Using Simple Light Sensors to Achieve Smart Daylight Harvesting
Jiakang Lu† , Dagnachew Birru‡ , Kamin Whitehouse†
† Department
‡
of Computer Science, University of Virginia
Controls, Communication & Healthcare Informatics, Philips Research North America
† {jklu,whitehouse}@cs.virginia.edu, ‡ dagnachew.birru@philips.com
Abstract
Lighting is the largest single energy consumer in commercial buildings. In this paper, we demonstrate how to
improve the effectiveness of daylight harvesting with a single light sensor on each window. Our system automatically infers the window orientation and the cloudiness levels
of the current sky to predict the incoming daylight and set
window transparency accordingly. We evaluate our system
with ten weeks of empirical data traces collected from windows around an office building and compare our approach
with non-predictive feedback control. Experimental results
show that our scheme can infer the orientation of a window
to within ±7°of the actual orientation and improve energy
savings by 10% over existing approaches without sacrificing
user comfort.
Categories and Subject Descriptors
C.3 [Special-Purpose and Application-Based Systems]: Real-time and Embedded Systems
General Terms
Design, Experimentation, Economics
Keywords
Building Energy, Lighting Control, Wireless Sensor Networks
1
Introduction
Artificial lighting is the single largest energy consumer in
commercial buildings, accounting for 26% of their total energy use on average [1]. Daylight harvesting systems aim
to reduce this energy by using natural sunlight when possible. The key challenge is to provide stable levels of illumination even though natural light is not a stable light source:
an office should have enough light to read and work, but not
so much that it causes glare and discomfort. An emerging
approach is to use electrochromic glass, also called smart
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BuildSys 2010 November 2, 2010, Zurich, Switzerland.
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glass [2], or motorized window blinds [3] to automatically
adjust the transparency of a window. When the natural light
source is too bright, the window transparency is decreased.
When it is too dim, the window transparency is increased
and supplemental artificial lighting may be used. Daylight
harvesting has been demonstrated to reduce lighting energy
by up to 40% in offices that have significant amounts of daylight [4, 5]. In addition, natural light is more pleasant and
comfortable than artificial light and has been shown to increase employee productivity [6].
Despite these advantages, the current daylight harvesting
technology still has limited effectiveness. A recent study
shows that 50% of existing photo-controlled daylight harvesting systems are disabled by the users and the other 50%
operate at 50% of their intended performance [7]. One reason is that natural light levels can change very quickly due
to clouds, movement of the sun, and trees or shadows, while
window transparency can only be changed relatively slowly.
For example, the switching speed of electrochromic windows, or the time required to change from transparent to
opaque, ranges from several minutes to up to two hours, depending on window size and outdoor temperature [8]. Even
mechanical blinds have a maximum switching speed because rapid changes to the blind position has been shown to
cause confusion to the user [9]. This difference between the
speeds of daylight dynamics and window switching speeds
will cause lighting errors. Thus, daylight harvesting systems
must address the risk that future daylight levels will be too
high or too low: being too aggressive about energy saving
may result in comfort loss due to overdimming, while being
too conservative will waste energy by relying on artificial
lighting.
In this paper, we propose a system that addresses this
problem by predicting future daylight levels and setting the
window transparency in advance. Our system has three key
components. First, we monitor long-term trends in daylight
levels to infer the window orientation, which allows us to
predict the maximum expected daylight levels at any time of
day, any day of the year. Second, we monitor light levels to
make on-line estimates of cloud dynamics to predict shortterm variations in daylight levels. Third, we introduce the
daylight weight to manage comfort risk given the user tolerable range of light errors. Our techniques require only a single
light sensor to be installed on the window, which is inexpensive and can easily be installed as an integrated part of the
electrochromic glass or mechanical blinds system. We evaluate our system using ten weeks of empirical daylight data
traces collected from a building-wide testbed. Experimental
results show that we can estimate the orientation of a window
to within ±7°of the actual orientation. Furthermore, our automatic modeling and predictive control approach can save
about 10% more energy than simple closed-loop feedback
control without sacrificing user comfort at various switching
speeds.
2
Cafeteria (W)
Hallway-1 (S)
Background and Related Work
Lighting control systems aim to illuminate a space that
provides sufficient ambient lighting for the needed tasks
to be performed. The Illuminating Engineering Society of
North America (IESNA) recommends 500 lux as the standard task illuminance for office workers performing regular
tasks [10]. Energy consumption for a lighting system can
be determined by the total amount of electricity needed for
the maintenance of the lighting setpoint. Visual comfort depends on the individual user preference. In general, there is
no definitive upper limit as long as excessive light does not
cause glary discomfort. The lower limit is usually discussed
in the context of detectable and acceptable illuminance during demand response: 10-15% is generally within the undetectable range for most people if the base is 500 lux [11],
and up to 40% may be acceptable depending on how slow the
light is dimmed [12]. In this paper, we constrain the scope
of visual comfort to task lighting intensity.
Wireless sensor networks (WSNs) have previously been
used for light sensing and actuation to achieve cost effectiveness, energy efficiency and user comfort. For example,
Singhvi et al. proposed and demonstrated a lighting control system with wireless sensors and a combination of incandescent desk lamps and wall lamps actuated by the X10
system [13]. In addition to office lighting applications, Park
et al. designed and implemented Illuminator, an intelligent
lighting control system for entertainment and media production [14]. High fidelity wireless light sensors were developed and implemented to form a sensor network for collecting stage lighting information [15]. Since the system was
exclusively for entertainment and media applications, energy conservation was not in the scope of their development.
Our system in this paper improves building energy efficiency
through daylight harvesting and maintains user comfort by
achieving stable task lighting despite unstable natural lighting.
Daylight harvesting systems have gradually gained popularity in modern buildings and have been shown to have
the potential for up to 40% energy savings [4, 16, 17].
Granderson et al. developed a framework for a daylighting system using wireless sensing and actuation that simultaneously minimizes energy consumption, balances diverse
user lighting preferences, and increases facilities managers’
satisfaction [18]. Lawrence Berkeley National Laboratory
(LBNL) deployed a daylight harvesting system in the New
York Times Headquarters Building [19] and the field study
enhances the understanding of daylighting controls in real
life. However, a recent study showed that many daylight
harvesting systems are not as effective as expected [7]. The
Hallway-2 (N)
Conference Room (E)
Figure 1. Testbed layout in an office building
techniques we present in this paper use predictive techniques
to improve the energy savings of daylight harvesting without
sacrificing user comfort.
Many emerging technologies have the potential to improve the effectiveness of daylight harvesting in buildings,
but are expensive or costly to install. For example, several
existing systems [20] can accurately track the sun position
during the day and guide daylight controls to avoid possible
glares caused by direct sunlight. However, these costly devices require expertise for the installation and cannot be easily into the existing energy management system. Window
shading technologies such as electrochromic windows [2]
and motorized blinds [3] have become popular for building
energy efficiency. However, the limited maximum switching
speed causes a slow responsiveness to daylight dynamics and
therefore reduces user comfort due to lighting errors. In this
paper, our system mitigates the gap between window control
delay and rapid daylight fluctuation by predicting incoming
daylight with simple light sensors.
3
Smart Daylight Harvesting
We envision a smart daylight harvesting system that exploits daylight sensing information to achieve stable task
lighting by predictively controlling the window transparency,
thereby improving energy efficiency without sacrificing user
comfort. Our system uses inexpensive light sensors installed
at the windows around an office building (Section 3.1).
Based on these sensors, the system employs three key techniques. First, the system infers the window orientation by
analyzing the long-term trends in the sensor data. This information enables us to estimate the maximum expected daylight from a window. Second, we predict short-term variations in daylight levels by estimating the cloud dynamics in
the current sky. The foundation of the prediction algorithm
is the cloudiness classification based on statistical analysis of
empirical daylight measurements (Section 3.3). Third, given
the user preference of light errors, the system automatically
adjusts the maximum amount of daylight that can be harvested in order to manage the risk of visual discomfort due
to daylight instability (Section 3.4). Finally, we present the
control scheme that integrates these automatic modeling and
daylight prediction techniques (Section 3.5). The predictive
4
2.5
Daylight Intensity (lux)
2
(a) Data Logger
(b) Hallway-1(S)
(c) Cafeteria(W)
x 10
Cafeteria(W)
Hallway−1(S)
Conference Room(E)
Hallway−2(N)
1.5
1
0.5
Figure 2. Deployment examples
0
control approach saves energy by allowing more daylight to
be used while also providing user comfort by maintaining
stable light levels.
3.1
Figure 3. Different locations exhibit different shapes of
daylight readings. The peak time of daylight intensity is
a good indicator of the window orientation.
Sensing the Office Building
Daylight intensity and distribution are deeply dependent
on geographic location, building orientation, sky conditions
and nearby surroundings. To explore the characteristics of
daylight in a realistic environment, we deployed a testbed
in an office building located in New York. The locations of
each node, along with the building cardinal directions, are
indicated in Figure 1. The testbed consists of eight U01212 data loggers designed by Onset, as shown in Figure 2(a).
These nodes can monitor the environment with the built-in
light, temperature, and humidity sensors. At each location,
there is a group of two nodes: 1) the node facing outside
measures the incoming daylight on a vertical surface; 2) the
node facing upwards monitors the actual light intensity on a
horizontal plane. Two examples of deployment are shown in
Figure 2(b) and Figure 2(c).
We collected the light sensor data from the testbed at a
sampling rate of every minute, and the duration of the sensor deployment lasts for ten weeks from early November to
late January. During the deployment period, we observed a
variety of weathers, including sunny, cloudy, rain, fog and
snow storm. All the data are stored in the data loggers and
are manually read out from the nodes every week. It is worth
mention that the system presented in this paper does not require any network connection for the communication among
nodes. In future work, we will replace data loggers with
wireless sensors in order to integrate the smart daylight harvesting into the energy management solution for the entire
building.
3.2
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Time
Inferring Window Orientation
One key challenge of daylight harvesting is to identify
window orientations of the building so that the system can
track the sun position relative to each window for better prediction. In order to infer the window orientation, our system analyzes the long-term trends of the daylight levels at
the window. The key idea is that, as the sun path is predetermined given geographic location and calendar date, the maximum expected daylight intensity at any time of day can be
calculated given the window orientation. Our technique only
requires one simple light sensor for each window to monitor
the incoming daylight. Based on the sensor data, we track the
peak time when the maximum daylight measurement occurs
during the day, and estimate the window orientation to be the
direction that has the same peak time of expected daylight on
the same day.
Figure 3 shows the daylight readings from nodes in the
testbed facing each of the cardinal directions on a clear day
and different locations exhibit different shapes of daylight
readings. We take Hallway-1(S) for example to illustrate
how to infer the window orientation. The daylight measurement at Hallway-1(S) reaches the maximum value at 12:27
PM and we infer the window orientation of 192.66°with
the peak time. The inference accuracy largely depends on
the quality of daylight measurements. If the daylight peak
time is not captured correctly by the sensor, the effectiveness of the algorithm will decrease. To address this problem,
our system analyzes the sensor data over a long period and
chooses the result of the day with the most daylight in the
duration of deployment. A detailed analysis of inference accuracy will be discussed in Section 4.4.
3.3
Detecting Sky Cloudiness
Cloud dynamics play a significant role in the practical
performance of daylight harvesting systems. Daylight varies
greatly under different sky conditions and sky cloudiness
may change significantly during the day as well, both of
which result in unstable illumination in the building. Figure 4 is an example that shows the difference in daylight
variance between a cloudy morning and a clear afternoon.
If the cloud dynamics were known, it would be much more
efficient for daylight harvesting systems to maintain a stable task lighting. Thus, it is critical for daylight harvesting
systems to be aware of the cloudiness levels in real time.
In order to detect sky cloudiness, the system must know
the daylight patterns of cloudiness levels. We perform an offline analysis on the empirical data from the testbed to investigate the statistical features of daylight that are indicative of
cloudiness levels. We select two features for each day in the
dataset: (i) average daylight intensity, which is the mean daylight level, and (ii) average daylight fluctuation, which is the
mean absolute difference between two neighboring daylight
measurements. The first feature describes the cloud thickness, while the second feature indicates the cloud movement.
4
2.5
x 10
4500
Hallway−1(S)
4000
Average Daylight Intensity (lux)
Daylight Intensity (lux)
2
1.5
1
0.5
3500
3000
2500
2000
1500
Overcast
Mostly Cloudy
Scattered Clouds
Partly Cloudy
Sky Clear
Centroids
1000
500
0
100
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Time
200
300 400 500 600 700 800
Average Daylight Fluctation (lux)
900
1000
Figure 4. The variations in daylight change under different levels of cloudiness during the day. This example
indicates the difference in daylight variance between a
cloudy morning and a clear afternoon.
Figure 5. While daylight levels are relatively stable on a
clear or overcast day, cloudy days have larger daylight
fluctuations and the variance ranges are determined by
the cloudiness levels.
In the statistical analysis, we apply the k-means clustering
algorithm on the sensor data of ten weeks. Figure 5 shows
the window at Hallway-1(S). The results indicate that 68 experiment days are classified into five clusters, each of which
represents a typical level of cloudiness [21]. All the statistical features for each cloudiness level are stored in what we
call a cloudiness map.
Based on the cluster analysis, we present an on-line algorithm that classifies the sky conditions by employing the
statistical features of daylight that represent each level of
cloudiness. To enable on-line cloudiness detection, our system assumes that the window orientation is inferred, in order
to calculate the maximum expected daylight intensity at any
time of day. The purpose of theoretical values is used to
normalize the daylight measurements to the same scale during the day. The on-line system applies a sliding window
on the light sensor datastream and uses the cloudiness map
as a lookup table. At every sampling time, we extract the
daylight patterns in the sliding window and look them up in
the cloudiness map, and estimate the current cloud dynamics with the best-matching cloudiness level. Assuming that
sky condition does not change significantly over a short time
duration, we estimate the short-term daylight variations with
the current cloudiness and predict the incoming daylight with
the corresponding statistical features.
sus stable artificial lighting, in order to achieve the desired
variance levels.
The key challenge for this technique is to decide the suitable value of daylight weight that satisfies user-defined setpoint and tolerable error range. To avoid manual configuration, our system automatically learns a constant daylight
weight using historical data. Each window will learn its own
daylight weight based on the average variations observed at
that window. After the self-learning process, the trained daylight weight ensures that lighting error falls within the border
of user tolerable range based on the setpoint. In future work,
we expect to be able to use a different daylight weight each
day based on the predicted cloudiness.
3.4
Managing User Comfort
Daylight harvesting systems typically harvest as much
daylight as possible in order to maximize the energy efficiency by reducing the loads of artificial lighting. However,
due to their limited device responsiveness, lighting errors are
inevitable in daylight harvesting and can be translated to the
loss of visual comfort. In our approach, we allow the user to
specify the maximum tolerable variance of the task lighting
levels. We achieve this variance by introducing a new control variable called daylight weight, which tunes the maximum percentage of lighting that can be provided by natural
daylight. In other words, this approach manages the comfort
risk by automatically determining the portion of task lighting that should be provided by unstable natural lighting ver-
3.5
Controlling Task Illumination
We present a control approach that employs the automatic
modeling and daylight prediction techniques to reduce the
loads of artificial lighting while maintaining the desirable
task lighting intensity. Our system automatically infers the
window orientation and detects the cloudiness levels of the
current sky to predict the incoming daylight, set the window
transparency accordingly and use the supplementary electric
light only when the harvested daylight is too dim. Meanwhile, the system monitors the lighting errors and automatically configures the daylight weight in order to limit the error
level within user tolerable range. The effect of the combination of these techniques is to provide stable task illumination
in the working space.
In summary, the window transparency at time t is calculated as:
α ∗ SP
Tw (t) = min(100%,
)
(1)
maxsw
k=0 I p (t + k)
where Tw is the window transparency, I p is the predicted incoming daylight, sw is the sliding window size, SP is the
lighting setpoint, and α is the daylight weight. The harvested
daylight at time t is calculated as:
IH (t) = Ia (t) ∗ Tw (t)
(2)
where IH is the harvested daylight, Ia is the actual incoming
70
140
65
120
60
Comfort Loss (lux)
Energy Savings (%)
100
55
50
45
40
80
60
40
35
25
20
Reactive
Smart
Optimal
30
1
2
3
4
5
6
7
Switching Speed (%/min)
8
9
10
0
(a) Energy Saving
Reactive
Smart
Optimal
1
2
3
4
5
6
7
Switching Speed (%/min)
8
9
10
(b) User Comfort
Figure 6. Performance of Smart Daylight Harvesting
daylight. The light intensity at the user desk is calculated as:
ID (t) = IH (t) + IE (t)
(3)
where IE is the electric light and ID is the desk light intensity.
4
Evaluation
In this section, we first describe the baseline algorithms
and the evaluation metrics that we use for evaluation and
comparison. Then, we present the evaluation results of our
system. Finally, we analyze the accuracy of window orientation inference and discuss the effect of daylight weight.
4.1
Baseline and Optimal Algorithms
We introduce a reactive scheme as a baseline for comparison. The reactive algorithm is a simple closed-loop feedback
control that periodically measures the current daylight, responds to the daylight change and satisfies the user-defined
setpoint by controlling window transparency and electric
light. The adjustment of window and electric light is constrained by the switching speed, which is defined as the maximum percentage of allowable lighting change in one minute.
For a fair comparison, we use the same switching speed and
control loop period for both reactive and smart schemes. In
order to make the energy characteristics comparable, we apply the daylight weighting scheme described in Section 3.4
so that both the smart and the reactive schemes produce the
same average comfort levels.
We also compare our system with an optimal algorithm
that provides the theoretical upper bound on energy saving
and user comfort. We assume that the optimal scheme knows
the daylight levels at all times, and that there is no control
delay in the window transparency change and electric lighting switch. This implies no lighting errors in the optimal
scheme and no loss in user satisfaction. In the optimal algorithm, daylight harvesting is always the first choice to maintain the lighting setpoint. The electric light will only be used
to compensate the offset whenever the harvested daylight is
not enough. Thus, no algorithm could achieve higher energy
saving and better user comfort than the optimal algorithm.
4.2
Evaluation Metrics
We evaluate the trade-off between energy efficiency and
user comfort in the experimental results with two quantita-
tive metrics: energy saving and comfort loss. Energy saving
is defined as the percentage of savings by the scheme over
the scheme that purely relies on artificial lighting to maintain
the setpoint at the desk level. Comfort loss is defined as the
root mean square error (RMSE) of the actual light intensity
at the desk over the user desirable setpoint.
4.3
Results
We evaluate our smart system against the baseline and optimal algorithms in a trace-based simulator that employs the
empirical data traces from the building testbed. The simulator allows the control schemes to adjust window transparency and electric lighting. In the experiments, we explore
the range from 1%/min to 10%/min that matches the switching speeds in the state-of-art electrochromic windows [8].
We use 15 min as control loop period, 500 lux as the setpoint
at the desk and 25% (±125 lux) as the user tolerable range.
Figure 6(a) shows the results of energy savings of the windows around the building testbed. The smart scheme outperforms the reactive scheme at all switching speeds. On average, our system saves more than 10% energy than the reactive algorithm. As the switching speed increases, the smart
scheme approaches the optimal performance faster than the
reactive scheme. Thus, the smart scheme can reduce more
energy consumption in a wide range of devices that have different switching speeds.
Figure 6(b) shows the comfort loss of the same three
schemes in multiple switching speeds. The results indicate
that our automatic configuration of daylight weight works
well as both smart and reactive schemes satisfy the user error limit within 125 lux. In general, the smart scheme saves
more energy than the reactive scheme at the same comfort
level. This provides a clear insight on the system performance in terms of the trade-off between energy efficiency
and user comfort.
4.4
Analysis
We evaluate the accuracy with which our system infers
the window orientations at different locations of the testbed.
As ground truth, we measure the actual window orientations
at each location with an electronic compass whose reading
accuracy is ±2°. Table 1 summarizes the results of window
Location
Conference Room (E)
Hallway-1 (S)
Cafeteria (W)
Hallway-2 (N)
Inferred
110.80
196.74
269.93
9.61
Measured
102.00
192.00
282.00
12.00
Error
+8.80
-4.74
-12.07
-2.39
Table 1. Window Orientation Inference Results
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5
Conclusions
In this paper, we present the concept of smart daylight
harvesting that senses incoming daylight in a building in order to achieve stable task lighting intensity despite the instable natural light source. This system uses a combination
of automatic modeling and daylight prediction techniques
to predictively control the window transparency. We evaluate our scheme against the baseline and optimal algorithms
in a trace-based simulator that employs the empirical data
from an office building. Experimental results show that our
scheme can provide larger energy savings without sacrificing
user comfort than existing baseline solution.
6
Acknowledgements
This material is based upon work supported by the National Science Foundation under Grant No. 1038271.
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