The application of the Building management system (BMS) is often limited to managing building operations and occupant comfort. This limits the capability of BMS for Optimizing Energy Performance in a building.
With the increasing climate responsibility and the race to Net Zero, the need for energy management has increased. To comply with government regulations and building codes, it is necessary to optimise energy performance of buildings as they consume 70% of the total electrical power being generated.
Building Management Systems control various building assets, including heating, ventilation, air conditioning (HVAC), lighting, and security systems. BMS systems gather data on asset performance and occupants' usage patterns of building components. Facility managers or building operators utilize the BMS to remotely control assets.
Presently, most buildings use BMS for enhancing occupant comfort, ensuring optimal performance of technical installations, facilitating the detection of malfunctions and equipment maintenance and centralizing the communication of useful information to operators.
However, to optimise energy efficiency of a building a deep insight into its energy consumption and wastage is very necessary. BMS has limited capabilities to offer insight into building assets. Yet its role is important and contributes to our end goal of achieving energy efficiency in buildings.
Building Management Systems (BMS) play an important role in improving building energy performance by centralizing control and monitoring of diverse building systems. Here's how BMS helps with energy management:
Even though BMS is capable of energy performance monitoring, achieving energy efficiency is still a difficult task. Facility managers or building operators must make informed decisions, allocate resources more effectively, improve operational efficiency, and optimise energy performance if they are to reach sustainability goals sooner. Let’s look at the reasons you might be falling short in using BMS for energy efficiency in buildings.
One of the key reasons why companies fail to optimize energy performance is the underutilization of BMS data. While these systems generate enormous volumes of data, interpreting it and generating insights to identify inefficiencies and opportunities for improvement is still untapped. The sheer volume of data can overwhelm users, making it challenging to extract meaningful insights without proper data management and analysis tools.
Currently, BMS data is not used to make decisions about asset allocation, repairs, retrofitting etc. Decisions that technicians make are often based on experience rather than being based on data and asset history. Many facility managers may lack the necessary skills or resources to effectively analyze BMS data and derive actionable insights.
Facility managers and building operators have to make informed decisions to improve operational efficiency and reduce energy consumption and wastage. Traditional BMS lack the advanced data analytics, crucial for deriving meaningful insights and to optimize energy performance of a building.
Many BMS employ static optimization strategies based on predetermined setpoints and schedules. While these strategies can provide energy savings under typical operating conditions, they may not adapt well to dynamic changes in building occupancy, usage patterns, or environmental conditions.
For example, In office buildings it is easier to recognize occupancy patterns as occupants come and leave the building at similar times. But in hotels, the occupancy patterns may get complicated and cannot be controlled as per the static optimization strategies. It requires advanced analytics to understand these complex occupancy patterns and quickly adapt to it.
Building Management Systems (BMS) do not have advanced predictive analytics capabilities, which are required for forecasting future energy demand and optimizing energy performance effectively. Currently, Facility Managers use real-time data and predefined setpoints in the BMS to adjust HVAC, lighting, and other building systems as needed. However, without predictive analytics, these systems struggle to predict future changes in building conditions or energy demand, resulting in inefficient responses to changing operating conditions.
Predictive analytics plays an essential role in enabling BMS to forecast energy demand based on a range of factors, including historical data, weather patterns, occupancy schedules, and other relevant factors. By predicting fluctuations in energy demand, the BMS may proactively adjust building systems to optimise energy consumption while maintaining occupant comfort. Without predictive capabilities, the BMS may miss opportunities to pre-cool or pre-heat spaces during off-peak hours, leading to increased energy consumption during peak demand periods.
Moreover, predictive analytics enables the BMS to carry out advanced energy-saving initiatives such as predictive maintenance, load forecasting, and demand response optimisation. For example, predictive maintenance uses data analysis to identify potential asset failure before they occur, reducing energy waste caused by inefficient operation or unplanned downtime. Without access to these capabilities, the BMS may resort to less sophisticated strategies, leading to poor energy performance and increased operating costs.
Predictive analytics can enable proactive energy management strategies, but implementing such capabilities may require additional resources and expertise.
Even though BMS is the tool that you need for Energy performance monitoring as well as asset management in your building, technicians and engineers fail to get most of it. Currently, most BMS are underutilized and are not being used to regulate and control energy consumption in the building.
A typical BMS architecture consists of multiple network layers, various communication protocols, numerous Programmable Logic Controllers (PLCs), and gateways. It operates through software within a network laden with codes. However, the user interfaces of these BMS devices are notably complex and challenging for technicians and engineers to comprehend. Surprisingly, these interfaces have seen little evolution despite the rapid advancements in technology. Consequently, technicians face considerable difficulty in configuring controls for their BMS and initiating energy management processes.
Effective utilisation of BMS necessitates a skilled workforce that understands the system's capabilities and can interpret the data it produces. Inadequate workforce training and expertise can undermine efforts to optimize energy efficiency and identify areas for improvement.
The complexity of the BMS interface often presents challenges for energy managers and technicians, limiting their ability to determine optimal controls and set points. Understanding of the system necessitates years of experience and familiarity, since navigating its numerous menus, settings, and data points can be overwhelming, leading to confusion and frustration among users. As a result, many workforce members may struggle to fully comprehend the functionality and capabilities of the BMS, limiting their capacity to extract valuable insights and make informed decisions based on the data available.
While BMS serves an important role in the journey of achieving building energy efficiency, they might not offer a full scale solution to it. BMS has limitations in terms of the above reasons to help you optimise energy performance. Yet BMS plays an important role as a stepping stone to improved energy performance because of the wealth of data it has.
Let’s look at how we can solve problems related to BMS performance and achieve energy efficiency in buildings.
Facility Managers or Energy managers encounter challenges in configuring optimal BMS controls to enhance the energy performance of buildings, due to the complex and hard to understand interface. Yet BMS plays an important role in managing and controlling energy consumption in the building.
BMS collects vast amounts of data from the assets. By analyzing this data on energy usage and occupancy patterns of the building, BMS can help identify areas where lighting or HVAC systems can be upgraded or adjusted to reduce energy consumption.
Integration of additional technological layers with BMS offers a solution for facility managers and building operators to optimize energy efficiency in buildings. Adoption of right set of tools such as asset performance management software not only facilitate asset maintenance but also simplify energy management tasks for your team. By leveraging asset performance management software, teams can fully harness the potential of their BMS, leading to increased energy savings and improved maintenance efficiency.
Hence, BMS alone cannot solve for energy efficiency in your building because of its complex interface. However, by integrating smart technology with your BMS and providing the right set of tools to your team, you can make their job easier and get the results you expect.