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Artificial Intelligence for Built Environment (AIfBE)

Artificial intelligence has tremendous potential to reduce energy, operational costs and carbon emissions, while ensuring occupant comfort, and resilience of buildings and communities. However, there is currently much speculation and lack of understanding about what AI can actually achieve. The result is a certain mistrust and perceived risk in implementation.

This wiki/paper aims to demystify AI and provide ‘best practice’ examples of real-world applications, so that these may be better understood, and applied with confidence by municipalities, developers, and building owners.

Our environment is increasingly being driven by our use of technology. This explains the interest in smart cities and smart buildings. While smart buildings are focused on meeting occupants’ needs for wellness, comfort, and productivity, the aim of smart cities is to link people and businesses to effective and efficient infrastructure such as secure and resilient power and water, multi-modal transportation, land use and waste management.

Cities and buildings generate a vast amount of data, which in-and-of-itself is of little use unless it can provide useful insights. This is where AI can offer a useful tool to achieve a better integration of smart buildings, smart city infrastructure, connected businesses, and people.

Figure 1. Interfaces within the city fabric

Artificial Intelligence (AI) has several subfields such as Machine Learning (ML) , which can make predictions and decisions, and execute operations without being explicitly programmed to do so; and Natural Language processing, which allows machines to read and understand human language and enable user interface.

These tools can help municipal planners, building owners and occupants to optimize the performance of buildings and interface with city infrastructure. The following table summarizes how different stakeholders can benefit from AI capabilities.

Municipal (policy making at county/city level)	Municipal (operations & purchasing at county/city level)	Large owners and Managers (portfolios)	(Institutional) Asset managers(pension funds/ insurance)	Medium to small building owners (single buildings)	People/ Community
BUILDINGS
	Optimizing Performance of Smart Buildings	Optimizing Performance of Smart Buildings		Optimizing Performance of Smart Buildings
Data Analysis and Predictions			Data Analysis and Predictions
URBAN INFRASTRUCTURE AND UTILITIES
Data Analysis and Predictions			Data Analysis and Predictions
Smart Grid, Transactive Energy and Renewables	Smart Grid, Transactive Energy and Renewables
TRANSPORTATION
Data Analysis and Predictions	Shared Mobility				Shared Mobility
	Autonomous Vehicles/Drones	Autonomous Vehicles/Drones
RESILIENCY
Data Analysis and Predictions	Operational Awareness				Notification/Awareness
BEHAVIOUR CHANGE AND INDIVIDUAL ACTION
					Behaviour Change and Individual Action

Optimizing Performance of Smart Buildings

Raiffeisen Tower

Raiffeisen Tower’s energy concept is compelling: Energy is provided by a photovoltaic system and a combined heat, cooling and power plant. Even the waste heat from the data center is re-used, with cooling partly coming from the Danube canal. The decisive factors in reducing energy consumption is the radically increased efficiency of the facade, building component connections, and the mechanical systems in combination with optimized shading equipment.

Buildings can improve energy performance and decrease operation cost and their carbon footprint through energy-efficient design and smart control systems capable of monitoring, analytics, and diagnostic of the building’s operations.

Many examples, such as the EDGE in Amsterdam, 22 Bishopsgate in London, and Raiffeisen Tower in Vienna, demonstrate that new high-rise buildings can achieve as much as 80% of energy savings compared to conventional high-rise buildings through the heating and cooling demand reduction.

However, the bulk of the building stock is existing. Retrofitting them with energy conservation measures such as autonomous optimization of existing Heating, Ventilation, and Air Conditioning (HVAC) control systems for maximum impact on energy consumption could reduce emissions by about 30–60% . The greatest challenge is not the technology but how to persuade the owners to install the smart technology and retrofit the buildings. Can AI help to reduce emissions of those buildings?

Sidewalk Quayside

Quaysideis a Toronto project that proposed to cut GHG emissions by 89% through energy and mobility initiatives, starting with energy-efficient building design and digital management tools. Sidewalk believed in pushing the smart building systems management beyond simple analytics and diagnostics to a self-managed system, based on the ‘DeepMind’ AI technology used to reduce Google’s Data Centre cooling bill by 40%.

Smart buildings use analytic and diagnostic systems that send data from the Building Automation System (BAS) to an analytical engine in the cloud, which process the data according to certain set rules, displays performance data, and even provides instructions and work-orders to building operators to help them optimize the building performance. Such systems are typically capable of reducing carbon emissions by 16-25%.

A significant reduction of carbon emissions can be achieved by regulating the ventilation air and temperature based on the occupancy. For example, there is no point heating and cooling the building where the is no one inside. The occupant density monitoring is being highlighted in the time of COVID, when it is important to know that a “social distancing” can be maintained. The AI programs can monitor the building occupancy density and inform the BAS which spaces are being occupied, at what density so that the appropriate amount of air, heat, and cooling can be delivered to that space. This increases efficiency, flexibility, sustainability, and resilience, optimize services and enhance the human experience.

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