly skilled staff or vendors to build the diagnostics and maintain the systems to manage this big data. As well as this, customised systems rarely allow remote access or utilise web browser interfaces because of the high-cost browser updates and software to combat security threats.

An SaaS data analytics solution is cloud based and is a more cost-effective and efficient option than custom-built systems. Big data is automatically pulled from the BMS and analysed in a virtual cloud environment. This gives building managers both the powerful insights of data analytics and the flexibility of remote access and control.

Leveraging a ‘mass customisation’ approach, these subscription-based solutions cost less to deploy because an existing, fully built library of complex diagnostics can be customised to individual buildings very quickly. Additionally, the pace of technology change is so rapid today that on-site solutions may become antiquated very fast. Cloud-based SaaS solutions can react to customer feedback and constantly deploy new versions with added features and functionality continuously at no additional cost to the user. Software upgrades and diagnostic improvements are also cost-effective and predictable, budgetable expenses because they are included in the subscription.

One issue with SaaS systems is that they require staff to manage the software, interpret the big data and act on the opportunities identified. Considering the scope and complexity of the information being collected in its raw form, a high level of expertise and in-depth knowledge of big data is necessary to take full advantage of this deluge by understanding and applying feedback effectively.

Facility managers can circumvent this necessity by choosing an MSaaS as an analytics solution. MSaaS combines the SaaS analytics solution with the oversight of remote engineering experts who can specialise in big data. Remote engineering analysts use insights from the information to monitor, detect, diagnose and identify energy savings opportunities. They understand complex data and its relation to building issues so can deliver high-level recommendations for upgrades, repairs or maintenance based on business priorities.

Additionally, an MSaaS analytics solution can increase the efficiency of vendors and partners by consolidating and integrating data from various building systems. This data can then be made accessible to all vendors, saving them time and making building services more effective. The data can be leveraged to improve vendor management by ensuring issues are fully resolved by utilising analytic findings and monitoring capabilities. This ensures issues do not reappear.

Facilities owners have made significant investments in sophisticated BMS systems that generate a wealth of data about a building’s performance. Data dashboards help facility staff visualise all this data, but dashboards tell only where inefficiencies exist - not why. Comprehensive data analytics software can interpret this big data and convert it into actionable information so facility managers can prioritise and proactively address issues for long-term solutions.

This can have a real impact on energy consumption, operational efficiency, occupant comfort and the financial wellbeing of buildings. The right analytics approach to big data management will proactively help facility managers achieve performance goals and contribute to a lower carbon footprint - all while driving a positive ROI, increasing portfolio value and maximising investments.