white paper
Artificial Intelligence in the field of Building Automation
Professor Michael Krödel,CEO, Institute of Building Technology, Ottobrunn, Germanyand Professor for Building Automation and Technology, University of Applied Sciences at Rosenheim
andGraham Martin
Chairman & CEO, EnOcean Alliance

The term „AI – Artificial Intelligence“ is increasingly associated with buildings and building
automation. The question is: what is it, where do its tangible benefits lie in this field, and how does
the building infrastructure need to be adapted to realise those benefits?
Today’s building automation systems in the main operate ‘statically’ in response to fixed time
programs or simple control parameters. Room temperature control is based on a preset temperaturethat is the same throughout the day. Lighting is operated manually, with switches, or on the basis ofsimple presence switches. None of this is truly ‘intelligent.’ The new dimension that AI can add into
the building automation environment is to use autonomous analysis of the data as a basis for
optimised operation. Thus the heating and cooling dynamic of rooms, weather forecasts, predicted
room occupancy during the course of the day can all be factored into the operation of the heating.
Similarly, cleaning schedules can be based not only on the current actual values in terms of theintensity of use of kitchens, canteens and toilets and other areas, but can be based on predictions
drawn from an analysis of usage patterns in the previous days and weeks. This kind of forward
looking building management can be applied in almost every area of building services, leading toincreased energy efficiency, reduced operating costs, improved space utilisation and otheradvantages.
All this - and much more - is possible when data on building system status and conditions isintelligently evaluated. This requires intensive processing of large amounts of data, with manyvariables to be considered. Artificial Intelligence (AI) offers many new, tailor-made solutions which
are eminently suited to efficient building management.

“Building Automation”, “Smart Building” and “Cognitive Building”
Initially, „Building Automation“ was comparatively „un-intelligent“. Systems were programmed tofollow a set of simple rules, allowing for quick system start-up and subsequent ease of maintenance.
The „Smart Building“ typically builds on this classic building automation with flexible IT-based
management systems . These offer unrestricted programming using modern IT languages and tools,
easy integration with other IT systems such as workspace/room reservation systems or data banks,and data visualisation for facility managers and for „ordinary“ users.
The growing assimilation of sensor-generated data into the IT-based management level opens theway for more advanced data processing solutions to come into play - such as AI tools. This is the pre-white paper
condition for the implementation of any prognosis-based form of building management. The
sophisticated processing of sensor-generated data makes the Smart Building into a „CognitiveBuilding“.

Illustration 1: Building Automation - Smart Building - Cognitive Building (source: IBM)

AI-learning process
The first step in any Artificial Intelligence process is system learning. This can take three forms.


Unsupervised Learning
Supervised Learning
Reinforcement Learning„Unsupervised Learning“ is used when large quantities of data must be processed and categorised.
This grouping enables the recognition of deviations from norms and interdependencies. For example,sensor data from identical circulation pumps can be grouped. If data from one pump or group ofpumps deviates from the norm, there may be a defect and a human engineer can be sent toinvestigate.
„Supervised Learning“ often makes use of neural networks. They consist of entry and exit nodes aswell as further nodes in the intermediate layers. Mathematically weighted relationships exist
between the diverse nodes (neurons). In order to optimise these relationships, the neural network is
subjected to a training phase with known input and output patterns. In the field of building
automation, for example, a neural network can ‚learn‘ the current consumption profiles of different
appliances and which appliances are active when. This information can be used to avoid ‚spikes‘ inbuilding energy consumption, by shutting down some appliances and extending the operation timeof others.
Another form of Artificial Intelligence is represented by processes that autonomously determine
which actions are appropriate in a given situation. They emulate human behaviour whereby different
solutions are tried in order to determine the best way forwards in a hitherto unknown situation, andconclusions drawn retrospectively. The learning task becomes more challenging when feedback isgiven much later and hinges upon events in the relatively distant past. This is true in a humancontext, and equally true in computer environments. The best-known example in this category is„Reinforcement Learning“. Consider the issue of determining the optimal start and stop times ofwhite paper
heating to achieve a comfortable temperature when the building opens. At the simplest level, thelearning algorithm receives the value from the room temperature sensor and can act on the actuatoron the radiator. By a process of trial and error, the algorithm can determine the necessary lead time.
However, this simple example ignores the fact that, for instance, the speed of heating also depends
on the outside temperature, so the reading from an exterior temperature sensor needs to beconsidered. Instead of providing a pre-set target temperature, the algorithm may be be given
evaluations (good / OK / cold) during the day and must learn in response to this feedback. Inaddition, the algorithm can be provided with an addditional rating every month based on the overall
energy cost: encouraging efficient behaviour and discouraging inefficient responses. A ‚stable‘
response that balances comfort and efficiency can be established, but exploration should continue toaccomodate changes in behaviour and the environment.
It can be seen that these three approaches are complementary. The learning method should bechosen depending on the task in hand - each has its merits.

Concrete applications
Many diverse AI-based applications are available in the field of building automation. They can bebroadly categorised as follows:




Optimised facility management: needs-based control of heating plants, circulating pumps,lighting etc. (as opposed to control on the basis of simple parameters or by timer).
Optimised utilisation of spaces and infrastructure: capacity analysis and forecasting, e.g. formeeting rooms, canteens, pantries, transit areas, toilets and parking spaces as well as theprovision of information in the short term (for building occupants) and in the long term (forfacility managers, e.g. in form of advice on building restructuring).
Load management: forward-looking operation of electrical systems in order to avoid (costly)
peak loads.
Precautionary maintenance and optimised servicing: analysis of failure probability, timely
maintenance and consequential avoidance of technical failures.
Employee-oriented value added services: mobile devices can - for instance - be used togenerate space utilisation forecasts, view canteen usage intensity, request parking space
availability and preferred workspace location or select individual meals.
Compensation of skilled-staff shortages: making effective use of facility maintenance staff inmanaging the building’s technical systems.
Focus on meaningful sensor data: generate as much data as possible from as few sensors as
possible - reducing redundancy, cutting investment and operating costs.

Demands upon system architecture
An AI platform is indispensable for the introduction of intelligent learning processes such as thosedescribed above. This can be either cloud-based or server-based. Cloud-based server farms offer
more processing power, and cloud-based AI frameworks offer a broader range of features, so thiscurrently represents common practice.
The AI platform is built on a Smart Building infrastructure, and all technical systems should ideally beconnected to a BMS (Building Management System). The BMS must be able to govern the building
facility and room automation systems.

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Cloud-based
AI platform

Internet
Managementlevel

BMS (BuildingManagement System)LAN

Automation
level

Controller(Manufacturer A)

Controller(Manufacturer B)

Field
level

„Communicative“
components„Smart devices“

Classic components
1Prof. Dr. Michael Krödel

Illustration 2: linking building automation with a (cloud-based) AI platform

Demands on building infrastructure
The AI platform requires a rich set of data from a variety of sensors around the building to operate
effectively. Conventional smart building systems use a sensor network to determine the status of abuilding now.‘ Cognitive“ buildings store and analyse historical sensor data to make predictions forthe future. For this reason, such buildings are even more critically dependent on the data inputs theyreceive for their success. Cognitive buildings need to be instrumented throughout with IoT sensor
devices that make the algorithms fully aware of every aspect of their operation: environment,occupants, energy requirements, service needs, security, and safety. The richer the data, the more
complete and intelligent the response of the AI. Wiring sufficient sensors into an established building
is hugely expensive – and even if it were done would create an inflexible architecture that couldn’t
be adapted as new applications emerge and learning progresses. The only effective solution isbattery- and maintenance- free energy harvesting sensors that can be fitted in a moment and movedat will. Energy harvesting wireless devices utilize the tiniest amounts of energy from theirenvironment. Kinetic motion, pressure, light, differences in temperature are converted into energy
which, in combination with ultra-low power wireless technology, creates maintenance-free sensor
solutions for use in smart buildings and the IoT.
The EnOcean Alliance eco-system offers more than 5,000 multi-vendor interoperable energyharvesting sensors enabling data collection for multiple applications, such as room or desk/chairoccupancy, temperature and air quality, energy usage and restroom usage. In addition to thetraditional option of collecting and analysing the data via the BMS (Building Management System),white paper
this can now be done by using the existing Wi-Fi network with the building. By securely interfacing
those IoT devices with new and existing Aruba Wi-Fi 5 and Wi-Fi 6 Access Points via a plug-in
800/900MHz radio, building control and business applications can become hyperaware of theiroperating environments. This information can be used to better model building behavior, tooptimize human activity monitoring, organizational redesign, augmented reality, human productivity,and occupant health and safety.

Conclusions
AI-based processes enable a broad range of applications in the field of building automation. The
concrete benefits anticipated from AI-based solutions should be clearly defined before
implementation, since this plays a determining role in the choice of learning process and its
modelling, as well as in the choice of AI platform and the type, number and location of the energy
harvesting sensors needed to supply the data inputs.

www.igt-institut.de
www.enocean-alliance.org