1 Introduction

Nearly Zero Energy Buildings (nZEB) not only consist of a highly insulated and an airtight building envelope but also have elaborated systems for sanitary hot water, heating, ventilation and cooling (HVAC). A Building Management System (BMS) managing the HVAC systems is essential in these nZEB buildings to maximize the indoor comfort while minimizing the energy use. Automatic control of ventilative cooling is one component of this BMS and is essential to guarantee an efficient and effective usage of ventilative cooling. The purpose of ventilative cooling control is to adapt the airflow rate to the actual cooling demand without compromising the thermal comfort and the indoor environmental quality. As a consequence, both ventilation and control system have to be integrated and designed in parallel.

This chapter describes the main control strategies and components, including actuators and sensors, with special regard to natural ventilative cooling. Case studies and practical applications are studied to define guidelines and suggestions to effectively control ventilative cooling systems in residential and non-residential buildings in different climates.

2 Control Strategies

2.1 Overview of Strategies

Manual control of ventilation openings has the advantage of its simplicity and low maintenance, but the disadvantage that it may not respond properly to complicated and dynamic external circumstances nor internal activities of occupants. As a consequence, spontaneous occupant control generally shows sub-optimal performance regarding energy savings and thermal comfort [1]. The following automated control strategies for window operation are proposed to better realize ventilative cooling potential in buildings. First is conventional rule-based heuristic control [2], second the more advanced model predictive control (MPC) [3, 4], and third newly developed control strategies that use machine-learning techniques [5].

Rule-based heuristic control is the most common control strategy for operable windows and HVAC systems. This strategy can be visualized in a decision tree, where each parameter is compared to threshold value (or criterion) causing a specific action or leading to another leaf node with another criterion comparison (see Fig. 6.1a). Rule-based heuristic control has a fairly low technical barrier when implemented in a real system hardware and an adequate performance is typically expected [6].

Fig. 6.1
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Schematic representation of rule based control (a), model predictive control (b) and reinforced learning (c) [6]

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A model predictive control (MPC) strategy, as seen in Fig. 6.1b, optimizes the actions by simulating the system using a physical or empirical model on a finite time-horizon. MPC has already shown savings for hydronic systems in operating buildings as indicated in recent studies (e.g. [7,8,9,10]), but is not widely used in ventilation systems. Potential of MPC in ventilation is amongst others studied by [11,12,13,14,15,16,17,18,19,20,21]. A well-designed MPC has the advantage that it can outperform a rule-based heuristic control on both thermal comfort and energy savings. However, MPC is much more complicated and requires an accurate mathematical model of the building system for prediction and optimization [6].

Reinforcement learning methods have the advantage compared to MPC that they are able to find optimal actions without requiring a model of the building and its system. While treating the environment as a black box, the algorithm is able to learn from the interaction with the environment [6], as shown in Fig. 6.1c.

2.2 Challenges

One of the main challenges of controlling natural ventilative cooling components are the outdoor environmental conditions. Rain and wind can limit the window opening. If adequate protection is not provided, window opening has to be restricted to avoid rain ingress or damage to the building [22]. As a consequence, rain and wind sensors are required to establish limitations on the opening control [23]. In addition, control systems need to be designed and controlled accordingly to avoid noise through ventilative cooling openings, e.g. at night or during rush hours (traffic noise). Moreover, during pollen season, occupants with allergies may not want the windows to be opened [23].

Another challenge of control is the security of open air inlets, especially when natural ventilation is used during unoccupied hours for nighttime ventilation. It is common to provide opening windows under occupant control in addition to the vents operated by the BMS system [22].

An next challenge is the interaction with and the behavior of the user. To have a high level of user's satisfaction and acceptability, users should get more information on how to modify or overrule the automatic ventilative cooling control. Although it is recommended that occupants should have the possibility of controlling their own environment, automatic control is necessary to support them in achieving a comfortable indoor climate and to take over when manual control cannot improve the condition and during non-occupied hours [23]. It is a challenge to find a balance between the satisfaction of the user and a good thermal comfort and maximum energy savings by automated control.

3 Control Components

Typical ventilative cooling components can be subdivided in air flow guiding; air flow enhancing; passive and natural cooling; and control and automation components [23]. The latter includes actuators and sensors.

An actuator responds to the output signal from a controller and provides the mechanical action to operate the final control device [22]. The core issue regarding actuation in ventilative cooling is modulating (opening and closing) the inlet and outlet air flow guiding components like windows and louvres.

Automated control includes sensors that measure internal and external environmental parameters e.g. indoor and outdoor temperature, to provide a feedback. Accurate sensors are a vital component of the control because computing or software functions cannot compensate inaccurate information for poor-quality of inappropriately mounted sensors [22].

3.1 Actuators

There is a large variety of actuators available: chain (including folding), linear and rotary actuators are the most commonly used for the operation of ventilation elements [24].

Chain actuators deliver a pushing steel chain and offer the benefit of slim construction without the disadvantage of the rod casement compromising the use of the adjacent space. They are commonly used for windows which are accessible by people, both top hung, side hung and also roof vent windows (see Fig. 6.2). In comparison to linear actuators, chain actuators offer the big advantage of small dimensions but are comparably limited in stroke and force [24].

Fig. 6.2
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Examples of chain actuators to open a window in Solstad Kindergarten in Norway (left) [25] and office buildings in Belgium (middle and right)

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Figure 6.3 shows an example of a linear and folding actuator. Linear actuators consist of solid tubes or prisms, with a push-rod, driven by an electric motor via a spindle or rack and are commonly used for domes, smoke exhaust flaps and for high level windows which are out of reach from persons [24]. Moreover, folding actuators and rotating arms are both suitable for top and bottom as well as side-hung windows and have opening angles even beyond 90° [24].

Fig. 6.3
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Examples of linear [26] (left) and folding (right) actuators

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3.2 Sensors

To guarantee a robust automated control of the ventilative cooling system, sensors have to meet the following requirements [27]. It is important that the operating range of the sensors includes the expected range of the measuring values to avoid errors. In addition, the accuracy determines the precision of the measured data. Before installing the sensors, the level of accuracy needs to be determined. The claimed accuracy of a sensor may not be available over the whole operating range. The accuracy can be affected by the stability of the sensor, hysteresis or environmental variables [22]. As a consequence, a high stability is advised for a period of at least 5 years where no recalibration is needed. A correct and linear output signal with a minimal deviation and low hysteresis is also required. For operation and control of the ventilative cooling, the response time of the sensors is also of importance. With a fast response time the system can control more stable and accurate the actual demand inside the room [22]. Moreover, a high precision and reproducibility and lack of interference with other sensors is important. This is especially the case for wireless sensors [28]. Furthermore, the position of the installed sensors needs attention. A position in direct sunlight or close to a door, window or air supply must be avoided.

Typical internal environmental parameters that are used for ventilative cooling control are: room and/or slab temperature, relative humidity (especially in humid climates or rooms with large humidity production), CO2-concentration or occupancy (especially in buildings with large but variable occupancy). In addition, external environmental parameters for ventilative cooling control include external air temperature, wind speed (to avoid damage or over-ventilation), wind direction (to select windward or leeward openings), rain intensity or precipitation (to avoid rain ingress through large ventilation openings).

For each parameter, different types of sensors are available. Table 6.1 summarizes the characteristics of the different types of sensors available for the most relevant parameters for ventilative cooling control. The listed sensors are well spread within building use to control heating, cooling or ventilation systems.

Table 6.1 Characteristics of sensor types for parameters for ventilative cooling control [22, 28,29,30]
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Three types of sensors are recommended to measure room temperature for ventilative cooling control. Thermistors and resistance temperature devices (RTD) deliver the highest accuracy but have a slower response time compared to a silicon temperature sensor. Silicon temperature sensors are found in most temperature sensors which are used in rooms to control the heating or ventilation system since they are easy to implement and are digital.

For measuring the relative humidity, a capacitive polymer or a ceramic resistance can be used. Both types of sensors have a comparable accuracy and response time. The main difference is the measuring range for both sensors [29].

CO2-concentration inside buildings can be measured by nondispersive infrared (NDIR) sensors, which offer a good accuracy. Response time is usually around 30 s. Still these sensors are expensive compared to temperature and humidity sensors. For CO2-concentration, it is recommended to have a sensor with an accuracy of ±50 ppm in the range of 400–2000 ppm. Recalibration for the sensors is advised at least every 5 years.

For occupancy, two techniques can be used to detect if people are inside the room or not. The passive infrared (PIR) detects if persons are inside a predefined range of the sensor. If people are too far from the sensor no presence is detected. The ultrasonic sensor is more accurate, since they do not use a fixed field of vision [28]. This type of sensor is based on the Doppler effect.

4 Case Studies and Applications

This section analyses the strategies and components that are used to control ventilative cooling. In addition, the specific control strategy of 3 different case studies worldwide is discussed in detail.

4.1 Overview

To evaluate the most common measured parameters and components used for the ventilative cooling control strategy, data is collected from 115 case study buildings [31, 32]. Out of these 115 buildings, 25% are educational buildings, 46% office buildings, 11% residential and 18% other type of building use. For the collected dataset the most common type of ventilation strategy was hybrid ventilation with a total of 61%, automatic natural ventilation was used in 37% of the buildings and manual natural ventilation in 3% of the buildings.

For ventilative cooling, the used control strategy is of crucial importance and depending on the used ventilation strategy of the building. Figure 6.4 shows the parameters used for the ventilative cooling strategy in the collected dataset. The main driver is the thermal comfort of the user as the measurement data demonstrates. In 92% of the cases studied, the internal and external temperature sensors are used to control the ventilative cooling system. CO2 and humidity sensors are also commonly used as control parameters for the ventilative cooling to maintain a comfortable indoor climate, in respectively 75 and 53% of the buildings. In addition, external weather conditions (wind velocity, precipitation) are used for natural or hybrid systems. For purely mechanical systems, the external weather conditions like precipitation or wind, are not considered.

Fig. 6.4
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Summary of sensors used in ventilative cooling control strategies [32]

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The most common type of actuator, used in the studied cases, is the chain actuator, as depicted in Fig. 6.5. In 54% of the evaluated buildings, this type of actuator is implemented for the window control. Two other types that are used are the linear and rotary actuator, in respectively 9 and 5% of the case studies.

Fig. 6.5
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Used actuators for ventilative cooling control strategies [32]

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4.2 Test Lecture Buildings KU Leuven (Ghent, Belgium)

In this educational building in Belgium, two control strategies can be defined as illustrated in Fig. 6.5. The first control is active during occupancy, the second control strategy is active at night for the control of the natural night ventilation. The first control strategy involves the mechanical ventilation system that is active during occupancy and is based on the internal and external temperature. The air handling unit (AHU) cools the supply air by controlling the bypass of the heat exchanger and by using indirect evaporative cooling (IEC) [33].

The second control strategy involves the actuation of the windows and is based on the internal temperature and external weather conditions. The night ventilation is based on predefined criteria based on indoor and outdoor conditions. When the criteria are fulfilled the night ventilation is activated between 22:00 and 06:00 h. When the night ventilation is activated the windows will be opened automatically on both sides of the classroom (Fig. 6.6).

Fig. 6.6
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(Left) Control strategy during occupancy, (Right) Control strategy natural night ventilation [33]

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4.3 Nexus Hayama (Japan)

In this office building in Japan [25] the control strategy is based on the actuation of the high-level automated openings. The natural ventilation is driven by temperature difference and wind and is used in spring (April to June), early summer and fall (October to November). The outside air is introduced from the top and first floor to the occupied zones of the building. The exhaust openings, using the principle of an H-type chimney, are located on top of the building. For the considered control strategy, the following parameters are involved: enthalpy, zone temperature, external temperature, external dew point, wind speed and direction, rain. The ventilation opening is controlled automatically based on the aforementioned parameters and the defined zone conditions as depicted in Table 6.2. Regarding the room temperature control: when the room temperature exceeds 28 °C, the system automatically switches to temperature control and the air conditioning system is used.

Table 6.2 Conditions for operation of the natural ventilation openings [25]
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4.4 Brunla School (Norway)

This school building in Norway [25] is operated by a mechanical ventilation systems combined with operable windows. The natural ventilation in the classrooms is single sided where each window can be opened individually. The control strategy is based on both the desired indoor air quality as the indoor temperature with priority for the thermal comfort. All the ventilation strategy modes are listed in Table 6.3 and are based on internal or external conditions. Opening of the windows is controlled automatically. In addition of the windows, the mechanical exhaust ventilation can be used if CO2 levels are above the predefined set point. When the natural force is insufficient or when high loads are requested the variable speed fan can increase the pressure differences, since the outlet is connected to a duct with dampers and thus control the airflow rate.

Table 6.3 Ventilation control strategy modes [25]
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An external weather station is installed on the roof of the building and the data collected is considered by the control system. The window opening is controlled and is based on the weather conditions and the internal temperature and CO2. Users can override the window opening allowing to open and close the window when desired, however after 15 min the control system regains the control and acts accordingly to reduce the energy use.

5 Lessons Learnt And Suggestions For Design

Each case study in the “Ventilative Cooling case studies” [25] of IEA EBC Annex 62 contains a section “Lessons learnt”. Conclusions regarding control are summarized and structured into the following topics: commissioning and optimization; user interaction and control; and suggestions for design.

5.1 Commissioning and Optimization

Optimization and commissioning of all the systems, including ventilative cooling, in each season are critical to maximize the cooling potential as well as to prevent overcooling. Two aspects can be distinguished in the lessons learnt from the case studies of IEA EBC Annex 62 [25].

First, amongst others, case studies in Norway, Ireland, Austria and Belgium show that the control system has to be trained, fine-tuned, adapted and optimized on a regular basis. It can take considerable time to establish an effective strategy with variable values and parameter settings. Preferably, more than one cooling and heating season is used to optimize the systems.

Second, a data monitoring system is essential to detect malfunctions in the control system and to optimize the interaction of different technical building and control systems. This was shown in an educational building in Belgium and an office building in Austria [25]. However, the Irish case study showed that monitoring is only justified if there is budget to maintain the system as well as to analyze the collected monitoring data [25].

5.2 User Interaction

Different cases show different opinions on the interaction of the user with the ventilation cooling control.

On the one hand, automatic control is preferred to manual control of ventilative cooling. Psomas et al. [34] proved in a residential building in Denmark that during mild summer conditions automated windows may significantly decrease thermal discomfort and overheating risk compared to manually opening of the windows. The effectiveness of occupant manually operated windows or louvres to control ventilative cooling reduces over time. This is shown in the offices in the zero2020 building in Ireland [25]. Occupants take less responsibility for maintaining the indoor thermal comfort. Moreover, manual control is unsuitable in high-rise buildings as experienced in Japan [25].

On the other hand, dwellings in France and China showed that it is really important that the user can overrule the automatic control of ventilative cooling [25]. Also in a Norwegian school, users are satisfied that they are able to control the ventilative cooling system [25]. A hybrid system, i.e. an automated control that indicates to the user when to manually open and close the windows, was tested in the Italian dwelling [25]. The occupants were in general satisfied with the suggestions of the BMS but indicated that manually opening and closing of the windows can become burdensome and is consequently not advised.

It can be concluded that an automated control with the possibility to ignore or overrule this control is the best option for a reliable system with a maximum cooling efficiency as well as a maximum user satisfaction.

5.3 Design Suggestions for Control

In the design of the ventilative cooling control, the users and their expectations of controllability play a central role. The Austrian office building noted that “the future building operator should be already involved in the planning phase to receive know how regarding the technical building systems features. He/she is the key actor for handling the technical building systems and building optimization.” [25] In the apartment building in Japan, it was shown that it is important that the openings can be controlled for each individual tenant to meet their own needs [25]. Controls have also been designed to give flexibility to the occupants as experienced in a computer room in the UK [25].

Another aspect that is important to take into account in the design phase is the future maintenance of the actuators. The users of an office building in Belgium [25] advised to make the location of the actuators (here placed in the extraction stacks) easy accessible for maintenance and replacement. In addition, the Italian case study showed that harmonization of different communication protocols is rather complex and have to be taken into account the design phase [25]. Moreover, the Norwegian case studies underline the importance to have accurate and well calibrated sensors for the control of ventilative cooling.