Introduction

The fact that occupant behaviour has a large effect on a building’s energy consumption is well accepted, see for example the review by Lopes et al. (2012). Occupants affect energy consumption by their behaviour with lighting, appliances, thermostats, etc. and interacting with envelope components such as windows and blinds. This paper focusses on blind use behaviour only.

Occupant interaction with blinds

Van Den Wymelenberg (2012) wrote a review on patterns of occupant interaction with blinds. The reviewed field studies show that blind position has a very low rate of change. Only 15% of manual blinds are adjusted daily or more often. The daily rate of change is higher with motorized and automated blinds. The strongest factors affecting blind use are orientation and season. Blinds are lowered and closed with a higher probability on south facades than on north facades and in summer than in winter.

Another comprehensive review of window shade patterns by O’Brien et al. (2013) came to similar conclusions. The authors reviewed 12 major observational studies and conclude that most office occupants do not operate their shades more than weekly or monthly. The adjustments are based on long-term solar radiation intensity and solar geometry trends rather than short-term events. Occupants do not anticipate future conditions but operate shades based on recent and current weather conditions. The strongest motivation for closing shades is to improve visual discomfort, especially prevent glare. On the contrary, occupants value increased illuminance, views and connection with the outside, and they open the shades to improve these benefits.

Studies on blind use behaviour mostly concern offices (Van Den Wymelenberg 2012; O’Brien et al. 2013). Very little information is available on occupant interaction in other types of buildings, and we cannot generalize the results to residential buildings. The following two studies concern residential buildings.

Bennet et al. (2014) performed a field study to assess how window blinds are manually adjusted in residential buildings. The study concerned one high-rise building located in Ottawa, Canada. The results show that most occupants move the blinds infrequently, similarly to in offices, but with a different schedule. The majority of shade opening occurred in the morning, i.e. occupants wake up, open the shades and leave for work. There was no similar clear pattern for shade closing. Significant factors affecting shade movement include time of day, weekday, orientation and cloudiness/sunniness.

Veitch et al. (2013) performed a survey of household control of indoor climate in Canada and reported results from 455 households in detached homes. The main reasons for closing blinds were found to be privacy, security, eliminating glare and keeping the house cool in summer. The main reasons for opening blinds were found to be to let in more daylight, improve the view to the outside and provide light to plants. In addition, in winter, blinds were opened to let solar heat in. The authors estimated that 90% of people open shades on winter days and 95% close them at night. In summer, a larger portion of people closes shades during the day.

Effect of blinds on heat loss through windows

Blinds affect energy consumption in two ways. Firstly, the blinds reduce solar transmission through the window, i.e. decrease the g value of the window. Secondly, the blinds reduce heat loss through the window, i.e. improve the U value of the window, although the effect depends on the slat angle.

Garnet et al. (1995), Hemmilä (2014) and Hemmilä and Heimonen (2005) have experimentally studied the effect of venetian blinds on window U values. Ye (1997) performed numerical modelling of heat transfer of a fenestration system with venetian blinds.

Garnet et al. (1995) measured a double-glazed unit (0.64 × 0.64 m) with internal venetian blinds. The U value was measured at 2.79 W/m2 K without blinds and 2.22 W/m2 K with closed blinds with a slat angle of − 75° and 2.36 W/m2 K with a slat angle of 75°. The U value decreased 15–20% when the blinds were closed and if the slats were near vertical, but increased 10% to 3.07 W/m2 K when the slats were turned horizontal (slat angle 0°). The numerical calculations by Ye (1997) are in good agreement with the work of Garner et al. except in the case of horizontal slats. Ye calculated the heat transfer rate to decrease (compared to the case without blinds) at all slat angles, i.e. also when the slats are horizontally adjusted.

Hemmilä (2014) and Hemmilä and Heimonen (2005) measured heat transfer through double- and triple-glazed units (1.2 × 1.2 m). The windows were Finnish-type double-sash casement windows, and the venetian blinds were between the sashes. The U value of the double-glazed window decreased from 2.44 to 2.05 W/m2 K (16%) with ordinary white venetian blinds and to 1.68 W/m2 K (31%) with low-emissivity blinds. The U value of the triple-glazed window decreased from 1.74 to 1.57 W/m2 K (10%) with ordinary white blinds and to 1.35 W/m2 K (22%) with blinds with low-emissivity, i.e. the reduction in U value caused by venetian blinds is smaller with highly insulated windows. Similarly to the aforementioned studies, the U value was found to be lowest when the slats are near vertical. With horizontally positioned slats, the U value was found to be slightly smaller than without blinds. Hemmilä noted that the decrease in U value is independent of window size; with larger windows, the effect of blinds is higher than with smaller windows.

Purpose of the work

Although research into energy-related behaviours has focused on the residential sector (Lopes et al. 2012), patterns of occupant interaction with venetian blinds have received little attention. Moreover, although venetian blinds are commonly available and are the most popular shading device (Konstantoglou and Tsangrassoulis 2016; Kirimtat et al. 2016), their energy-saving potential has been largely neglected.

Energy-saving campaigns and guidelines aimed at households focus on room temperature levels and on the use of air conditioners, lighting, washing machines and dishwashers and other appliances, but include no mention of the use of blinds (e.g. De Almeida et al. 2011; Palmer et al. 2012; Behavioural Insights Team 2011; Dahlbom et al. 2009). Occupants are not advised how to use blinds energy efficiently, or if they are, the advice deals with sun protection. The effect of blind use patterns on energy consumption is not well understood.

The purpose of the work is to improve our understanding of the role of blinds as a part of the building energy system. This is valuable for both designing automatic control of blinds and advising occupants towards energy-efficient use of manual blinds.

This study originates from my personal observations in our neighbourhood in Southern Finland. I have noticed that many families keep their blinds lowered and closed for long periods, even when they are not at home. This is true also in the heating season when the sun is shining, i.e. where less heating energy would be needed if the blinds were raised (Fig. 1).

Fig. 1
figure 1

Typical situation: all blinds are lowered and closed although letting solar heat in would reduce heating energy consumption

Full size image

Most studies on blind use behaviour focus on the effect on cooling demand and may include the effect on the need for electric lighting but neglect the effect on heating demand. In this study, in addition to cooling and lighting, the focal point of interest is the effect of blind use on heating energy consumption in different climates.

Method

Computer simulations were performed to study the effect of blind use patterns on energy consumption of single-family houses. The IDA Indoor Climate and Energy (IDA ICE) simulation environment (expert edition, version 4.7.1) (EQUA Simulation AB 2013) was used for all calculations. The detailed window calculations of IDA ICE are based on ISO standard (ISO 15099 2003) and able to handle, for example, the effect of angle of incidence on solar energy transmission and the effect of outdoor temperature on window U value. The simulations not only took into account the heat balance of windows but also calculated the effect on heating, cooling and electric lighting demand.

Three sets of full-year calculations were performed; one set with a multi-zone building and two sets with a single-zone building. The window area and window orientations were fixed in the multi-zone building. Single-zone calculations were performed to study the effect of window size on the results. Table 1 gives an overview of the simulated buildings. The floor plans of the buildings are shown in Figs. 2, 3 and 4. The simulations were performed with weather data from different parts of Europe; four locations from the Arctic Circle to Southern Italy were considered (Sodankylä, Finland 67° N; Helsinki, Finland 60° N; Frankfurt am Main, Germany 50° N and Napoli, Italy 40° N). The simulated buildings were assumed to be sited in open areas.

Table 1 Simulated buildings. Thermal characteristics of windows are presented in Table 2
Full size table
Fig. 2
figure 2

Multi-zone building. Total room area 142 m2. Total window area 19.4 m2

Full size image
Fig. 3
figure 3

Single-zone building with small windows. Total room area 100 m2. Total window area 13.8 m2 (3.45 m2 in each cardinal direction)

Full size image
Fig. 4
figure 4

Single-zone building with large windows. Total room area 100 m2. Total window area 41.5 m2 (10.37 m2 in each cardinal direction)

Full size image

The windows were Finnish-type double-sash casement windows, and the venetian blinds were between the sashes. The outer sash contained a single glass pane, and the inner sash contained a double-glazed insulating glass unit filled with argon gas (Fig. 5). The U value of the glazing was 0.909 W/m2 K. The slat angle was fixed at 85° (near vertical, upper side of the slat turned outward). Table 2 gives an overview of the properties of the windows with and without venetian blinds. No other solar shading than blinds were applied.

Fig. 5
figure 5

Structure of the simulated triple-glazed window and the position of venetian blinds. The outer sash contains one glass pane, and the inner sash contains a double-glazed insulating glass unit filled with argon. Not to scale

Full size image
Table 2 Glazing and shading properties at reference conditions (ISO 15099 2003) calculated by IDA ICE. In the simulation presented in this paper the slat angle of the venetian blinds was 85°
Full size table

A new strategy to control venetian blinds was developed and is presented in Table 3. The strategy aims at minimizing energy consumption by controlling blinds in a way that takes into account the effect of blinds on both solar transmission and heat loss through the window. (For a real-world solution sensors and automation could be integrated into each window. The characteristics of windows with and without blinds could be provided by BIM. In addition, a measurement of solar irradiance outside each window and measurements of outdoor and indoor room air temperature are needed.)

Table 3 New strategy to control venetian blinds. The blinds of each window and room are controlled separately. The slat angle is not controlled but is fixed at 85° (near vertical)
Full size table

The strategy does not include daylighting optimization or control. Electric lighting is controlled based on occupancy and daylight level (open-loop) as described in Table 1, i.e. the use of blinds affects the need for electric lighting.

Table 4 presents the simulated blind use patterns (A–H) that include complete passive strategies, various sun protection strategies and strategies that provide a view to the outside in addition to the new strategy.

Table 4 Simulated blind use patterns
Full size table

Heating, cooling and lighting demands are presented separately in the results. Heating demand includes the energy used by the air handling unit to heat supply air. The air handling unit has a heat recovery unit and a heating unit but no cooling coil (Table 1).

Results

The new control strategy (A) described in Table 3 was found to give the lowest total energy consumption in all three buildings in all four climates.

The proportional savings gained with the control strategy A were higher with larger than smaller windows and higher in southern than northern locations, as shown in Figs. 6, 7, 8 and 9. The absolute consumption values are presented in Tables 6, 7 and 8. Figure 10 shows an example of blind use patterns when strategy A is employed.

Fig. 6
figure 6

Total energy consumption of heating, cooling and lighting compared with strategy A in Sodankylä, northern Finland. Cases A–H refer to the different blind use patterns described in Table 4

Full size image
Fig. 7
figure 7

Total energy consumption of heating, cooling and lighting compared with strategy A in Helsinki, Southern Finland

Full size image
Fig. 8
figure 8

Total energy consumption of heating, cooling and lighting compared with strategy A in Frankfurt, Germany

Full size image
Fig. 9
figure 9

Total energy consumption of heating, cooling and lighting compared with strategy A in Napoli, Italy

Full size image
Fig. 10
figure 10

Example of blind position for one window when strategy A is implemented. A week in February in Helsinki, Finland

Full size image

The single-zone building with small windows gave similar results to the multi-zone building as the proportional amount of windows is similar in these building models.

One of the two completely passive use patterns (always lowered and closed, always raised) was the most consuming (Table 5):

  • Strategy F (always raised) was the most consuming if windows are large, except in Sodankylä (Table 8).

  • Strategy F (always raised) was the most consuming in the south (Napoli, Italy) irrespective of window size.

  • Strategy E (always lowered and closed) led to highest consumption if windows were small and the building was located in central or northern Europe. In Sodankylä, strategy E was the most consuming irrespective of window size.

Table 5 Comparison of total annual energy consumption of blind use patterns A–H. Results are presented in more detail in Tables 6, 7 and 8 and Figs. 4, 5, 6 and 7
Full size table

Total energy consumption always increased with window size, although lighting consumption decreased.

The smallest proportional savings from using strategy A were gained in the northernmost location (Sodankylä, Finland) (Fig. 6). The absolute savings from using strategy A compared to strategies B–H were, however, higher on average in Sodankylä than in Helsinki and Frankfurt.

Strategy G (always lowered and closed during nights, always raised in daytime) is simple and gave generally good results if windows were not large and if the building was not located in the south (Napoli, Italy). Although strategy G always led to higher consumption than strategy A, the proportional difference in total consumption was small if the need for sun protection was low.

The sun protection strategy was simulated with different threshold values (B–D). The blinds were lowered and closed when the solar radiation level outside the window exceeded the threshold value. In northern and central European climates, the total consumption was lowest with a threshold value of 200 W/m2, whereas in the southern European locality (Napoli), the lowest total consumption was achieved with a threshold value of either 100 or 200 W/m2 (Tables 6, 7 and 8).

Table 6 Annual energy consumption (kWh) of multi-zone building. Cases A–H refer to the different blind use patterns described in Table 4. Highest and lowest consuming blind use patterns are shown in italics
Full size table
Table 7 Annual energy consumption (kWh) of single-zone building with small windows. Highest and lowest consuming blind use patterns are shown in italics
Full size table
Table 8 Annual energy consumption (kWh) of single-zone building with large windows. Highest and lowest consuming blind use patterns are shown in italics
Full size table

Additional simulation cases were performed to test the effect of window orientation on the results. The new control strategy (A) was applied only to one orientation (south, west, north or east) while the venetian blinds of other windows were always lowered and closed (strategy E). The results presented in Table 9 show that total consumptions are lowest when strategy A is applied to a window facing south and highest when applied to a window facing north. The results regarding a window facing east and a window facing west are similar to each other. The orientation has a proportionally larger effect on total energy consumption in southern than in northern locations: the difference between south and north orientations is 10% in Napoli, 5% in Frankfurt, 4% in Helsinki and 3% in Sodankylä. The absolute difference is, however, highest in Sodankylä where it is 404 kWh.

Table 9 Effect of orientation on the results in single-zone building with small windows. The new control strategy (A) was applied only to one orientation (south, west, north or east) while the venetian blinds of other windows were always lowered and closed (strategy E). Annual energy consumptions in kWh are presented
Full size table

Discussion

Other factors affecting venetian blind use

The new control strategy developed in this work (Table 3) was found to lead to lowest energy consumption in all cases. The control strategy is, however, not fully realistic because energy-optimal blind use is often not satisfactory, as there are other reasons to use blinds than merely optimizing energy consumption. Occupants use blinds especially to improve living comfort, although considerable individual differences in behaviour exist (Meerbeek et al. 2014). The reasons for occupants using blinds and the effect of adjusting the blinds on energy consumption are presented in Table 10. As shown in the table, the effect is situation-dependent, i.e. the effect of these behaviours on energy consumption is dependent on the weather and the current need for heating or cooling.

Table 10 Reasons for the use of blinds and their effect on energy consumption. This overview concerns lowering and raising blinds and does not include slat adjustments
Full size table

Manual and automatic operation of blinds

Manual adjustment by the occupant is the most common way of controlling blinds. Automated blind control is another alternative with advantages and disadvantages. Fully manual and fully automated control represent the extremes of the level of control (Parasuraman and Riley 1997). Their advantages and disadvantages are compiled in Table 11, which also includes a third control option, manual with information system. As it is not easy for occupants to understand how blinds should be used for energy efficiency, an information system could help in daily decisions regarding the blind position. Meerbeek et al. (2016) designed an expressive interface that communicates the status and intentions of the blinds systems to the occupants with coloured arrows. Their system had three levels of automation for blind operation: (i) fully automatic, (ii) suggestions automatically carried out if the user did not act within 50 s and (iii) suggestions made, but responsibility for action left to the user.

Table 11 Control options for blinds
Full size table

Having personal control over the living environment has been found to have positive influences on occupant satisfaction in numerous studies, e.g. Luo et al. (2014), Toftum (2010), Brager et al. (2004), Leaman and Bordass (2000) and Jaakkola et al. (1989). Fully automatic control of blinds is unlikely to be preferred by occupants due to their generally high level of mistrust towards automation and their desire to be in control of the system (Karjalainen 2013). Fully automatic control of blinds may be more acceptable in periods of absence or, e.g., in the living room during night time.

Reinhart and Voss (2003) studied automatically controlled venetian blinds with a manual override in German offices. They found that occupants were more willing to accept automatic blind opening than closing. Occupants re-opened the blinds in 45% of instances when they were automatically lowered.

Guillemin and Morel (2001) created and evaluated a lighting control system that involved control of blinds. Considerable energy savings were gained in offices, but questionnaires showed that users quickly resented the automatic system when it did not take into account their wishes. The researchers conclude that the benefits of an automatic system are lost if the users reject it. Meerbeek et al. (2014) performed a field study on motorized exterior blinds and found that in the majority of offices automatic mode was switched off.

Sadeghi et al. (2016) studied human interactions with motorized roller shades in offices and found that higher daylight utilization was observed with easy-to-access controls. The results showed a strong preference for personalized indoor climate instead of fully automatic operations.

Manual control, however, also presents its problems. The number of windows and blinds in a single-family house can be high, making it bothersome to manually control blinds energy efficiently. Moreover, it is not easy for occupants (even when motivated) to know when to open and close blinds for maximum energy efficiency. Practical issues such as these should be taken seriously in the creation of energy-efficient buildings. It may be advantageous to place less onus on the occupant to learn how buildings work and not to suppose that occupants are motivated to save energy and eager to perform specific energy-saving actions (Karjalainen 2016).

The optimal system seems neither to be fully manual nor fully automatic but has some characteristics of both and is able to deliver a good balance of comfort and energy efficiency. One review of dynamic shading systems (Konstantoglou and Tsangrassoulis 2016) has concluded that occupants are more willing to accept dynamic shading systems if it is possible to override the control system. The expressive interfaces developed by Meerbeek et al. (2016) were found to increase user acceptance of automated blinds. In addition to the carefully chosen level of automation, the following system characteristics may potentially improve acceptance of domestic automation: predictability, transparency and feedback; simplicity and usability; and suitability for everyday life (Karjalainen 2013). Future work is needed to create the optimal solution that has characteristics of automatic and manual operation.

Most venetian blinds are fully manual and are likely to remain so in the near future. Occupants could save energy significantly (with no installation costs involved) if they knew how to operate their blinds energy efficiently. Simple guidelines for energy-efficient use of manual blinds (not aiming at ideal behaviour) can be given as follows. In the heating season, (1) raise the blinds and let solar heat in when the sun hits the window and (2) lower and close the blinds at night time, especially in cold weather. In summer, or when no heating energy is needed, lower and close the blinds when the room temperature rises in order to decrease cooling demand. The simple guidelines are based on pattern G but also include sun protection.

Considerations regarding the simulated buildings

The effect of blind use behaviour on energy consumption depends on a wide range of factors. Although a large number of simulations were performed, not all factors could be taken into account. Simulations representing different occupant behaviours were performed in four European locations from north to south and with two window sizes in all of the four locations. Only two blind positions (fully lowered with slat angle of 85° and fully raised) were calculated. Other characteristics, such as building structures and temperature set points (Table 1), also have an effect on the results but were kept constant in the simulations. The level of indoor heat gains also affects the results. The simulated buildings were well-insulated, which is typical for Finland but not for southern locations such as Italy.

The simulated windows were of ordinary Finnish type with triple glazing. The venetian blinds were ordinary white blinds located between the sashes. The blinds would have had a larger effect on the thermal properties of the windows in double- rather than triple-glazed windows (see the “Effect of blinds on heat loss through windows” section). It can therefore be supposed that simulations performed with double-glazed windows would have resulted in larger proportional differences in energy consumption of blind use patterns A–H.

Conclusions

Venetian blinds are the most common type of sun protection device. Studies on blind use behaviour focus on sun protection and their effect on cooling and lighting demand but neglect their effect on heating energy consumption.

The new control strategy developed in this work aims at minimizing energy consumption by controlling blinds in a way that takes into account the effect of blinds on both solar transmission and heat loss through windows. The strategy was found to lead to lowest total energy consumption in all the simulation cases, which included four European climates and two window sizes.

Passive use of blinds was found to increase energy consumption compared to active adjustment. Of all of the simulated behaviours, one fully passive behaviour—leaving the blinds always lowered and closed or always raised—led to the highest energy consumption.

The new strategy developed in this work consumes the least energy but is not fully realistic since the energy-optimal blind use patterns may not be satisfactory from the viewpoint of comfort, and occupants may not be willing to accept the automatic behaviour of blinds. Fully manual control requires motivated occupants and may be bothersome because the number of windows and blinds can be high. An optimal system would be neither fully manual nor fully automatic but has characteristics of both, being able to deliver a good balance of comfort and energy efficiency. Future work is needed for developing such system.

Significant energy savings could be achieved just by lowering and raising the blinds wisely. The following simple guidelines for energy-efficient use of manual blinds were drawn up based on the simulation results. In the heating season, (1) raise the blinds to let solar heat in when the sun hits the window and (2) lower and close the blinds at night, especially in cold weather. In summer or when no heating energy is needed, lower and close the blinds as the room temperature increases to reduce cooling demand.