The world around us is becoming increasingly intelligent. We see self-driving cars, drones that grow apple trees, body sensors that monitor our health, and a plethora of other ingenious IoT applications. However, one of the industry segments that has been slow to adopt smart solutions is the building sector, despite the fact that energy efficiency in buildings is a hot topic - for all the right reasons:
• Collectively, buildings in the EU are responsible for 40% of our energy consumption and 36% of greenhouse gas emissions, which is not sustainable at all. • 75% of Europe's buildings are not energy efficient today, and it is predicted that 95% of those buildings will still be in use 2050, which means property owners are leaving a lot of money on the table, especially during energy crises.
Europe is experiencing an energy crisis, and many utility companies are resorting to fossil-based energy production to meet demand. The call to action could not be stronger, but capital, time, and resources are scarce. The million-dollar question is where to put your money.
Investing in IoT and AI
The obvious solution to the aforementioned problems is to improve building insulation, replace blowing windows, and replace fossil-fueled heating systems with heat pumps and solar panels. Clearly, this is something that must be done, but let's be honest: it doesn't happen overnight. The current energy renovation rate of building stocks in Europe is 1%, and the vast majority of the global building stock will still exist in 2050. If the building sector is to reduce carbon emissions by 60% by 2030, the energy renovation rate of Europe's building stock must double - a difficult task given rising building material costs and a scarcity of resources in this industry.
In many industries, digitization provides another way to address challenges. Collecting all types of data related to a building's indoor climate, weather conditions outside the building, energy consumption, and heating system performance, and analyzing and visualizing this data, allows for valuable insights into a building's energy performance, as well as steering heating and cooling systems to optimise energy consumption and minimise carbon emissions.
In this approach, value is created not through building insulation and window replacement, but through the use of IoT and AI-engines to collect and process building data. The technology is available, the investments required are much lower when compared to mechanical construction, and results can be obtained much more quickly. But let's be clear: smart building solutions supplement, not replace, energy-efficient building renovation.
However, the construction industry is a bit of a digital laggard. Data collection and visualisation would be used not only to reduce energy consumption, but also to help decision makers decide where and why to invest. Unstructured data accounts for 80-90% of an organization's data today. The data points required to optimize energy efficiency (such as humidity, temperature, electricity consumption, building infrastructure, and so on) that could be available and structured are frequently unavailable.
At the same time, few people realise or recall that pioneering work for smart buildings dates back to 1977, when James Southerland, a young engineer at Westinghouse Electric, built his ECHO IV (Electronic Computing Home Operator) computer. Aside from providing control panels to steer alarm clocks and televisions, it also enabled remote control of a thermostat - quite forward-thinking at the time!
Returning to today, consider the business value of building energy management.
Energy management
Data-driven energy consumption reduction will undoubtedly lower energy costs. Annual energy cost reductions of 10%-15% are well achievable in larger, multi-tenant buildings, depending on the specific case, particularly by reducing and more dynamically adapting the supply temperature of the heating system. This is a prime example of low-hanging fruit!
Another important aspect to monitor, and perhaps even more valuable, is the early detection of technical faults in heating systems. Typical issues include faulty temperature sensors or valve servo motors. Aside from avoiding unnecessary energy costs, heating system repair or replacement costs can also be avoided. Remote detection and analysis reduces the number of onsite visits by service personnel and allows them to solve problems on the first visit.
Property value is obviously critical from the standpoint of the owner. Investment funds are optimising their property asset portfolios in terms of energy efficiency. While highly inefficient buildings are at risk of becoming stranded assets, investments in energy management and optimization can significantly increase property value.
A more detailed analysis of the business benefits can be found here, as well as a related business value calculator.
As we can see, investments in data-driven energy management of buildings can generate significant business value in a short period of time and with minimal capital. This does not eliminate the need for building energy renovations, but it does allow for rapid improvements in energy efficiency. The data gathered will also reveal where to invest in renovation to maximize the return on investment.
Conclusions
Since James Southerland built his ECHO IV home computer in 1977, smart building energy management has come a long way, and the building segment has the clear potential to evolve from a digitization laggard to an innovation hotspot.
The concept of virtual sensors enables the collection and combination of data from various sources without the need to install a physical sensor at each location.
It is becoming increasingly feasible to create comprehensive digital twins of buildings, fed by an accelerating stream of data from physical and virtual sensors. Building digital twins that represent not only the physical structures, but also all active operating technology and building usage. Energy efficiency is only one aspect of building management.
Machine learning and artificial intelligence (AI) are the first steps toward data-driven, intelligent buildings that can control themselves and constantly learn new patterns to self-optimize their operations.
Building automation success relies on a variety of enablers, including AI-systems, sensors and actuators, and, not least, IoT connectivity. The key to success in this industry segment is the creation of co-value in ecosystems to make smart buildings a reality.
• Collectively, buildings in the EU are responsible for 40% of our energy consumption and 36% of greenhouse gas emissions, which is not sustainable at all. • 75% of Europe's buildings are not energy efficient today, and it is predicted that 95% of those buildings will still be in use 2050, which means property owners are leaving a lot of money on the table, especially during energy crises.
Europe is experiencing an energy crisis, and many utility companies are resorting to fossil-based energy production to meet demand. The call to action could not be stronger, but capital, time, and resources are scarce. The million-dollar question is where to put your money.
Investing in IoT and AI
The obvious solution to the aforementioned problems is to improve building insulation, replace blowing windows, and replace fossil-fueled heating systems with heat pumps and solar panels. Clearly, this is something that must be done, but let's be honest: it doesn't happen overnight. The current energy renovation rate of building stocks in Europe is 1%, and the vast majority of the global building stock will still exist in 2050. If the building sector is to reduce carbon emissions by 60% by 2030, the energy renovation rate of Europe's building stock must double - a difficult task given rising building material costs and a scarcity of resources in this industry.
In many industries, digitization provides another way to address challenges. Collecting all types of data related to a building's indoor climate, weather conditions outside the building, energy consumption, and heating system performance, and analyzing and visualizing this data, allows for valuable insights into a building's energy performance, as well as steering heating and cooling systems to optimise energy consumption and minimise carbon emissions.
In this approach, value is created not through building insulation and window replacement, but through the use of IoT and AI-engines to collect and process building data. The technology is available, the investments required are much lower when compared to mechanical construction, and results can be obtained much more quickly. But let's be clear: smart building solutions supplement, not replace, energy-efficient building renovation.
However, the construction industry is a bit of a digital laggard. Data collection and visualisation would be used not only to reduce energy consumption, but also to help decision makers decide where and why to invest. Unstructured data accounts for 80-90% of an organization's data today. The data points required to optimize energy efficiency (such as humidity, temperature, electricity consumption, building infrastructure, and so on) that could be available and structured are frequently unavailable.
At the same time, few people realise or recall that pioneering work for smart buildings dates back to 1977, when James Southerland, a young engineer at Westinghouse Electric, built his ECHO IV (Electronic Computing Home Operator) computer. Aside from providing control panels to steer alarm clocks and televisions, it also enabled remote control of a thermostat - quite forward-thinking at the time!
Returning to today, consider the business value of building energy management.
Energy management
Data-driven energy consumption reduction will undoubtedly lower energy costs. Annual energy cost reductions of 10%-15% are well achievable in larger, multi-tenant buildings, depending on the specific case, particularly by reducing and more dynamically adapting the supply temperature of the heating system. This is a prime example of low-hanging fruit!
Another important aspect to monitor, and perhaps even more valuable, is the early detection of technical faults in heating systems. Typical issues include faulty temperature sensors or valve servo motors. Aside from avoiding unnecessary energy costs, heating system repair or replacement costs can also be avoided. Remote detection and analysis reduces the number of onsite visits by service personnel and allows them to solve problems on the first visit.
Property value is obviously critical from the standpoint of the owner. Investment funds are optimising their property asset portfolios in terms of energy efficiency. While highly inefficient buildings are at risk of becoming stranded assets, investments in energy management and optimization can significantly increase property value.
A more detailed analysis of the business benefits can be found here, as well as a related business value calculator.
As we can see, investments in data-driven energy management of buildings can generate significant business value in a short period of time and with minimal capital. This does not eliminate the need for building energy renovations, but it does allow for rapid improvements in energy efficiency. The data gathered will also reveal where to invest in renovation to maximize the return on investment.
Conclusions
Since James Southerland built his ECHO IV home computer in 1977, smart building energy management has come a long way, and the building segment has the clear potential to evolve from a digitization laggard to an innovation hotspot.
The concept of virtual sensors enables the collection and combination of data from various sources without the need to install a physical sensor at each location.
It is becoming increasingly feasible to create comprehensive digital twins of buildings, fed by an accelerating stream of data from physical and virtual sensors. Building digital twins that represent not only the physical structures, but also all active operating technology and building usage. Energy efficiency is only one aspect of building management.
Machine learning and artificial intelligence (AI) are the first steps toward data-driven, intelligent buildings that can control themselves and constantly learn new patterns to self-optimize their operations.
Building automation success relies on a variety of enablers, including AI-systems, sensors and actuators, and, not least, IoT connectivity. The key to success in this industry segment is the creation of co-value in ecosystems to make smart buildings a reality.