Smart building technologies and services
The group was established in 2016 and current research focus is in new solutions based on sensor networks, human-building interaction and intelligent control strategies of building systems. Research in conducted in close co-operation with companies and associations in the field.
On-going research projects
The aim of the project is to find common methods to share a data between building systems and IT systems.
The project has got funding from KIRA-digi, which is one of the key project of Finnish Government. Duration of the project is from December of 2016 to November of 2017. Participating organizations of the projects are Granlund Oy, City of Helsinki, Senate Properties, Helvar Oy, and Tieto Oyj. The project divided to steps as below:
- A State-of-the-art review and expert interviews of open communication interfaces and semantic data models of building systems
- Open workshop for studying communication interface possibilities
- Piloting of potential interfaces and sematic data models
- National recommendations of communication methods
- An open workshop of outcome of the project The aim of the project is to find common methods to share a data between building systems and IT systems.
The project will create a foundation to new solutions that utilize building measurement data. The project also gives specifications to building owners for the procurement of open building systems. In addition, organizations can exploit the outcome of the project in their device, software, and service development.
Buildings consume approximately forty percent of total energy in modern countries. Most of the building energy usage is consumed in HVAC devices.
Therefore, energy efficient HVAC systems have a great potential in conserving energy and diminishing the climate impact. Multiple energy conserving strategies already exists, however, implementing these strategies are typically time consuming and requires a thorough designing. However, the main function of buildings is to provide a comfortable and adequate environment for its users. Therefore, one should conserve no energy by worsening indoor environment considerably.
A growing sensor number in buildings and the internet of things increases the available data from buildings, which computers can use to learn controlling and optimize the operation of building automation system (BAS). In this project a universal autonomous self –adaptive BAS control algorithms is built by using machine learning and existing data to teach the model.
The model will ease implementing energy efficient HVAC control systems, therefore decreasing the climate impact of built environment. In addition, the model will aim to increase indoor conditions at the most of the time. The model is tested in an existing building after developing the model to demonstrate the functioning of the model.
User comfort and energy conserving may be conflicting objectives. In order to find a balance between energy consumption and human comfort one must understand human behavior in buildings. Utilization highly affects to indoor environment, user comfort and energy consumption, thus, information from user behavior and utilization rates are essential to optimize HVAC operation. However, obtaining reliable information from user behavior or utilization is expensive and has possibly privacy concerns.
Thus, the research starts by developing a method to reliably asses occupation number in buildings by analyzing data provided from their existing BAS. This will ease collecting data cost effectively without a need for investing in devices designed to gather user information. Secondly, a dynamic model is developed to predict occupant number in buildings and its spaces, which the model uses as an input amongst the other for the final model. Lastly, the universal autonomous self –adaptive control algorithm is developed and tested in existing buildings. In order to improve the generalizability of the research, over seventy buildings with different usage will be studied. Buildings are mainly public and private offices, museums and schools.
During this project, the possibility of using wearable sensor devices for thermal comfort monitoring is explored.
People in developed countries spend the majority of their lives indoors, which suggests that the quality of built environments has a substantial effect on well-being, performance, and overall quality of life. Along with energy conservation, providing comfortable conditions is the main objective for indoor environment control.
Great effort has been made to develop control solutions to minimize energy consumption without reducing comfort. Comfort is taken into account in the current indoor environment standards. The required conditions for acceptable thermal comfort, for instance, have been defined in terms of various physical and physiological variables in addition to the indoor temperature range.
The most widely used index for thermal comfort, Fanger's Predicted Mean Vote, is based on the physics of heat exchange between the occupants and their environment. It provides a practical framework to assess thermal comfort of a large population on average. More recent thermal comfort research has focused on modeling individuals' thermal comfort with environmental and/or physiological variables.
Wearable devices have become increasingly popular in consumer use during the past decade. In addition to the traditional fitness and sports applications, wearable devices are now used for several new purposes, such as monitoring stress and sleep quality. During this project, the possibility of using wearable sensor devices for thermal comfort monitoring is explored. The main objective of the project is to develop software for automatic collection of data from multiple sensor sources. Based on the combined measurements from a wearable sensor and room temperature and humidity sensors, a data-driven model is constructed to estimate the user's thermal comfort in real time. Moreover, the suitability of wearable sensors for indoor environment control in general is discussed
The project focuses on investigating the optimal activation of demand response (DR) methods during a DR event for typical city owned buildings.
The optimal activation will be determined with a building energy simulation software IDA ICE and multi-objective building optimization software MOBO. Optimal demand response action activation will be determined with MOBO using variable hourly price rates for supplied energy. The optimization will consider all types of energy supplied to the building, i.e. electricity, district heating and district cooling. The results of the optimization will be compared to base cases with no demand response to determine potential economic gains for customers and for the energy supplier/producer, resulting from changes in energy consumption and production curves.
The aim of the project is to provide some insight on which demand response actions would be the most beneficial targets to focus on when planning participation in local demand response programs. This is accomplished by establishing a premise for optimal order of activation for several possible demand response actions during a demand response. Variable hourly energy price rates are introduced as means to incentivize the participation to demand response for the customer.
The research aims to develop prediction models of the building usage and their spaces using machine learning and typical existing data streams from building automation systems (BAS).
The occupant number and usage rate varies significantly between office buildings. Moreover, the space usage divides unevenly within buildings. The building usage information rarely exists without to mention the usage forecast. A building usage highly affects in user comfort, indoor environment quality, maintenance costs and energy consumption. Usage information and the forecast can help to understand user needs better, improve user satisfaction, increase space efficiency and allocate maintenance resources.
Even now, numerous buildings are of sufficiently modern type that include several sensors measuring indoor conditions and the HVAC device operation. By using this data and machine learning, it is possible to estimate the space usage. Furthermore, usage information enables creating a prediction model that uses occupant history and other relevant data. These methods facilitate to predict usage with greatly less cost and more private than with, for example, separate camera based systems that purpose is in occupant calculation.
The research aims to develop machine learning and data based prediction model for building usage. The research studies the viability of BAS data for building usage prediction and the prediction model accuracy. The study will be conducted in real building environment with the data collected from real office buildings.
The project tests new smart technology solutions in a pilot environment in Otaniemi. The building services being integrated include ventilation, lighting heating and building automation. Multiple sensors are also installed into the spaces and they are used to collect information about various environmental aspects, such as carbon dioxide concentrations and room temperature.
Accelerating digitalization is causing major changes in buildings just like in all other industries. To stay competitive and up to the increasing standards of the end users, the operators in the building and real estate fields of industry must develop smarter solutions. These should improve accessibility, time usage, comfort, well-being of the user and provide experiences.
Achieving this requires collaboration of the various building service systems, Internet of Things solutions, user friendly user interfaces, data analytics and self-learning control solutions. These areas of smart building services are the focus of this project and they are being worked on in close collaboration with various companies and organizations from the industry.
Following companies and organizations are involved in the project: Beckhoff, Caverion, Fidelix, Granlund, Helvar, KNX Finland ry and its affiliates, KT Interior, Mirasys, NCC, Schneider-Electric, Siemens, Swegon, Connected Finland, Soficta and Flexitila.