Smart technology incorporated into buildings has become a hot topic of heated discussion. In the past decade, various IoT devices have rapidly emerged to collect data. These devices vary from air-conditioning systems, power distribution systems, rooftop solar systems, to security surveillance systems, covering every nook and cranny of the building. It would not be an exaggeration to say that buildings are where IoT devices are most heavily used. Across the United States, for example, 2.2 billion IoT devices have been installed in about six million commercial buildings. These IoT devices in mind-boggling numbers are continuously sensing temperature, lighting, air quality, noise, personnel density, energy consumption, and other parameters 24 hours a day; the endless stream of massive data forms the digital twin of the building, allowing building management to see an overall picture to make more informed decisions.
As IoT Devices in Buildings Become Common, an Enormous Amount of Data Analytics Must Leverage AI
However, a challenge that comes with massive data is that the huge amount of data can no longer be processed manually; it requires AI to perform simulation, analysis, and make accurate predictions. Without human intervention, the system can deliver automatic decision-making, while also continuously learning and making iterative improvements as the amount of data increases.
In fact, advanced building AI solutions are already available on the market, which can continuously learn and optimize. For example, integrated with external information such as sunlight and weather forecasts, some solutions have been able to predict the temperature of an air-conditioning covered zone in two hours with up to 98% accuracy. BrainBox AI, a start-up company specializing in architectural AI technology, has deployed its own solutions to more than 100 million square feet of building space in 18 countries. Based on its estimate, buildings equipped with AI-powered technology can reduce energy expenditure by 25%, reduce carbon footprints by 40%, and increase asset life by 50%.
Obstacles Lie Ahead for AI-powered Smart Technology Used in Buildings
Building users and managers also profoundly realize that it is not easy to employ various smart technologies such as AI. Only a very small number of exemplary top-level buildings are eligible, and the practice has not yet become mainstream. Reasons for this include: 1) difficulties in integrating old and new systems such as existing LED lights and smart light sources that can be controlled individually; 2) failure to plan holistically from the early stage of architectural design—smart system integrators are often consulted at a very late stage; 3) lack of interoperability among the solutions available on the market, such as an air-conditioning system and rooftop solar system operating separately from each other; and 4) building managers that are unaware of the benefits of leveraging smart technology, and tend to purchase cost-effective devices such as water chiller units to save energy, rather than spend on comprehensive smart technology integration.
To overcome the above-mentioned barriers to the owner from adopting a smart building approach, one must first fully understand that the development of smart buildings is a continuous journey that cannot be achieved in one step, or target a specific goal. It will have different milestones as technology evolves, user needs change, and regulations are adjusted. The best way is to leave one's options open at the beginning of architectural planning and communicate with the integration service provider of smart solutions. If a special taskforce can be designated to address smart technology and a budget can be allocated exclusively for the task, rather than a one-time expenditure or diverted from daily operating funds, it will facilitate the implementation of the planning.
Mature Technology, Common Standards, and Regulatory Requirements Drive the Popularization of Smart Buildings
The good news is that the cost of smart building solutions on the market, whether it is AI models, cloud platforms, edge control, or IoT sensor networks, will become more and more competitive as technology development matures over time. Meanwhile, the industry is actively developing common open standards that work the same as standardized communications protocols used to transmit data for smart meters, lighting, and air conditioning, which will lead to a greater degree of integration and synergy. As for external factors that will accelerate building AI technology into smart buildings, green regulations, ESG investment momentum, and rising electricity prices are often cited among others. In addition, regulations on automation and smart norms derived from building carbon reduction in various countries are becoming more stringent. The European Union, for example, requires buildings to be equipped with Smart Ready Technologies (SRT), while Singapore has launched its green building label certification and Australia also has put the NABERS energy consumption rating system in place.
India is regarded as one of the fastest-growing smart building markets in the world. According to an Indian industrial technology website TimesTech, there will be several key trends in smart building technology in 2023, including IoT integration, BMS (building management system) decision-making analysis, and control of the workplace conditions (such as lighting and air conditioning to improve health and productivity), digital twins into BMS, and remote integrated management platforms. What connects these trends is AI/Machine Learning analysis tools, which empower building managers to obtain sufficient information and insights for predictive management. An example of how applying AI models and Machine Learning to this data leads to predictive maintenance resulting in less downtime, energy savings, and fewer disruptions in the building is that managers are able to know in advance the different wear-and-tear conditions of the elevator components. This allows them to initiate more targeted and refined maintenance accordingly, instead of taking the current indiscriminate one-and-done approach of replacing all parts when the maintenance time is up regardless of their conditions.
Back to the basics, technical and structural barriers are not the biggest obstacles to the implementation and optimization of smart building solutions; rather, the top critical success factors are the correct understanding of smart technology by building users and managers, their determination to improve energy efficiency and experience, and their firm commitment to net-zero carbon reduction. Although AI brings infinite possibilities to smart buildings, the final decision still lies with "people."