Innovative simulation models for building performance and control

In SMARTeeSTORY, TECNALIA leads Work Package 3, which aims to develop a new approach to operational modelling, optimisation and control of buildings.

The work package aims to develop a new approach to operational modelling, optimisation, and control of buildings by creating methods for understanding and managing large amounts of data. This will result in a tool that will help researchers and organisations analyse and use data effectively. Building Performance Simulation (BPS) is a powerful tool to represent various aspects of buildings’ performance. Lets explore the BPS and discover how SMARTeeSTORY partners use BPS to turn historical buildings into climate-neutral landmarks.

What is building performance simulation?

Building Performance Simulation (BPS) represents various aspects of buildings’ performance using physic-based models. These models consider key building characteristics, such as geometry, constructive materials, heating, ventilation, and air conditioning (HVAC) systems, lighting, and occupancy. By applying mathematical equations based on physical laws, these models simulate building energy behavior under different conditions, which can be simulated using commercial tools like DesignBuilder, EnergyPlus, TRNSYS, Modelica, and APACHE.

By simulating dynamic interactions between energy and mass flows within the building and its surroundings over time, BPS models are capable of characterising energy use, thermal behaviour, and other performance aspects such as illuminance or indoor air quality. Thus, they serve as effective tools for building performance asessment for both designing new buildings and assessing existing ones.

tecnalia building performance simulatoin 0724
Building plans of the office spaces which are the sites of interventions at the Technical University of Delft demo site.

Creating a BPS model

In order to build up a physics-based model, some input parameters must be provided to the tool. These parameters represent various factors affecting internal ambient temperature and indoor environmental quality of a building. They include:

  • External weather conditions
  • Thermo-physical characteristics of the building envelope
  • Occupancy patterns
  • HVAC, DHW and lighting systems description
    (generation and distribution sub-systems, and terminal units)
  • Operational patterns for HVAC, DHW and lighting systems
    (setpoints, schedules, etc.)
  • Specific control strategies for HVAC, DHW and lighting systems
  • Other energy transformation, transmission, and emission components
  • Passive solutions such as: smart blinds, external shading, window opening, etc.

[ HVAC: Heating, ventilation, and air conditioning; DHW: Domestic hot water ]

Best practices for the data gathering process

Creating accurate BPS models involves comprehensive data gathering and a thorough understanding of the building. This is important in existing buildings where building operation in reality may not match the predicted behaviour. Key data gathering steps include:

  • Assessing data availability and accuracy (geometrical, energetic, operational).
  • Conduct technical visits to verify building operation and collect user behaviour data.
  • Engage with maintenance and energy management teams.
  • Consider new monitoring systems if needed.

When gathering data, some required information for the energy model may be incomplete. To address this lack of data the following methods are recommended:

  • Building Data/Information: This includes physical structure, system characteristics, and internal gains. When real building data is lacking, consider using reference values from public databases or local building policies.
  • Weather Data: when measured weather (e.g., outdoor temperature, solar radiation) is not available, consider using public databases from nearby weather stations.

After data gathering, the calibration reduces the performance gap between simulated outputs and real data. In this regard, following steps could be helpful:

  • Assume Input Parameters: Document assumptions.
  • Verify Simulation Results: Check if predicted operating results (e.g., temperature, humidity) are reasonable.
  • Compare Energy and Demand Results: Use metered data for comparison. Assess differences between simulation and calibration data.
  • Revise Assumed Input Data: Adjust assumptions to improve predicted results.

[ steps based on Option D of the International Performance Measurement and Verification Protocol: Concepts and Options for Determining Energy and Water Savings; Volume I: Revised March 2002 ( ]

BPS modelling for building retrofitting

BPS modelling can support various phases from the design to operation, even to test renovation solutions from the digital or physical point of view. In renovation, BPS modelling could focus on the design process and code compliance assessment or even real-time building control as part of the retrofitting.

In SMARTeeSTORY, accurate physic-based models are being developed for different purposes. At an early stage, physic-based models will generate synthetic data to calibrate the long- and short-term prediction models used in following stages. Afterwards, these models will serve for long-term assessment of building performance, to be used throughout the pre-intervention energy assessment and target setting process as well as performance assessment along the operation of SMARTeeSTORY. At last, the physic-based models will create an emulation environment to test control logics and algorithms before their deployment at real building level.

Lessons learned

From our BPS model development experience, we have identified some aspects that should be considered for anyone intending to develop these models, especially in the renovation scenario:

  1. Data Limitations:
    • Lack of architectural and physical data leads to model inaccuracy and reduced reliability.
    • Developing physics-based models is time-consuming. Communication with facility managers or knowledgeable individuals is crucial.
    • When modelling small areas within a building, consider boundary conditions (e.g., surrounding rooms).
  2. Defining Objectives:
    • Clearly define model objectives and functionalities:
      • General performance assessment (e.g., HVAC sizing, envelope comparison)
      • Specific element assessment or control (e.g., smart shading, strategies)
    • Collaborate with partners to address specific needs (e.g., SMARTeeSTORY intervention packages).
  3. Importance of Monitoring Data:
    1. Real data (consumption, indoor temperature, weather) ensures accuracy and reliability of models.
Authors: Antonio Garrido Marijuan, Noelia Vicente Gómez, TECNALIA
In SMARTeeSTORY, TECNALIA leads the Work Package 3 “Modelling, Optimisation and Smart Predictive Control”.
TECNALIA is a private, independent, non-profit applied research centre of international excellence. TECNALIA is the leading private and independent research and technology organisation in Spain and one of the largest in Europe.
TECNALIA has one goal: to transform knowledge into GDP, meaning wealth to improve people’s quality of life by generating business opportunities for industry. TECNALIA is committed to generate major impacts in economic terms, by means of innovation and technological development, addressed by six business divisions covering the economic sectors of Energy, Environment, Industry, Transport, Construction, Health and ICT.

A significant portion of the historic building that houses the Faculty of Architecture and the Built Environment at TU Delft - one of our three demonstrators - is currently undergoing renovations. The area being renovated spans two floors, featuring several open spaces and individual offices. Approximately 40 researchers work in this area.

The renovation plan includes the installation of over 150 sensors (see Figure below). We are pleased to announce that the first batch of sensors, provided by TERA, has arrived for the demo. These sensors will measure indoor environmental quality (IEQ) conditions at desk level, including air temperature, air humidity, and illuminance. These metrics are crucial for assessing occupant comfort. The strategic placement of these sensors will enhance our ability to implement advanced personal comfort models using machine learning techniques.

The remaining sensors, supplied by Schneider Electric, are en route to Delft. These will monitor occupant behavior and help us develop our own Building Management System (BMS). The final installation is scheduled to begin at the end of June 2024, in preparation for summer monitoring and before the SMARTeeSTORY solution is implemented.

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Second floor layout of sensors intervention in Delft demonstrator (Credits: TU Delft)

On 22 October, TU Delft invited families and children to experience the project’s activities first-hand at the University’s annual Science Day 2023. During this free family event, various departments open their doors to welcome children with their parents to learn more about science and technology. The SMARTeeSTORY partners organized several interactive activities to share the project’s and other ideas on human-building interaction.

'Smartification' always sounds a bit like anything and everything, right? Therefore, one of the activities was to ask visitors to describe what they think a smart building actually is. The answers were intriguing, ranging from "moving houses", "climate resilience" and "healthy indoor climate" to "soft" homes - all of which are indeed smart ideas!

Seeing all the people, children and families having a great time while learning about the SMARTeeSTORY research has been the best reward one could imagine. And activities like these really illustrate what is meant by technical terms like “smart buildings” or “user interaction” and how we can use smart appliances to improve our living environments in the future, while at the same time saving energy.

Thank you to the Green Village at TU Delft and the TU Delft Science Centre for hosting and helping us! Thanks to eyrise® B.V. for the collaboration through Face2Face and especially thanks to the SMARTeeSTORY project partners on site, Alessandra Luna NavarroPedro Pablo de la Barra Luegmayer and Pablo Martínez Alcaraz, for organising the event.

On 17 and 18 October 2023, the SMARTeeSTORY project partners met at the Delft University of Technology for their first General Assembly.

The meeting kicked off on 17 October with a site visit to the University building hosting the Faculty of Architecture and the Built Environment. There, the partners learned about the special history of the building as well as its specific challenges with respect to energy management. Originally, this monument from the early 20th century was supposed to be used for chemical engineering purposes, which is why, until this day, it has a chimney and various vents on the façade. Vents are also a common sight throughout the inside of the building, which are nowadays used for the building’s ventilation. However, the building was never used for its originally intended purpose – instead, it served as the University’s main building for many years. In 2008, the ‘Faculty of Architecture and the Built Environment’ then moved into its halls, after their own building was completely destroyed in a fire. The project partners visited the original site of the faculty’s building as well. Over the years, it has been turned into the “Green Village”, a living lab for sustainable innovations in home, work and living environments, where also some of the SMARTeeSTORY implementations will be tested soon.

On 18 October, the project partners gathered inside the Delft demonstrator to hold their General Assembly, discussing project progress, upcoming activities as well as challenges. Spending 8 hours inside the building gave the partners the unique chance to experience its energy-related challenges firsthand: Due to the high ceilings, the numerous (but impressive!) windows, and the overall poor insulation (amongst other things…), the rather chilly October temperatures have made themselves felt in the meeting room as well. A situation that the students and university staff are experiencing on a daily basis and which SMARTeeSTORY hopes to be able to improve in the coming years.

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