E-mail: sheryl_grace2001@yahoo.co.in

Prospective Techniques of Effective Daylight Harvesting in Commercial Buildings by Employing Window Glazing, Dynamic Shading

Devices and Dimming Control—A Literature Review

Sheryl G. Colaco1( ), Ciji P. Kurian2, V. I. George1, Anitha M. Colaco3

1. Department of Instrumentation & Control Engineering, Manipal Institute of Technology, Manipal, India 2. Electrical & Electronics Engineering, Manipal Institute of Technology, Manipal, India

3. Electrical & Electronics Engineering, NMAMIT, Nitte, India

Received: 4 August 2008 / Revised: 6 October 2008 / Accepted: 10 October 2008 © Tsinghua Press and Springer-Verlag 2008

Abstract Over a few decades, daylighting has been perceived to possess good potential for energy conservation. In this perspective, there have been significant advances in research methodologies and technologies for optimizing energy consumption through daylight harvesting in commercial buildings. In light of this, a thorough understanding of the application of available technology is very important for daylighting practices for building energy management. The objective of this paper is to examine the status of published research on three key building parameters: window glazing area, dynamic shading devices, and daylighting controls playing a rule on energy conservation. This article may serve as a coherent literature survey that would provide better understanding of the subjacent issues and possibly rejuvenate research interest in this immensely potential field of energy engineering.

Keywords energy conservation, daylighting, window glazing, automated window blinds, daylighting control, dimming ballast, power quality

1 Introduction

Nowadays, increase in energy consumption particularly in commercial buildings and their likely impacts on the environment is a topic of discussion. Literature reports claim that, in commercial buildings air-conditioning and artificial lighting accounts for majority of total energy consumption (Galasiu and Veitch 2006). An important measure to trim down artificial lighting and air-conditioning load and augment savings is to create provision for daylight. From past two decades, quest to light buildings with daylight is enjoying interest from building owners and architects alike.

The recent surge in interest is attributed to three major developments: recent discoveries of the impact of light on human health, the growing influence of “Green Building Rating Schemes”, progress in integrated lighting control technologies for visual comfort and energy saving (Reinhart

and Selkowitz 2006; Reinhart and Fitz 2006). Daylighting is not only an energy efficiency technology but also an architectural discipline (Li and Wong 2007; Miguet and Groleau 2002). Even now in most of the commercial buildings sidelit windows are the only means of transporting daylight and warmth into the buildings. Daylighting in a building requires careful planning to balance heat gain and loss depending on climatic conditions, control glare, illuminance uniformity and adjustment for variation in daylight availability (Gugliermetti and Bisegna 2006; Kurian 2007). For this reason, efficient daylighting techniques require study of proper orientation of building particular to that climate, type of window glazing, lighting controls integrated with artificial light dimming and window shading systems. This strategy requires proper coordination between structural, mechanical, and electrical systems. The intervention of these systems in a building admits daylight without excessive heat gain or glare. This calls for considering an integrated approach combining the

Build Simul (2008) 1: 279–289 DOI 10.1007/s12273-008-8126-8

REVIEW ARTICLE

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daylighting and thermal aspects for optimum cooling, heating and lighting energy balance (Bodart and De Herde 2002). Recent advancements in information technology, fenestration technologies, and electric lighting control options reflect the trends of making buildings more intelligent and energy conserving. All these developments are accredited to the commendable work reported in scientific literature.

Although many studies report the benefits of daylighting on health, environment and energy conservation, the daylighting in buildings are still in their preliminary stages throughout the world (Kurian 2006; Li et al. 2006; Krarti et al. 2005). The lack of information on the suitability of daylighting, system design, and its potential to save energy is the main reason for such an unenthusiastic response to daylighting designs (Li and Tsang 2008; Rubinstein et al. 1990). It is envisaged that this literature survey would reinforce the importance of strategies adopted for reducing energy consumption in daylit commercial buildings.

The intention of the present paper is to summarize the procedures proposed in scientific literature on how the afore mentioned three building parameters can be tuned for effective daylight utilization and energy saving in commercial buildings. The bibliography covering a period from 1977 to 2008 is subdivided into three special topical sections. The first section is a review of impact of the window size, orientation and influence of the glazed area on energy consumption for commercial buildings. The second section examines the energy saving potentials with exterior and interior shading devices. The third section scrutinizes the impacts of dimming of artificial lights on energy consumption.

2 Effect of window area and glazing type

Windows being the integral component of a building play a vital role in deciding the energy demand of a building. Generally, glazing variety used for a window controls the amount of daylight and heat penetrating into an interior. Customarily, glazings are chosen according to performance needs or for its aesthetic appeal. The thermal performance of a glazing is primarily based on U-value as well as solar heat gain coefficient (SHGC) and, visual performance is governed by visible transmittance (Tvis). Figure 1 shows the illustration of the terms: SHGC and U-value. Higher the visible transmittance of a glazing, higher will be the solar heat penetration into a building interior. Heat gain associated with daylight could be both an asset and a liability depending on climatic condition of the region (Gugliermetti and Bisegna 2006; Kim and Jones 1993; Apian-Bennewitz et al. 1998). Accordingly, there is a need

for compromising between light transmission along with heat rejection to cut down the electricity bills on lighting and cooling.

Fig. 1 Total heat gain is the sum of heat gain due to direct solar radiation denoted by solar factor (a) and heat gain due to temperature difference denoted by U-value (b)

Electric lighting and cooling/heating load requirements can be reduced by designing the windows in conjunction with climatic conditions of the locality (ASHRAE 1993; Shaviv 1999). Claridge (1977) examined the potential energy- saving effects of two categories of window management (1) seasonal management and (2) daily management. Heating and cooling season window management were examined in detail for the case of New York City, and a graphical technique of displaying relative energy savings for the range of management techniques were developed. The seasonal management could be often implemented during the planning stage like selection of type of glazing, surrounding trees and overhangs. On the other hand daily management entails the proper exertion of rollers, Venetian blinds and drapes. Claridge (1977) concluded that daily management has

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greater scope of annual savings in most climates relying on the conscientious users or automated devices. While on the contrary, seasonal management demands only infrequent effort on the part of the user, hence savings are more likely to be realized than with daily management.

For a given glazing type, the critical factor determining the daylight entering a building is the window area. The prime requisite for optimum window area specification depends on (i) its ability to provide adequate daylight into the space and (ii) its influence on peak heating/cooling and artificial lighting energy consumption. In addition to this, several other factors such as glare, occupants comfort or aesthetics should be evaluated at a second step, when shading devices are also considered in the integrated design process (Tzemplikos and Athienitis 2005). Moreover the recommendations for energy efficiency and daylighting do not always agree, making it difficult for the architects to choose which window area would be most appropriate (Ghisi and Tinker 2004). Several researchers have documented the most favourable window orientation, window area and glazing types for enhancing the daylight performance in commercial buildings.

Wilkinson (1992) formulated an expression for determining the area of glazing in order to attain minimum expenditure on heating and lighting when a top-up system is employed for the control of electric lights. The author expressed the sum total of energy expended on heating and electric lighting related to energy consumed by heating proportional to window area plus energy consumed by lighting which is inversely proportional to window area. Further, the author presents a graph showing the relationship between total energy expenditure versus area of glazing. It could be observed from the graph (Wilkinson 1992) that the minimum on the total expenditure line is on a rather shallow curve and as a result wide range of glazing areas may be chosen with little effect upon the energy expenditure. Wilkinson (1992) concludes that the proposed approach offers a useful method for investigating a number of aspects related to glazing design and energy consumption.

Similarly, Li and Lam (2000) presented a simplified method to assess the energy efficiency of fenestration systems in terms of the trade off between daylighting and solar heat gain in cooling dominated office buildings. The energy savings are judged based on on-off and top-up controls.

Bülow-Hübe (1998) investigated among other aspects, how glazing type and glazing areas influence both the energy balance and the lighting conditions in a single person office module in Lund, Sweden. The base case office considered had two windows in the south facade with the lower edge of the glazing located 1m above the floor. The base case building considered had a triple glazed

window with clear glass (U=1.97W/(m2·K), Tvis=72%) and glazing to wall area ratio (GWAR) of 30%. The glazing size and the thermal conductance of the wall, glazing and frame was chosen according to Swedish building practice. Bülow-Hübe (1998) noted that the base case building annual energy consumption was 68kW·h/m2, 22kW·h/m2, and 46kW·h/m2 for heating, cooling, and lighting respectively. In order to arrive at optimal window area for energy efficiency, the authors considered the impact of six different glazed areas 10%, 20%, 40%, and 50% GWAR and four different glazing types double clear (U=2.65W/(m2·K), Tvis=80%), triple clear (U=1.97W/(m2·K), Tvis=72%), triple low-e (U=1.57W/(m2·K), Tvis=69%), and superwindow (U=0.97W/(m2·K), Tvis=66%) on lighting, heating, and cooling energy consumption. The simulations for daylight savings were computed with ADELINE 2.0 and thermal simulations were performed with DEROB-LTH. Bülow- Hübe (1998) concluded that the effect of changing the window size showed a large influence on cooling demand and a small effect on heating demand. The authors indicate that heating demand varies almost linearly with the U-value of glazing types. While, the cooling demand increased to some extent faster than linearly with the glazing areas. When compared to base case building, a 10% increase of GWAR increases the cooling demand by 8kW·h/m2 and vice versa for 10% reduction in GWAR. The results (Bülow-Hübe 1998) showed that for an uncoated triple glazing (U=1.97W/(m2·K)), a change in GWAR from 30% to 50% caused an increase of annual cooling demand by 15kW·h/m2 and heating demand by 5kW·h/m2. On the contrary, the super insulated window (U<1W/(m2·K)) outperformed all other glazing types with heating demand reduction of 15kW·h/m2 and cooling demand 1kW·h/m2 when compared to the base case. With regard to window orientation pertaining to Swiss climate the authors reported that when compared to base case north orientation showed highest annual heating demand (+21kW·h/m2) and lowest cooling demand (–12kW·h/m2). The largest cooling demand occurred for S-E orientation (+2kW·h/m2). Cooling demand for both east and west was similar to south while the heating demand was significantly higher than south. Energy consumption of N-E, N-W orientation generally falls between the main orientations. In addition, the effect of daylight utilization resulted in a cooling demand reduction of 6kW·h/m2, increase in heating demand by 4kW·h/m2 and annual electric light saving of 10kW·h/m2 or a reduction of 47% compare to base case.

Ghisi and Tinker (2005) presented a methodology to predict the potential for energy savings on lighting using an ideal window area (IWA) concept when there is effective daylight integration with the artificial lighting systems. According to the authors, an IWA is the one in which there

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is an ideal balance between daylight provision and solar thermal load there by energy consumption due to artificial lighting and air-conditioning in the room is the lowest (Ghisi and Tinker 2005; Jain et al. 2006). The energy analysis was performed using the VisualDOE programme for the climatic conditions of Leeds, in UK, and Florianópolis, in Brazil. The simulation model was developed for a building of 10-storey high with the incremental glazed facade area ranging from 0 to 100%. In order to compare the amount of daylight reaching the working surface in wide shallow rooms with the amount of daylight reaching the working surface in narrow deep rooms, five rooms were generated with the room ratios (ratio of room width to depth) 2:1, 1.5:1, 1:1, 1:1.5, and 1:2. To take into account the influence of the size of the room on the supply of daylight, each room ratio was assessed over ten different room sizes as a function of the ten room indices varying from 0.6 (small rooms) to 5.00 (large rooms). For the simulation of the building model in Leeds a set-point temperature of 23℃ was assumed for summer cooling and 20℃ for winter heating. The occupancy schedule was assumed to be 100% between 9 a.m. and 5 p.m., Monday to Friday, with artificial lighting and equipment operating over the same period. Like wise in Florianópolis, the set-point temperature for summer cooling was assumed to be 24℃, with no heating over the winter period. The occupancy schedule considered an occupation of 100% from 8 a.m. to 12 noon and from 2 p.m. to 6 p.m., Monday to Friday, with artificial lighting and equipment operating over the same period. Ghisi and Tinker (2005) indicate that in Leeds, for an external illuminance of 5,000lx energy savings on lighting ranged from 10.8% to 44.0% and in Florianópolis, for an external illuminance of 10,000lx the saving potential ranged between 20.6% and 86.2%

Ghisi and Tinker (2005) concluded that possibility for energy saving occur when (i) IWA is larger for larger rooms (larger room index)—simulation results for Florianópolis with a room ratio of 2:1 for north orientation indicated that for lowest energy consumption IWA ranges from 10% for a room index of 0.6 to 22% for a room index of 5.00; (ii) the IWA is prone to be larger on the orientation whose energy consumption is lower due to the smaller thermal loads reaching the facade—for the building in Leeds, IWA tends to be larger on the north orientation, since the city is located in the northern hemisphere and the solar thermal load in this orientation is insignificant. Likewise for Florianópolis the ideal window area tends to be larger on the east and south orientation. The authors claim that the methodology can be applied to determine the IWA for any space, in any city world wide for assessing the potential of energy savings due to daylight utilization.

Singh and Bansal (2004) found that for a building with

a U-value higher than 0.6W/(m2·K) the energy required for lighting is much less than the energy needed for heating and cooling. Studying both energy for lighting and cooling/ heating, the window area was optimized for six types of climates in India: hot and dry, warm and humid, composite, moderate, cold and cloudy, cold and sunny. Generally for the cold climates the optimum window area was larger than for the other types of climate and varied between 20% and 40% depending on type of window and the number of solar hours. Using a double glazed low-e window instead of a plain double glazed combination decreases the difference for different orientations and lowers the total energy usage.

Contradictory to Singh and Bansal (2004), Bodart and Herde (2002) showed that an increase in the ratio of window area to floor area from 16% to 32% can reduce the lighting consumption by 12% or 36% for a glazing with the visual transmittance of 20% or 81%, respectively. This was done for a Belgian climate and nine glazing types of various visible transmittances were analyzed. The different locations for which the two studies were made give different lighting needs. It can be noted that the Indian climate gives higher needs for lighting during the post summer depending on how the building construction allow daylighting into the building, while the Belgian building needs more lighting during the winter when less solar radiation is available.

A simple model to estimate lighting energy savings is proposed by Krarti et al. (2005) when the values of perimeter area, window area, and window type are specified. For annual lighting reductions the authors (Krarti et al. 2005) has established a direct correlation between window transmittance and window area. The overall savings are articulated as a function of three normalized parameters window to perimeter floor area (Aw/Ap), perimeter to total floor area (Ap/Af), and percentage annual savings due to daylighting controls ( fd). These parameters provide a useful insight on the relationship between building perimeter, floor area, glazing area and window area and their effects on artificial lighting energy use. The results show that an increase in the Aw/Ap ratio produced higher savings of lighting energy use; additionally greater savings could be realized by increasing Ap/Af ratio. However, it is indicated that for most commercial buildings with transmittance value above 50% increasing window area to floor area ratio above 0.5 daylighting does not provide significant additional lighting energy savings. The results show that for a daylighting aperture of 0.5 maximum lighting reduction ranges from 16% to 68% for Ap/Af ratios of 0.23 and 0.96 respectively. Ihm et al. (2008) extends and validates the application of the proposed methodology to several U.S. and international locations in application with various lighting control strategies like step dimming and continuous

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dimming. The author claims that the proposed method can be applied to any location of the world.

A similar analysis has been carried out in Egypt by El Mohimen et al. (2005) to determine the effectiveness of daylighting in reducing electrical energy consumption for office buildings in Egypt. The authors established an optimal value of window to wall ratio (WWR) of 0.2 to minimize the annual electricity use for office buildings in three Egyptian locations, Cairo, Alexandria, and Aswan. By utilizing daylighting in a prototype office building of Cairo with a clear single pane glazing and WWR=0.2, El Mohimen et al. (2005) estimated a 15% reduction in peak electricity demand and 7% on peak cooling demand.

In general, an attempt was made in this section to highlight some of the research work carried out to establish favourable window orientation, window area, and glazing types for enhancing the daylight performance in commercial buildings.

3 Dynamic window shading devices

Daylight varies instantly depending upon the type of sky, sun position, climate, and window orientation for a given site. The direct sunlight usually causes discomfort glare and more overheating than diffused sky light. For this reason, shading provisions are often considered for south, east, and west facing window orientation (Vartianen 2000). Window blinds serve diverse purpose. They often act as a combined heat and glare protection device to maintain adequate visual and thermal comfort conditions under sunny ambient sky conditions and/or to reduce cooling loads (Choi and Sung 2000). Dynamic exterior shading devices are more effective than interior shading devices in reducing solar heat gain because they block radiation before it passes through a window (Loutzenhiser et al. 2007). Several studies report that overheating and glare, as the prime factors that trigger occupants to manually operate window blinds.

For manual controlled shading devices it is assumed that occupants will not use the blinds to optimize the quality criteria and energy conservation. They alter the shading position only when they are exposed to extreme discomfort. Table 1 present the summary of the assumption made by Leslie et al. (2005) for their work on predictions of energy savings for two proposed daylighting technologies, a daylight-sensing switch and an automatic blinds system.

Table 1 Assumptions of direct solar radiation for manual adjustment of blinds (Leslie et al. 2005)

North blinds are always open South blinds are always closed East blinds are closed until 13:00 West blinds are open until 13:00

In a typical, south-facing office in Toronto, Newsham (1994) examined the impact of manual control of window blinds on annual energy consumption. The author considered an office model of dimensions 4.5m× 7.5m× 3m with one third of the south wall being double glazed with clear glass (U=2.6W/(m2·K)). The internal window blind in the room excluded 50% of solar gain and 80% of daylight (compared to the “open blinds” condition). The heating, ventilation and air-conditioning (HVAC) system heating and cooling were set at 21℃ and 24.5℃ respectively. Artificial light level on desks was set at 500lx for the three positions of the occupants. The room was considered to be occupied between 08:00 and 17:00. For manual operation of blind to occur Newsham (1994) considered that occupants would close the blinds if solar intensity was greater than 233W/m2 depending on their seating position. Further the blinds would remain close until the following morning. Newsham (1994) concluded that, for Toronto climate combined effects of both manual control of lights and blinds, increased annual lighting energy by 66%; lowered cooling energy by 7%, heating energy increased by 17%, consequently the total energy consumption increased by 33%.

Further, a field study carried out Reinhart and Voss (2003) document that people in offices close their blinds to avoid direct sunlight above 50W/m2 and incoming solar gains above 50klx (~450W/m2). Although such actions give a feel of thermal and visual comfort to the occupants, it consecutively affects building energy consumption (Rubin et al. 1978).

“Natural human laziness favours the development of automatic control” (Fontoynont 2003). Automated control of blinds can be efficient and promising means to overcome human inertia and to improve occupants comfort and energy performance. The major requirement for real time control is the automatic determination of blind tilt angle required in order to reduce glare, solar gains, and simultaneously maximum possible amount of daylight into the room (Tzemplikos and Athienitis 2002).

Lee et al. (1998) explored the potential energy saving offered by automated Venetian blind operating in synchro- nization with daylighting controls in Oakland, California. The measurements for calculating cooling load and electric lighting power consumptions were carried out in two side by side offices of dimensions 4.57m× 3.71m× 2.68m furnished with nearly identical building materials and furniture. The window openings were 3.71m wide and 2.74m high with glazed with 6mm single pane, green tinted glass with Tvis=75%, SHGC=0.46, U=0.24W/(m2·K). A curved slat, non retractable, with a provision of only adjustable slat angle, semi specular, white aluminum Venetian blind, 0.127m wide covered the entire vertical height of the window.

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For the energy consumption analysis Lee et al. (1998) considered, a base case and prototype office system. In the base case system, Venetian blind was set to one of three fixed static positions: 45°(nearly closed), 15°(partly closed), or 0°(horizontal) for simulating the “manual” operation through out the day. For the prototype system the Venetian blinds were activated every 30s to block direct sun and maintain daylight design illuminance of 540 − 700lx. The range of motion for the blinds was restricted to 0 − 68°to limit sky view and glare. For both the offices the daylighting control system consisting of photosensor and electronic dimming ballast was programmed to supplement daylight and maintain an average design illuminance of 510lx at the horizontal plane.

Lee et al. (1998) concluded that savings with integration of automated Venetian blinds with daylighting controls ranged from 7% to 15% and 19% to 52% for cooling and lighting loads respectively compared to a static 45°blind angle.

To minimize electric energy consumption for lighting in an office space incorporating motorized window blind system Tzemplikos and Athenitis (2002) presented a methodology for combined lighting-daylighting numerical simulation and calculation of dimming levels for electric lights. The experiments were carried out in Montreal, in an outdoor test room dimension of 2.7m× 2.3m× 2.4m. The window system comprised a double glazed system with low-e coating and highly reflective louvers of dimension 1.08m× 1.08m integrated between the two panes.

Based on the experimental studies Tzemplikos and Athenitis (2002) developed daylight transmittance equations for a dynamic window system, as a function of sky conditions (clear and overcast sky), blind tilt angle, and angle of solar incidence. The simulation results obtained by the application of the methodology shows an annual electric lighting energy saving of 75% for overcast days and 90% on clear days compared to a base case without daylight/dimming controls.

As per information from recent literature application of soft computing techniques (SCTs) such as fuzzy systems, artificial neural networks (ANNs) and genetic algorithms are gaining interest for solving building HVAC and lighting control problems. The flexibility and intuitive use for constantly responding and adapting to dynamic variation in daylight are the chief reasons for regarding SCTs as superior over classical controllers (Dounis et al. 1995; Lah

et al. 2006; Guillemin and Molteni 2002). A fuzzy logic based control algorithm (TU-Wien)

minimizing thermal and artificial lighting energy demand in the building was first developed by the Technical University of Vienna (Bauer et al. 1996). But the TU-Wien algorithm focused only on energy conservation rather than

visual comfort or thermal comfort. In the international energy task DELTA, a blind control project, Bauer et al. (1996) proposed modification of TU-Wien controller for achieving both energy efficiency as well as thermal and visual comfort. The experiments were carried out in two south facing facade office rooms with a floor area of 15.6m2 and with a glazed area of 3.77m2 integrated with simple textile external blinds which could cut the solar radiation to about 10% of the incident radiation. The experimental results show that employment of smart blind controller could achieve artificial light energy savings of 11% and heating/cooling energy savings of 20% to 50%. In addition the smart controller allows artificial lighting energy saving of 8% compared to a user who always keeps the blind half open. The savings accrued would be much higher up to 50% when compared to a user who always keeps blinds closed.

Guillemin and Morel (2001) developed and tested user adaptive controllers for integrated operation of blinds, electric lighting, and HVAC. The authors applied fuzzy logic techniques for automated shading device controller capable of adapting to the user behaviour and to the room characteristics. User adaptation was achieved by means of genetic algorithms that optimize the parameters of the fuzzy logic controllers. The function of shading device was split into two parts depending on the user presence. With the detection of room occupancy, priority was given to visual comfort, while unoccupied priority was given to thermal aspects (heating/cooling energy saving).

Guillemin and Morel (2001) developed the afore mentioned integrated system with three nested control loop levels. First loop (Level 1) made the translation from physical values to the appropriate commands of the corresponding device. Second loop (Level 2) incorporated domain knowledge and used adaptive models for realizing a smart global control strategy. The third loop allowed a tuning of the Level 2 rule base using genetic algorithm (GA) for continuous adaptation to the real building and weather data conditions and to the user requirement and wishes. With the detection of user presence controller switched to the visual optimization mode. In this mode, the algorithm was divided into two parts. The first part determined a maximum blind aperture in order to avoid glare (using a fuzzy rule base) and the second part tried to find the blind position (below the maximum value) that lead to the inside illuminance corresponding to the illuminance set point chosen by the user. When the room is unoccupied for a certain amount of time (typically for 15 minutes at least) the controller switched from the visual optimization to the energy optimization algorithm. Each night, a process of adaptation was undertaken. By the application of GA’s, the system learns to adapt itself to the user’s behaviour and

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to the parameters of the building and the environment. The experiments (Guillemin and Morel 2001) were carried

out in two rooms with similar dimensions of 4.75m× 3.6m × 2.8m. With a south facing window and total window area of 5.75m2 integrated with textile blinds. The rooms were exchanged several times in order to reduce the experimental bias due to room characteristics. Guillemin and Morel (2001) concluded that due to the predictive ability of the controller the integrated system could save 25% of energy during 94 days of experiments.

In a daylight-artificial light integrated scheme, for optimizing visual comfort, thermal comfort and energy consumption Kurian et al. (2008b) proposed a fuzzy logic based automated window blind controller suitable for tropical climate. The authors consider daylight on window as the main fuzzy input for its correlation with the regional climatic conditions rather than a universal climate blind model. A multistage fuzzy logic interface has been presented for reducing the number of fuzzy rules. For occupants comfort and energy conservation, human interactions are taken care of in the model in terms of occupancy and user wishes. The proposed models were developed and validated by application of MATLAB/SIMULINK fuzzy logic toolbox.

Kurian et al. (2008b) derived five different fuzzy algorithms for the development of efficient blind control strategy involving visual and thermal comfort variables along with human interactions: (i) fuzzy controller for visual comfort in the presence of user, (ii) fuzzy controller for visual and thermal comfort in the presence of user, (iii) fuzzy controller for energy optimization in the absence of user, (iv) fuzzy controller for glare control and uniformity in the presence of user, (v) fuzzy controller for visual and thermal comfort in the presence of the user. The proposed methodology was applied to Mangalore (South India) for energy performance evaluation for four cardinal window orientations. By the simulation results Kurian et al. (2008b) concluded that when the proposed fuzzy blind controllers are integrated with daylighting controls, annual energy savings of nearly 20% to 80% could be achieved compared to base case of daylighting control with manual blinds.

A similar attempt has been made by Kristl et al. (2008) for real time harmonization of operations of thermal and illumination system in a test chamber located in Ljubljana, Slovenia. In the experiment a fuzzy logic regulator was programmed to adjust the position of roller blinds and heating/cooling system in variation with outdoor solar radiation and outdoor air temperature. The experimental results present the functioning of fuzzy controller in order to maintain visual and thermal comfort to set point value. However, the authors have made no report of the quantitative energy savings achievable with the system.

In this section an effort was made to draw attention on the research documents illustrating the developmental stages of SCTs for automated window blind systems from the viewpoints of optimizing energy consumption and occupants comfort.

4 Daylighting controls

Electric lighting energy can be easily wasted when (i) lights are on during the periods when a building is partially or completely unoccupied, and (ii) supplying full electric lighting when daylight is available to satisfy lighting requirements partially (Verderber et al. 1983). In 1977 − 1978 Crisp (1977, 1978) reported the preliminary study on automatic artificial light control in accordance with variation of daylight. The purpose was to supplement the available daylight at the task area with just enough electric light to meet the design level. Energy efficiency strategies based on daylight artificial light integrated schemes have proved to be efficient by many researchers world wide (Kurian et al. 2006). Consequently, using a daylight responsive dimming system becomes an appropriate option to reduce the electric lighting energy consumption in spaces where daylight can be a useful source of illumination (Choi and Mistrick 1998; Li and Lam 2003). This section analyses the energy saving capability of daylight responsive dimming systems. Furthermore, the latter part of this section highlights how the benefits of hybridisation between simulation and machine learning can be suitably applied for the purpose of light control.

4.1 Daylight responsive dimming systems

The function of daylight responsive dimming system is to preserve the resulting work plane illuminance (daylight+ dimmed electric light) at least equal to desired illuminance level (Choi and Mistrick 1998). In its basic form, it consists of three essential components: photosensor, artificial lighting controller, and electronic dimming ballast (NLPIP 1998). The basic algorithms prevalently implemented in a controller are open-loop, closed-loop, integral reset control algorithms (NLPIP 1998; Doulos et al. 2008; Li and Lam 2003).

Rubinstein and Karayel (1984) examined the energy saving benefits of two lighting control strategies scheduling and daylighting. Investigations were carried out in two commercial buildings Pacific Gas & Electric Company (PG&E) in San Fransisco and former World Trade Center (WTC) in New York. A computer based automatic scheduling operation of lighting systems was incorporated in WTC, in order to conform to occupancy patterns (7:30 a.m. to 5:30 p.m.). Whereas, a continually dimmable lighting controller was installed at PG&E company. The daylighting

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control components used for the experiments in PG&E building consisted of photosensor, an electronic controller a triac based dimming panel, and a special dimming core coil to drive the fluorescent lamps. By weighting the energy savings under clear and overcast sky conditions with relative frequency of clear and overcast conditions for each month of the year, Rubinstein and karayel (1984) calculated an energy consumption reduction due to daylighting controls from a low of 26% (during winter) to 35% (during summer) compared to 10% − 40% due to scheduling techniques. Based on these calculations Rubinstein and karayel (1984) concluded that the dimming system incorporated in PG&E to be more energy conserving than switching system employed at WTC.

In continuation with this study, Rubinstein et al. (1990) presented a first documented demonstration of closed loop photocell control system that could correctly compensate for both changes in daylight as well as lumen depreciation of the electric lighting system. As a modification to previous study a novel two part photocell and electronic dimming ballast capable of providing dimming range form 100% to 20% were used. The experimental results (Rubinstein et al. 1990) due to nine months of operation showed a lighting energy saving of approximately 50% due to integrated operation of daylighting, lumen maintenance and scheduling.

Bodart and Herde (2002) evaluated by simulation the impact of lighting management as a function of daylight availability in office buildings of Belgium. The simulations were performed with Superlink software. For nine different glazing types with visible transmittance ranging from 0 to 81%. The results show an annual energy saving energy of 35% to 45% depending on the window orientation and glazing transmittance. However, the simulation results (Bodart and Herde 2002) were not compared with measurements in real office.

A survey in Istanbul, Turkey conducted by Onaygil and Güler (2003) had the aim of finding out how much energy could be saved by using a daylight responsive lighting control system according to the type of sky. The experiments were carried out in an office building room of dimension 3.35m× 6.25m× 3.25m with a window area of 3.03m2 facing north-east direction. The room was equipped with six lumianires and seven sensors. For the study the lighting system was adjusted for maintaining a constant illumination level of 500lx on the working desk. When the daylight level at the work desk exceeded 500lx, the lighting controller was programmed to switch off automatically. The total illumination values (daylight+artificial light) measured by these sensors were recorded continuously by computer through a data collection unit every 6min for 174 days from 8:00 a.m. to 6:00 p.m. For that particular year of study Onaygil and Güler (2003) identified 109 clear,

38 mixed, and 27 overcast days. The results presented show an accrued energy saving of 35%, 33%, and 16% for clear, mixed and overcast days respectively. Onaygil and Güler (2003) concluded that for any climate similar to Turkey encompassing 62% clear days, a minimum of 30% lighting energy could be saved by employing automated lighting controls.

Duolos et al. (2008) compared the performance of eighteen samples of EDB (electronic dimming ballast) from five manufacturers to quantify their impact on energy savings in daylight responsive dimming systems for two control algorithms: closed loop and integral reset. In the experiment, each ballast were manually dimmed through a digital power supply from 10V to 0V to study the relationships between light output ratio and control voltage, consumed power and control voltage, power factor and control voltage, and finally consumed power and light output ratio. The simulations were performed on a hourly basis using DAYSIM software for Athens, Greece. Duolos et al. (2008) concluded that the control algorithm dominates the performance of the daylight responsive dimming system. The maximum annual difference in the energy saving values between the tested EDB’s was estimated equal to 5.9% for closed loop algorithm as against 5.74% for the case of integral reset algorithm. Also, closed loop algorithm performed much better than integral reset to meet the target illuminance for more hours during the day.

Benefits of hybridization between simulation and machine learning can be advantageously used for the purpose of light control. Because, such a controller would progressively learn to adapt to building and environmental characteristics. However, from the literatures (Guillemin and Molteni 2002; Bauer et al. 1996; Guillemin and Morel 2001), it is found that most of the researches have devoted the application of artificial intelligence and adaptive predictive techniques to window blind controllers to control heating, cooling, and occupant comfort. For the dimming control of artificial light, a photo-sensor based technology is still being used. In this regard the application of adaptive predictive techniques for dimming of artificial lights employing adaptive neuro fuzzy inference scheme (ANFIS) proposed by Kurian et al. (2005) shows a possibility of using a model based artificial light control technology.

An attempt has been made by Kurian et al. (2008a) for applying simulation assisted computational model for adaptive predictive control in a daylight-artificial integrated scheme for energy saving, visual comfort, and thermal comfort. For maximizing energy saving while optimizing the performance and the quality of the visual environment the author proposes an integrated scheme comprising of: (i) a system identification approach for lighting control

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strategy, (ii) a fuzzy logic based window blind controller to reduce glare, increase uniformity and thermal comfort, and (iii) an adaptive predictive control scheme for the dimming of artificial light. In addition, the scheme was so designed as to coordinate and control the automated electric lights as well as window blind systems as per user presence and user wishes. The simulation results (Kurian et al. 2008a) carried out for tropical climates of Manipal, South India using test reference year (TRY) 2005 show that ANFIS dimming with a fuzzy logic based window blind controller provides complete optimization of thermal comfort and visual comfort including both glare control and uniformity in the interior with an annual energy savings of 23% − 49%. Whereas, ANFIS dimming with the fuzzy blind controller designed only for glare control show a increased annual energy saving of 35% − 60% according to window orientation.

5 Conclusions and suggestions

The present paper covered the up-to-date overview of those current design methods reported in scientific literature for effective daylight utilization. Hitherto, there are many attempts of researchers in reducing the energy consumption by daylight harvesting. The review primarily focused on optimizing energy consumption by tuning of three building parameters: window glazing area, dynamic shading devices and daylighting controls. However, the relative importance of these different factors varies enormously from one installation to other depending on aesthetics and performance needs of the building and the occupants. We have attempted to describe the background and the objectives, and we would emphasize the need for creative design. The recent studies and research described above indicate that the use of “artificial intelligence” for building automation can elevate energy saving besides optimizing visual and thermal comfort. In this regard, it turns out that future research should be in the following directions:

The accurate response of a conventional daylighting control requires optimization of proper positioning of photocell and calibration of control system. The application of adaptive predictive control systems with real time linking of defined controller routines can overcome these drawbacks of sensor dependency. Test the scheme in realistic scale experiments and improve the strategies according to real time execution issues.

With predictive control systems it is possible to reduce the number of sensors/connections preserving or improving the control system performance leading to a cost dropping activity (EDIFICIO). Laboratory studies should be carried out in this regard.

The integration of intelligent services in building automation industry is still poor and users are not yet convinced of the system reliability. These new developments could be disseminated by formation of network between manufacturers, building professionals, and energy researchers. Joint effort could be made to determine solutions to common problem areas.

6 References

Apian-Bennewitz P, Goller M, Herkel S, Kovach-Hebling A, Wienold J (1998). Computer-oriented building design: Advances in daylighting and thermal simulation tools. Renewable Energy, 14(1 − 4): 351 − 356.

ASHRAE (1993). Fundamentals Handbook. Atlanta, GA: American Society of Heating, Refrigerating, and Air-Conditioning Engineers.

Bauer M, Geiginger J, Hegetschweiler W, Sejkora G, Morel N, Wurmsdobler P (1996). DELTA, a blind controller using fuzzy logic. Final OFEN Report, LESO, EPFL.

Bodart M, De Herde A (2002). Global energy savings in offices buildings by the use of daylighting. Energy and Buildings, 34(5): 421 − 429.

Bülow-Hübe H (1998). The effect of glazing type and size on annual heating and cooling demand for Swedish offices. In: Proceedings of the Renewable Energy Technologies in Cold Climates ’98 , Solar Energy Society of Canada Inc. (SESCI), Montreal, Canada, pp. 188 − 193.

Choi AS, Mistrick RG (1998). Analysis of daylight responsive dimming system performance. Building and Environment, 34(3): 231 − 243.

Choi AS, Sung MK (2000). Development of a daylight responsive dimming system and preliminary evaluation of system performance. Building and Environment, 35(7): 663 − 676.

Claridge D (1977). Window management and energy savings. Energy and Buildings, 1(1): 57 − 63.

Crisp VHC (1977). Energy conservation in buildings: A preliminary study of the use of automatic daylight control of artificial lighting. Lighting Research &Technology, 9: 31 − 41.

Crisp VHC (1978). The light switch in buildings. Lighting Research &Technology, 10: 69 − 75.

Doulos L,Tsangrassoulis A, Topalis F (2008). Quantifying energy savings in daylight responsive systems: The role of dimming electronic ballasts. Energy and Buildings, 40(1): 36 − 50.

Dounis AI, Santamouris MJ, Lefas CC, Argiriou A (1995). Design of a fuzzy set environment comfort system. Energy and Buildings, 22(1): 81 − 87.

EDIFICO, Efficient Design Incorporating Fundamental Improvements for Control and Integrated Optimization, http://lesowww.epfl.ch/downloads/Edificio.pdf.

El Mohimen MA, Hanna G, Krarti M (2005). Analysis of daylighting benefits for office buildings in Egypt. Transactions of the ASME: Journal of Solar Energy Engineering, 127: 366 − 370.

Build Simul (2008) 1: 279–289 288

Fontoynont M (2003). Comment 1 on “monitoring manual control of electric lighting and blinds” by C.F. Reinhart and K. Voss. Lighting Research and Technology, 35(3): 258.

Galasiu AD, Veitch JA (2006). Occupant preferences and satisfaction with the luminous environment and control systems in daylit offices: A literature review. Energy and Buildings, 38(7): 728 − 742.

Ghisi E, Tinker JA (2004). Window sizes required for the energy efficiency of a building against window sizes required for view. In: Proceedings of the CIB World Building Congress, Toronto, Canada.

Ghisi E, Tinker JA (2005). An ideal window area concept for energy efficient integration of daylight and artificial light in buildings. Building and Environment, 40(1): 51 − 61.

Gugliermetti F, Bisegna F (2006). Daylighting with external shading devices: Design and simulation algorithms. Building and Environment, 41(2): 136 − 149.

Guillemin A, Molteni S (2002). An energy-efficient controller for shading devices self-adapting to the user wishes. Building and Environment, 37(11): 1091 − 1097.

Guillemin A, Morel N (2001). An innovative lighting controller integrated in a self-adaptive building control system. Energy and Buildings, 33(5): 477 − 487.

Ihm P, Nemri A, Krarti M (2008). Estimation of lighting energy savings from daylighting. Building and Environment, doi: 10.1016/j.buildenv.2008.04.016.

Jain P, Kanoongo J, Goyal T (2006). Study of power consumption and temperature profile in glazed buildings. In: Proceedings of the National Conference on Advances in Energy Research (AER-2006), Bombay, India, pp. 164 − 169.

Kim JJ, Jones JA (1993). Conceptual framework for dynamic control of daylighting and electric lighting systems. In: Proceedings of the IEEE Conference, Toronto, Canada, pp. 2358 − 2364.

Krarti M, Erickson PM, Hillman TC (2005). A simplified method to estimate energy savings of artificial lighting use from daylighting. Building and Environment, 40(6): 747 − 754.

Kristl Ž, Košir M, Lah MT, Krainer (2008). Fuzzy control system for thermal and visual comfort in building. Renewable Energy, 33(4): 694 − 702.

Kurian CP (2006). Advanced Lighting simulation tools: An asset to assess energy saving potentials in lighting. Lighting India magazine, May − June 2006: 78 − 89.

Kurian CP (2007). Optimization and robust control of energy consumption in daylight-artificial light integrated schemes. PhD Dissertation, Manipal University, Manipal, India.

Kurian CP, Aithal RS, Bhat J, George VI (2008a). Robust control and optimization of energy consumption in daylight—Artificial light integrated schemes. Lighting Research and Technology, 40(1): 7 − 24.

Kurian CP, George VI, Aithal RS, Bhat J (2008b). Fuzzy logic based window blind controller maximizing visual comfort, thermal comfort and energy conservation suitable for tropical climate. Journal of Institute of Engineers (India)—Architecture, 89: 14 − 22.

Kurian CP, George VI, Bhat J, Aithal RS (2006). ANFIS model for the time series prediction of interior daylight illuminance. Journal of Artificial Intelligence and Machine Learning, 6(3): 35 − 40.

Kurian CP, Kuriachan S, Bhat J, Aithal RS (2005). An adaptive neuro-fuzzy model for the prediction and control of light in integrated lighting schemes. Lighting Research & Technology, 37(4): 343 − 352.

Lah MT, Zupančič B, Peternelj J, Krainer A (2006). Daylight illuminance control with fuzzy logic. Solar Energy, 80(3): 307 − 321.

Lee ES, DiBartolomeo DL, Selkowitz SE (1998). Thermal and daylighting performance of an automated venetian blind and lighting system in a full-scale private office. Energy and Buildings, 29(1): 47 − 63.

Lesile RP, Raghavan R, Howlett O, Eaton, C (2005). The potential of simplified concepts for daylight harvesting. Lighting Research & Technology, 37(1): 21 − 40.

Li DHW, Cheung GHW, Lau CCS (2006). A simplified procedure for determining indoor daylight illuminance using daylight coefficient concept. Building and Environment, 41(5): 578 − 589.

Li DHW, Lam JC (2000). Measurements of solar radiation and illuminance on vertical surfaces and daylighting implications. Renewable energy, 20(4): 389 − 404.

Li DHW, Lam JC (2003). An investigation of daylighting performance and energy saving in a daylit corridor. Energy and Buildings, 35(4): 365 − 373.

Li DHW, Tsang EKW (2008). An analysis of daylighting performance for office buildings in Hong Kong. Building and Environment, 43(9): 1446 − 1458.

Li DHW, Wong SL (2007). Daylighting and energy implications due to shading effects from nearby buildings. Applied Energy, 84(12): 1199 − 1209.

Loutzenhiser PG, Manz H, Felsmann C, Strachan PA, Maxwell GM (2007). An empirical validation of modeling solar gain through a glazing unit with external and internal shading screens. Applied Thermal Engineering, 27(2 − 3): 528 − 538.

Miguet F, Groleau D (2002). A daylight simulation tool for urban and architectural spaces—Application to transmitted direct and diffuse light through glazing. Building and Environment, 37(8 − 9): 833 − 843.

Newsham GR (1994). Manual control of window blinds and electric lighting: Implications for comfort and energy consumption. Indoor and Built Environment, 3(3): 135 − 144.

NLPIP (National Lighting Product Information Program) (1998). Specifier Report: Photosensors, 6(1).

Onaygil S, Güler Ö (2003). Determination of the energy saving by daylight responsive lighting control systems with an example from Istanbul. Building and Environment, 38(7): 973 − 977.

Reinhart C, Fitz A (2006). Findings from a survey on the current use of daylight simulations in building design. Energy and Buildings, 38(7): 824 − 835.

Build Simul (2008) 1: 279–289

289

Reinhart C, Selkowitz S (2006). Daylighting—Light, form, and people. Energy and Buildings, 38(7): 715 − 717.

Reinhart CF, Voss K (2003). Monitoring manual control of electric lighting and blinds. Lighting Research and Technology, 35(3): 243 − 258.

Rubin AI, Collins BL, Tibbott RL (1978). Window blinds as a potential energy saver—A case study. NSB Building Science Series No. 112, National Bureau of Standards, Washington.

Rubinstein F, Karayel M (1984). The measured energy savings from two lighting control strategies. IEEE Transaction on Industry Applications, 20(5): 1189 − 1197.

Rubinstein F, Siminovitch M, Verderber M (1990). 50% energy savings with automatic lighting controls. Industry Applications Society Annual Meeting, IEEE conference record, Seattle, USA, pp. 2004 − 2008.

Shaviv E (1999). Integrating energy consciousness in the design process. Automation in Construction, 8(4): 463 − 472.

Singh I, Bansal NK (2004). Effect of window type, size and orientation on the total energy demand for a building in indian

climatic conditions. International Journal of Energy Technology and Policy, 2(4): 323 − 334.

Tzemplikos A, Athienitis AK (2002). A methodology for simulation of daylight room illuminance distribution and light dimming for a room with a controlled shading device. Solar energy, 72(4): 271 − 281.

Tzemplikos A, Athienitis AK (2005). The effect of shading design and control on building cooling demand. In: Proceedings of the International Conference on Passive and Low Energy Cooling for the Built Environment, Santorini, Greece, pp. 953 − 958.

Vartianen E (2000). Daylight modelling with the simulation tool delight. PhD thesis, Helsinki University of Technology, Finland, Report TKK-F-A799.

Verderber RR, Rubinstein F (1983). Lighting controls: Survey of market potential. Energy, 8 (6): 433 − 449.

Wilkinson MA (1992). The effect of glazing upon energy consumption within buildings. Lighting Research and Technology, 24(2): 99 − 101.