Traditional building management systems
The backbone of facility control for decades, traditional building management systems rely on pre-set rules and schedules to regulate heating, ventilation, air conditioning (HVAC), lighting, and other building services. Maintenance programmes are calendar-based and reactive. Traditional building management systems also rely heavily on human intervention, with operators manually adjusting settings. Decisions about energy sources tend to be made well in advance of energy delivery.
While effective to some extent, these traditional BMS are limited in their ability to adapt to the dynamic conditions that affect buildings’ energy needs. Buildings are used differently depending on the day, time, and occupancy level, while energy providers’ sources and prices are in constant flux. A traditional BMS’ failure to respond to these changing conditions can mean that building owners pay higher energy costs and, potentially, generate a higher carbon footprint.
AI-powered building management systems
In contrast, AI-powered building management systems are ‘self-steering’. They leverage the capabilities of machine learning algorithms and data analytics to optimize building operations, with minimum human intervention required. These systems continuously learn, adapt, and improve efficiency over time, providing a more dynamic and responsive approach to building management. Importantly, they can also respond to changes to energy prices and sources to ensure your building is powered by the most cost effective and sustainable energy providers available.
Introducing Priva ecoBuilding: The smart energy solution
Priva ecoBuilding is a self-learning software solution that automatically optimizes climate conditions and energy usage in your building, based on historical and real-time data. It operates 24 hours a day, seven days a week - without any human intervention. Here are some of the key ways that Priva ecoBuilding boosts your building performance, while creating at least a 20% reduction on your building’s energy expenditure (as well as saving you valuable labor time):
- Energy efficiency
As an AI-powered BMS, Priva ecoBuilding analyses data from various sensors, weather forecasts, and occupancy patterns to optimize HVAC systems. This results in significant energy savings by ensuring that resources are used only when and where needed. - Sustainability
By optimizing a building’s energy use, Priva ecoBuilding helps to reduce energy demand and therefore CO2 emissions. This is critical given the growing regulatory, financial, and social pressure on commercial real estate firms and building owners to decarbonize their building operations. - Predictive maintenance
Traditional BMS often rely on scheduled maintenance, leading to unnecessary downtime and costs. In contrast, Priva ecoBuilding uses predictive analytics to anticipate equipment failures, enabling proactive maintenance. This not only reduces maintenance costs but also extends the lifespan of building systems. - Adaptability
Priva ecoBuilding excels in adapting to dynamic conditions. Whether it is responding to unexpected weather changes, fluctuations in occupancy, or changes in building usage or energy prices, Priva ecoBuilding swiftly adjusts settings to maintain optimal conditions, ensuring occupant comfort and energy efficiency.
The integration of AI into building management systems represents a paradigm shift in how we approach the efficiency and sustainability of our built environments. AI-powered BMS like Priva ecoBuilding are an indispensable tool for cost-effective, efficient, and carbon conscious energy management that also improves occupant comfort. The result is a better, more sustainable building.
Interested in more efficient and sustainable energy management to save time and money? Contact us today for a free Priva ecoBuilding consultation.
