978-1-5090-2320-2/16/$31.00 ©2016 IEEE

Adaptive Control for Lighting, Shading and HVAC Systems in Near Zero Energy Buildings

Fabio Bisegna, Chiara Burattini, Matteo Manganelli, Luigi Martirano, Benedetta Mattoni, Luigi Parise Sapienza University of Rome - DIAEE

Rome, Italy matteo.manganelli@uniroma1.it

Abstract—Due to the increasing amount of energy consumption and CO2 emissions, Building Automation Systems have been developed in order to increase energy performance in buildings and guarantee indoor comfort at the same time. However, along with the development of the hardware itself, the need to develop also suitable control systems and methods for improving the management of smart buildings, is arising. In this framework, fuzzy logic may be a valuable tool for developing suitable control systems. In this paper, fuzzy logic has been used in order to optimize comfort features and energy demand in an office. Starting from the model defined in a previous work, the aim of the paper is to simulate different configurations of the room and compare the results in order to evaluate the impact of room variations (window geometry, glass properties and wall reflectance) on the fuzzy control system. Given the same solar radiation inputs, it was noticed that different room configurations require a diversified management of the shading systems and artificial lighting control in order to achieve a good balance between comfort and energy consumption.

Keywords— Lighting systems, building automation, efficiency, technical building management, smart building, energy saving, fuzzy logic.

I. INTRODUCTION

A great share of energy demand and CO2 emissions are ascribed to buildings. Efficiency and energy saving in buildings are high priorities, also in European policies (e.g. all new buildings are required to be “nearly zero energy buildings”, NZEBs, by 2020, as per Directive 2010/31/EU). On the other hand, with advancing technology, the importance of guaranteeing high levels of comfort inside the building is growing, having also an impact on energy demand and consumption [1]. According to this, it is important to achieve a balance between energy demand and comfort features.

In a typical office building, artificial lighting and HVAC systems are major contributors to energy consumption; in particular the International Energy Agency [2] has highlighted that artificial lighting consumes a bigger amount of energy compared to cooling and heating systems, due to the offices functional and operational requirements.

New technologies have been developed in order to improve lighting energy performance and comfort and this sector is rapidly increasing and constantly advancing. In addition to the development of more efficient devices, lamps

and light sources, recent research is focusing on lighting design and control. Many studies have introduced strategies to distribute the lighting system inside the room [3], in order to reduce the energy demand by optimizing the illuminance, uniformity and glare levels.

The presence of daylight is also a very important influence factor in the assessment of building energy consumption [4]: on one side, it allows to reduce the artificial lighting requirements but on the other side it can increase air conditioning demand, as in winter for thermal dispersion through glass, as in summer for loads increase due to solar irradiation. Moreover daylight has several positive impacts on human comfort and wellbeing: it enhances the visual comfort, regulates the circadian rhythms, improves the cognitive performance and produces psychological benefits [5].

For these reasons a large number of works investigated the impact of different design and features of windows [6]-[10] and proposed models for optimizing the daylight contribution in indoor building which matches human comfort and energy requirements [11]-[15].

Another important advancement in terms of building performance improvement regards the building façade, especially through the development of dynamic façade components. As the façade is the interface between outdoor and indoor conditions, advanced design and control of façade component [16], especially sun control systems, are recently gaining increased attention, in the direction of “intelligent façades” [17].The study of dynamic shading control systems is a basic aspect for obtaining energy savings and managing the negative thermal and visual aspects caused by daylight in order to achieve a good level of comfort.

The combined control of daylight, artificial lighting and shading systems could allow to reduce lighting energy demand by taking advantage of available daylight. Thus, a synergic management of dynamic shading and artificial lighting control systems leads to a more efficient exploitation of the available resources (solar gains and daylight) and allows a more performing control of the indoor comfort conditions.[18]-[21].

In this framework, a big amount of effective instruments based on new technologies have been introduced for improving building design and operation, in line with the concepts of smart buildings and NZEBs [22].

For example, Building Automation Systems (BASs), and, more precisely Building Energy Management Systems (BEMSs), can improve the energy performance by monitoring and controlling technical services (lighting, HVAC etc.). Originally applied in large buildings, BASs are now available for different kinds of buildings and their effectiveness has been assessed, e.g., in the European standard [23].

However, along with the development of the hardware itself, the need to develop also suitable control systems and methods for improving the management of smart buildings, is arising. In fact, in order to simultaneously manage lighting and thermal behaviors of the building, an overall approach is needed. In summertime, indeed, lighting and cooling energy demands vary oppositely with solar irradiance, thus the shading configuration must produce a tradeoff. On the other hand, in winter, both daylight and solar gain are beneficial.

According to this, suitable algorithms are needed to weigh the energy demands for different uses and to assess an optimal façade configuration. Many models and algorithms have been developed in literature in order to optimize visual and thermal performances of a building. As an example, Gugliermetti et al. [24]-[25] developed a thermal algorithm (IENUS) based on a transfer function method, in which different calculation modules allow to simultaneously manage and optimize the visual and thermal environment.

Some authors have found fuzzy control to be a convenient solution when dealing with the optimization of lighting, thermal and comfort aspect [26]-[29], being a flexible tool for building simple and robust algorithms. In particular, in their previous works, Martirano et al. [30] exploited the potentialities of fuzzy logic in order to program a smart automated system for energy and lighting management in the virtual model of an office room.

In these papers, the balance between energy demand and occupant comfort has been achieved by setting the best position of the dynamic shading system and managing the artificial lighting control system in order to minimize the lighting and thermal electric loads and meeting at the same time the Standard requirements.

Starting from the model defined in [30] and using the same fuzzy logic approach, the aim of the paper is to simulate different configurations of the room and compare the results in order to evaluate the impact of room variations (window geometry, glass properties and wall reflectance) on the fuzzy control system. MATLAB’s Fuzzy Inference System was used to program fuzzy logic controllers and Simulink was used for modeling and simulations. DIVA software was employed for the evaluation of daylight and the design of shading and lighting systems.

II. MATERIALS AND METHODS

A. Room model and setting A smart office has been studied under summer conditions,

on the basis of the model previously introduced by some of the authors [30]. The office room measures 6.6 m long, 4 m wide and 3 m high; the windows are placed only on one of the two

short wall. The room is divided into three zones, according to daylight penetration: window zone (WZ), transition zone (TZ) and internal zone (IZ).

Seven different room configurations, in terms of windows geometry, glass transmission and transmittance and wall reflectance have been analyzed, in order to assess the relation between room features and visual and thermal performance.

The configuration settings are shown in Table I: the conditions have been changed one by one. Case A is the reference case, having the highest values in terms of window size and glass transmission and transmittance.

The room is provided with a dynamic shading system which can vary the percentage of window shield and light penetration, according to the input given by the control system; 25 %, 50% 75% and 100 % Shading factors (SF) have been considered.

TABLE I. ROOM CONFIGURATIONS

Case

Configurations Window Glass Wall

N Size

(length x width)

Transmission %

Transmittance W/m2K

Reflectance %

A 1 4 x 1.2 90 5.82 50

B 1 1.4 x 2 90 5.82 50

C 2 1 x 1.2 90 5.82 50

D 1 4 x 1.2 80 2.71 50

E 2 4 x 1.2 65 1.63 50

F 1 4 x 1.2 90 5.82 90

G 1 4 x 1.2 90 5.82 70

Two alternative electric lighting conditions have been evaluated:

• LED lamps with on-off control (1);• LED lamps with dimming of luminous flux (2).

In both these conditions, 3 in-line luminaries mounting 2 linear lamps have been installed in each room zone; differently from the previous study, lamps with a narrow beam have been used for all the cases, so that the luminous flux emitted by the lamps only influences the relative zone.

The number and the position of the lamps in the room have been defined on the basis the lighting requirements for offices stated in the UNI EN 12464-1[31].

The technical data of the lamps are shown in Table II.

TABLE II. LAMPS TECHNICAL DATA

Lamp Type Data

Luminous flux (lm) Power (W) CCT (K) Ra

LED 2000 25 5000 95

B. Simulations procedure and features The control system for optimizing the energy and lighting

management inside the room has been developed using the software Simulink and it is based on the same hierarchical model developed in [30]. The model is given inputs on occupancy and weather conditions; inputs correspond either to signals produced by sensors or to physical phenomena. An exemplifying occupancy profile has been employed in the simulations which could correspond to the input of proximity sensors for monitoring the presence inside the room and managing the lighting system.

In [30] these inputs allowed the dynamic shading system to change its configuration based on two different modes: the comfort mode and the economy mode. In the first case the shading system allowed daylight to enter the room as much as possible without exceeding the limit imposed by the lighting requirements (minimum levels of illuminance and glare) in order to guarantee the comfort. In the second case the control system managed the level of solar radiation inside the room in order to make the electric loads of the HVAC and lighting systems as low as possible, eventually balancing the lighting and air conditioning systems in order to meet the illuminance and temperature requirements. In this paper a unique mode (comfort + economy) has been developed which simultaneously controls the lighting and thermal inputs optimizing both comfort and electric loads (See Figure 1).

Fig. 1. Control system framework

The illuminance and temperature values inside the room are monitored by sensors. One control group of lighting sensors (CG) has been installed for each of the three zones. Finally, the model produces outputs on shading and electric lighting configuration, which correspond to signals supplied to actuators.

The technical features of the room, which correspond to the main variables of the optimization process, are the following:

• External Illuminance [lx]

• Solar Irradiance [W/m2]

• Occupancy

• Cooling Load Temperature Difference [°C]

• External Temperature [°C]

These parameters hourly vary during the day.

For calculating the thermal behavior of the 7 room configurations during the optimization process, the hourly summer thermal loads have been evaluated through the ASHRAE method [32]. The illuminance level on each zone, at lighting and shading systems partial load operation, has been indeed evaluated as follows:

EUi = j ELij + EDi = j Sj EMLi + SF DFi Eout

where: EUi is the average illuminance on the working plane on zone i; ELij is the average artificial illuminance on zone i from CGJ ; EDi is the average daylight illuminance on zone i; Sj is the control signal of CGJ (0 or 1 when switching, 0 to 1 when dimming); EMLi is the average artificial illuminance on zone i at full power lighting; SF is the shading factor; DFi is the daylight factor of zone i and Eout is the outdoor illuminance.

Differently from [30], the influences of the lamps of the adjoining zones have not been taken into account in EMLi i calculation, since lamps with narrow photometric curves have been used. The values of EMLi have been calculated using the software DIALux.

In order to assess the daylight input inside the room, the Daylight Factor (DF) of each configuration has been calculated using the lighting simulation software DIVA. The results are shown in Table III.

TABLE III. DAYLIGHT FACTORS

Case Daylight Factor (DF) % Window Zone

(WZ) Transition Zone

(TZ) Internal Zone

(IZ)

A 13.35 1.77 0.52

B 7.21 1.56 0.46

C 6.87 0.87 0.24

D 11.77 1.58 0.45

E 9.24 1.28 0.36

F 13.92 1.99 0.65

G 13.63 1.87 0.57

C. Fuzzy optimization A fuzzy logic control system was implemented, according

to the methodology described in [30]. Fuzzy control systems are effective in controlling complex systems, which are hard to be described analytically. They are able to absorb the knowledge of human operators into a linguistic, rule-based engine: the control law is usually described by a set of “if-then” rules, which allow to program the control system basing on experience, without a detailed description of the involved phenomena. In this paper, fuzzy controllers have been defined using MATLAB’s Fuzzy Logic Toolbox.

An economy-oriented control system requires to predict the amount of lighting and cooling electric power necessary for guaranteeing comfort levels and minimizing loads by dynamically varying the shading configuration. In this case the cost function CF is calculated as the total amount of electric power (W) needed for lighting and cooling during the day, as the values of solar radiation SR and shading factor SF vary.

A set of simulations has been run, with increasing solar radiation and shading factor, in order to associate every SF with each value of SR with the aim of minimizing the CF. These optimal configurations, in which SF and SR have been matched, were used for defining a series of fuzzy rules: the values of daylight and occupancy give inputs to the control system which sets the optimal shading factor according to the fuzzy rules. After setting the shading system, the temperature inside the room and the level of illuminance are calculated, in order to evaluate the cooling and lighting loads needed for achieving the comfort requirements. At this point, other rules for lamps control have been introduced, in order to define the optimal lighting system configuration inside the room.

As mentioned in paragraph A, two electric lighting configurations have been evaluated: 1) LED lamps with on-off control; 2) LED lamps with dimming of luminous flux.

In the configuration with the switching control, the occupancy and illuminance levels EUi are detected in all the three zones; in the Window Zone (WZ) and Transition Zone (TZ), if the illuminance levels are higher/lower than the minimum/maximum defined thresholds, the input of switching on or off the lamps is given to the Control Groups of each zone. The lamps in Internal Zone (IZ) are only regulated by the occupancy schedule.

The dimming configuration is instead controlled with another set of fuzzy rules. After evaluating the occupancy of each zone, the difference between the level of EUi and the lighting requirements are calculated: the luminous flux of the lamps is dimmed using an incremental controller which allows to progressively increase or decrease the EUi for achieving the target values.

III. RESULTS AND DISCUSSION

Results of DIVA simulation software (Table III) showed an obvious decrease of DF in the interior zone of the room in all cases; moreover it decreases in all the three zones when glass transmission and transmittance are reduced, while it is influenced in a very little measure by wall reflectance.

These results of DF have been used as input data for the fuzzy process optimization of comfort and energy consumptions.

Fig. 2. Daily energy consumption for cases A-G: LED dimming (left) and LED switching (right)

Fuzzy logic controlled the shading factor, the cooling supply by HVAC and electric lighting in the two hypotheses of dimming and switching on-off the lamps, for each of the 7 room configurations (A-G). Case A has been considered as reference condition and all the other 6 settings have been compared to that.

Total energy consumption for the whole day is shown in Figure 2. Glass type variation causes the widest change in power demand (cases D and E); on the other hand, wall reflectance affects power demand very little (cases F and G). Power demand in time for cases A to G is shown in Figure 3, which is subdivided in two panels: LED lamps with dimming (panels a, top) and LED lamps with switching control (panels b, bottom). The valley between 1 and 2 pm is due to shading closing, lighting switching off and air conditioning reduction, caused by users’ absence during lunch time.

Fig. 3. Daily power demand for cases A-G, for LED lamps with dimming (panels a, left) and for LED lamps with switching control (panels b, right)

Walls reflectance does not affect energy demand, as not relevant changes in DFs have been registered compared to the original A condition. On the contrary, low transmitting glasses reduces the DFs, so that a lower value of shading factor is needed. It allows more daylight to come in and consequently reduces the power demand for electric lighting. The bigger window size in A condition, compared to B and C cases, causes the necessity of higher shading factors, increasing consumption for lighting and reducing power demand for cooling. In the switching configuration, the lower window size in cases B and C, causes oscillations of artificial lighting consumptions during the first and last hours of the day, as shown in Figure 4.

Lighting energy demand is mainly affected by glass type (cases D and E), while it is partially affected by window configuration (cases A to C). The higher lighting consumptions occurred, paradoxically, in the best condition of DF: in these cases the fuzzy optimization logic sets high values of shading factors for achieving thermal comfort, increasing lighting power demand in order to guarantee visual comfort. This behavior is visible in the difference between cases D and E for switching control configuration: with a glass transmission of 65 % energy consumption is lower, since the lamps are set on only in one or two zones (IZ and TZ). On the contrary, lighting consumptions are higher with a glass transmission of 80% since all the lamps are set on due to high shading factors.

Fig. 4. Daily lighting power demand for cases A-G, for LED lamps with dimming (panels a, left) and for LED lamps with switching control (panels b, right)

Energy demand for cooling is equivalent changing window size (settings A to C) and wall reflectance percentage (conditions A, F and G). As shown in Figure 5, the highest variation in energy requirements for air conditioning is due to

glass type (cases F and G), both in dimming and switching configurations.

Fig. 5. Daily air conditioning power demand for cases A-G, for LED lamps with dimming (panels a, left) and for LED lamps with switching control (panels b, right)

IV. CONCLUSION

The balance between consumptions request and comfort needs is one of the most important building challenge from the energy point of view; in this framework, numerous effective instruments based on new technologies have been introduced for improving building design and operation, in line with the concepts of smart buildings and NZEBs.

Nevertheless, along with the improvement and development of innovative systems and technologies, it is also necessary to introduce models which are capable to manage lighting and thermal behaviors of the building from a global and integrated perspective.

The use of solar energy, indeed, allows to reduce the energy consumption for lighting and to improve physical and psychological wellness of the users but it causes at the same time an increase of energy consumption for cooling in summer.

Fuzzy method appears to be a convenient solution when dealing with the optimization of lighting, thermal and comfort aspect. Starting from a previous work developed by some of the authors, a fuzzy logic approach was used to optimize electric power consumptions in an office room. Different configurations of the room have been simulated and results

have been compared in order to evaluate the impact of room variations (window geometry, glass properties and wall reflectance) on the fuzzy control system.

Results showed that variations in wall reflectance do not have an impact on energy consumption; the highest change in power demand, both for lighting and conditioning, is related to glass type as DFs, SHGC and transmittance vary the most. It was noticed that glass with higher transmission and transmittance values (case A) is less performing from the thermal point of view, as expected, causing the activation of the shading systems: it produces an overall increase in energy demand due to the increase of electric lighting supply. As for the glass properties, in the case with the biggest window (case A) power demand for cooling and lighting is higher comparedto the other cases.

Finally it was noticed that artificial lighting dimming control causes an increase of energy consumption compared to switching but it ensures a much smoother power control. In fact it adjusts lighting emission according with illuminance variations on the working plane due to daylight and shading. Switching control is less precise and could cause oscillations in power emission (case B and C) and low level of illuminance in the room which highly affect visual comfort.

The proposed methodological approach clearly requires further developments, such as a more detailed and accurate definition of data input and fuzzy rules. It could be also interesting to test the cases under other environmental conditions and latitudes in order to verify if a different management of the shading and lighting systems would be required.

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