Proposal of load reduction method of the control computer in the cloud-type Intelligent Lighting System

Transcription

1 224 Int'l Conf. Artificial Intelligence ICAI'16 Proposal of load reduction method of the control computer in the cloud-type Intelligent Lighting System Shinya DAINAKA 2, Mitsunori MIKI 1, Sota NAKAHARA 2, Katsuya ITO 2, and Hiroto AIDA 1 1 Department of Science and Engineering, Doshisha University, Kyoto, Japan 2 Graduate School of Science and Engineering, Doshisha University, Kyoto, Japan Abstract Authors are engaged in research and development of the Intelligent Lighting System, which makes improving the comfort of workers at offices compatible with reducing power consumption. As the Intelligent Lighting System was found to be effective as a result of demonstration tests, it is expected to be introduced into large-scale offices. We are thus examining a cloud-type Intelligent Lighting System which controls multiple areas by a single control computer. This study thus verifies the impact of an increase in controlled areas under a cloud-type Intelligent Lighting System on the control computer. It also proposes a method to reduce a load on the control computer. Keywords: Office Intelligent Lighting System, Cloud, illuminance sensor 1. Introduction In recent years, there has been a rise in attention to approaches for improving the intellectual productivity, creativity and comfort of office workers [1]. A study by Boyce et al. have revealed that providing the brightness (illuminance) optimized for the work of each worker is effective from the viewpoint of improving the lighting environment[2]. Against this backdrop, the authors have undertaken studies on Intelligent Lighting Systems aimed at improving worker comfort in offices and reducing power consumption by lightings[3],[4]. An Intelligent Lighting System realizes the illuminance level requested by each worker (target illuminance) at the relevant illuminance sensor position with a minimum power consumption. An office with an Intelligent Lighting System is expected to allow workers to work in lighting environments customized for each of them, which will improve their comfort and reduce their stress. Moreover, providing the necessary levels of luminance at areas in need can lower the average illuminance in the whole room, which will result in a significant reduction of power consumption. As these advantages of Intelligent Lighting Systems are recognized, verification experiments have been underway at several offices in Tokyo, which have successfully realized required illuminance levels at the points where they are required, realizing high energy efficiency[5]. Since the control method used in the current demonstration tests requires one control computer per area, as the number of areas increases, the number of control computers increases. Therefore, it had an issue that the initial cost and the cost for maintenance and management increase accordingly. As a solution to this issue, we are examining a cloudtype Intelligent Lighting System which controls multiple areas by a single control computer. No verification has been made, however, on a load on the control computer when it controls multiple areas by using the control algorithm of the current Intelligent Lighting System. This study thus verifies the impact of an increase in controlled areas under a cloudtype Intelligent Lighting System on the control computer. It then proposes a method to reduce a load on the control computer of a cloud-type Intelligent Lighting System in a large-scale environment on the basis of the verification result. 2. Intelligent Lighting System An Intelligent Lighting System realizes an illuminace level desired by the user while minimizing energy consumption by changing the luminous intensity of lightings. The Intelligent Lighting System, as indicated in Fig.1, is composed of lighting fixtures equipped with lighting control device, illuminance sensors, and electrical power meters, with each element connected via a network. Fig. 1: Configuration of Intelligent Lighting System The lighting control device evaluates the effectiveness of the current lighting pattern based on the illuminance data from illuminance sensors and electrical power data from a power meter. By repeating microscopic lighting pattern

2 Int'l Conf. Artificial Intelligence ICAI' variations and effectiveness evaluations, the control system tries to minimize power consumption while satisfying the illuminance conditions required by each worker. Authors have conducted demonstration tests using various control algorithms of the Intelligent Lighting System. Control algorithms of the System can be roughly classified into two types. The first type of algorithms are a ANA/RC (Adaptive Neighborhood Algorithm using Regression Coefficient) based on Simulated Annealing (SA)[3],[6] SA is a generalpurpose local search method in which an approximate solution within a range near the current solution is generated and the approximate solution is accepted if the objective function improves. Taking the luminance of the lighting fixture as design variable, it randomly varies the luminance of each lighting fixture in each search to an extent unnoticeable by workers to search an optimum lighting pattern. By repeating lighting control attempts of about a second 30 to 100 times, an Intelligent Lighting system realizes the target illuminance level requested by each worker. The second type of algorithms are lighting control algorithms that use a method which can quickly realize the target illuminance for each worker (hereinafter referred to as the "simulation- based method") [7]. Since desks are infrequently moved in an ordinary office, it is possible to measure an illuminance/luminance effect in advance in an environment into which the Intelligent Lighting System is to be introduced by turning on and off its lighting fixtures one by one. By using that illuminance/luminance influence factor, a precise simulation environment can be constructed on the control computer. As a lighting pattern that satisfies illuminance required by each worker is rapidly derived in a simulation environment and applied to a real environment, the target illuminance for each worker is realized quickly. We have measured the illuminance/luminance influence factor before introducing the Intelligent Lighting System. For this reason, we use the simulation-based method, which can quickly realize the target illuminance for each worker, as a control algorithm in this study. As the largest-scale office building environment, authors assume one composed of several floors, each of which has 1,000 lamps and 500 illuminance sensors that are separated into multiple areas. An area here refers to a partitioned space or room. Under the Intelligent Lighting System currently used in demonstration tests, only one area is controlled by a single control computer. For this reason, introducing the Intelligent Lighting System in the present configuration into a largescale office results in an increase in the initial introduction cost and the cost for operation and maintenance. It is thus intended to solve these problems by using a cloud-type Intelligent Lighting System which controls multiple areas by a single control computer. Fig. 2: Figure of constitution of the Cloud-Type Intelligent Lighting System 3. Cloud-Type Intelligent Lighting System 3.1 Overview of the Cloud-Type Intelligent Lighting System A cloud-type Intelligent Lighting System is a system that controls multiple areas by one control computer. Fig.2 shows the configuration of the cloud type intellectual lighting system. Floor management communication instrument in Fig.2 is a device that obtains illuminance values from illuminance sensors and performs the light control of lamps upon request by the control computer. This floor management communication instrument is placed on each floor. The control computer in Fig.2 obtains illuminance values from illuminance sensors installed in each area from the floor management communication instrument and execute processing for determining luminance for each lamp to satisfy the target illuminance set by each worker. This processing is called the next luminance determination processing and starts up a process that determines the next luminance for each area. After the next luminance is determined, the next luminance for each lamp is transmitted to a floor management communication instrument. A floor management communication instrument receiving the next luminance dims lamps. The interval between the reception of illuminance values from a floor management communication instrument by the control computer and the transmission of the next luminance to the instrument is set to 60 seconds as many areas are controlled simultaneously. 3.2 Control of Cloud-Type Intelligent Lighting System It show a flow of the control in the control computer of the cloud type intellectual lighting system. 1) Set initialization parameters (initial luminance, target illuminance, illuminance/luminance influence factor, etc.).

3 226 Int'l Conf. Artificial Intelligence ICAI'16 2) Transmit luminance data for each lamp to floor management communication instruments to turn on each lamp. 3) Request a floor management communication instrument to obtain illuminance values to obtain an illuminance value from each illuminance sensor. 4) Calculate the value of the objective function at the current luminance. 5) Determine an appropriate range (neighborhood) within which the next luminance is generated in accordance with the regression coefficient of each lamp. 6) Randomly generate the next luminance within the neighborhood determined in (5) for each lamp to determine the next luminance. 7) Estimate the illuminance value of each illuminance sensor by using the illuminance/luminance influence factor. 8) Calculate the value of the objective function at the luminance determined in (6). 9) If the value of the objective function calculated in (8) is worse than that calculated in (4), reject the next luminance and revert to the previous luminance. 10) Repeat steps (4) through (9) for the specified times and send the next luminance at the end of the repetition to the floor management communication instrument and turn on lamps. 11) Wait for a specified period of time and return to (3). In order to curb the maximum CPU utilization rate, steps (3) through (10) are executed for each floor instead of controlling all floors simultaneously. 3.3 Study parameter of Control of Cloud-Type Intelligent Lighting System The current cloud-type Intelligent Lighting System starts a process for determining the next luminance severally for each area. Therefore, if the number of areas subject to control increases, the number of processes increases, which is considered to increase the load on the control computer. We thus verify the impact of an increase in the number of areas on the control computer. 4. Verification of a Load on the Control Computer Caused by an Increase in the Number of Areas 4.1 Verification environment We verify a load on the control computer brought about by an increase in the number of areas in a cloud-type Intelligent Lighting System. As an area in the environment used for verification, we assumed the environment of Ecozzeria in Shin-Marunouchi Building, where a demonstration test using the simulation-based method was conducted. Fig.3 shows a Light Fixture Illuminance sensor Fig. 3: Figure of placement of the illumination sensor and lighting fixture of Ecozzeria figure of placement of the illumination sensor and lighting fixture of Ecozzeria. Authors assumed the introduction of the system into an office building having several floors each of which has 40 areas identical to Ecozzeria shown in Fig. 3 (960 lamps and 520 illuminance sensors). Verification was carried out by increasing the number of floors until the maximum CPU utilization rate reached 100 to make stable control no longer possible. Fig. 4 shows the assumed verification environment. Table.1 shows the details of control computer and floor management communication which I used by this inspection Hub Floor management communication equipment 1..8 Hub Floor management communication equipment Gateway Fig. 4: Inspection environment Control device Table 1: Detail of the control computer Control computer Floor management communication instrument CPU Intel Core 2 Duo (2.80 Ghz) ARM1176JZF-S (770 Mhz) Memory 2GB 512MB 4.2 Load inspection of the control computer and consideration We verified an additional load on the control computer when the number of areas controlled is increased. Assuming a work day at a real office, control was performed for

4 Int'l Conf. Artificial Intelligence ICAI' hours in this verification. In addition, as the result of demonstration tests confirmed that the frequency of changes in target illuminance is small, it was assumed that the target illuminance is changed twice in one area. It is also assumed that workers come to the office in the first two hours of the simulation period. Table.2 shows an inspection result Assuming the environment same as the one assumed in Chapter 4, load verification was performed using the process suspension method. Fig. 5 shows the maximum CPU utilization rate, the maximum memory usage rate, and the maximum swap area usage rate when the method was used. Table 2: Load of the control computer one floor two floor maximum CPU utilization rate maximum memory usage rate maximum swap area usage rate Table 2 shows the maximum CPU utilization rate, the maximum memory usage rate, and the maximum swap area usage rate. Table 2 shows that the maximum CPU utilization rate, the maximum memory usage rate, and the maximum swap area usage rate increase with an increase in the number of floors. When two floors were simultaneously controlled, the maximum CPU utilization rate reached 100 and made stable control difficult. As the next luminance determination is processed severally for each floor, it does not raise the CPU utilization rate significantly. It is considered as the reason for a large increase in the CPU utilization rate that the maximum memory usage rate reached the limit to cause the use of the swap area, resulting in the use of CPU resources for swap processing (slashing). As a result of this verification, the number of stably controllable floor is up to one. This study thus proposes a method to reduce the memory usage rate and the CPU utilization rate of the control computer in order to increase the number of simultaneously stably controllable areas. For comparison with the proposed method, the control method used in this verification is called the standard method. 5. Composed of Control of Cloud-Type Intelligent Lighting System using the process suspension method and inspection A cloud-type Intelligent Lighting System using the standard method intelligently controls lighting in each floor severally. In processing the next luminance determination for each floor, the process goes into sleep after completing the next luminance determination so that the light control interval may be 60 seconds. This chapter proposes a process suspension method which suspends the process while it is in a sleep mode to reduce a load on the control computer. This method can increase the number of simultaneously controllable areas by releasing memory allocated even during a sleep period after the completion of the next luminance determination processing under the standard method to reduce a memory load. Max use rate CPU Memory Swap space Number of floors Fig. 5: Load of the control computer when using the process suspension method Fig. 5 confirms that up to 6 floors can be stably controlled by using the process suspension method. Based on this result, it was confirmed that the use of the process suspension method is effective for reducing the memory usage rate. It was also confirmed that there was no large change in the maximum memory usage rate when the number of floor is from 1 to 6. This is because the next luminance determination is processed at staggered timing for each floor, as a result of which processes for floors which are not light controlled are suspended. On the other hand, it was confirmed that the maximum memory usage rate increased when 7 floors are controlled. In this verification, the light controlling interval is set to 60 seconds. While it was possible to control 6 floors in 60 seconds, when 7 floors were controlled, the next luminance determination was simultaneously processed for 2 floors. For light control was performed severally for each floor in 60 seconds, which are not sufficient to cover 7 floors. This is considered to be the reason for the said increase in the maximum memory usage rate. Consequently, when 7 floors were controlled, the maximum CPU utilization rate reached 100 to make stable control difficult. This is considered to be because the memory usage rate reached the limit to cause swap processing. It takes about 10 seconds after the control of 1 floor starts until the control of the next floor starts. Since the light controlling interval is 60 seconds in this verification, if 7 floors are controlled simultaneously, the control computer must start processes for 2 floors simultaneously. Therefore, the maximum memory usage rate increased significantly when 7 floors were controlled.

5 228 Int'l Conf. Artificial Intelligence ICAI'16 6. Composed of Control of Cloud-Type Intelligent Lighting System using the communication blocking method and inspection A cloud-type Intelligent Lighting System using the standard method continues to control lighting intelligently after the illuminance value of every illuminance sensor in the area converges to the target illuminance in order to respond to external light from a window or a change in the target illuminance by a worker. External light, however, does not cause a sharp change in illuminance in a short time, and it has been found by multiple demonstration tests that workers seldom change the target illuminance. Therefore, it is considered to pose no problem if the intelligent lighting control is suspended during the time when there is neither any large change in illuminance values by external light nor a change in the occupancy status of a seat or the target illuminance by any worker. Therefore, we propose a communication blocking method which suspend intelligent lighting control and block communication with the control computer after the illuminance of every illuminance sensor in the area converges. Under the communication blocking method, if a sharp change in illuminance values is indicated by an illuminance sensor in an area or if a worker changes the occupancy status of any seat or the target illuminance, a request to resume control is sent to the control computer to resume intelligent lighting control. This methed reduces the memory load of the control computer. The thresholds for determining whether illuminance has converged or not are set at 3 above and below the current illuminance taken as the standard. If illuminance values shown in the past 10 illuminance logs retrieved from each illuminance sensor are between those thresholds, illuminance is determined to have converged and communication is blocked. Assuming the environment same as the one assumed in Chapter 4, load verification was performed using the process suspension method. Fig. 6 shows the maximum CPU utilization rate, the maximum memory usage rate, and the maximum swap area usage rate when the method was used. Fig. 6 confirms that up to 5 floors can be stably controlled by using the communication blocking method. Stable control became difficult when 6 floors were controlled, which is considered to be caused in the following manner. As there were many areas simultaneously communicating with the control computer, the memory usage rate reached the limit to cause swap processing, which in turn caused the CPU utilization rate to reach the limit. Based on this result, it was found that, whereas only up to one floor can be stably controlled under the standard method, up to 5 floors can be stably controlled by using the communication blocking Max use rate CPU Memory Swap space Number of floors Fig. 6: Load of the control computer when using the communication blocking method method, increasing the number of stably controllable floors by 4. These results confirmed that the use of the communication blocking method is useful for reducing the load on the control computer. 7. Composed of Control of Cloud-Type Intelligent Lighting System using combined method and inspection Methods proposed in Chapters 5 and 6 reduced a load on the control computer and increased the number of areas controllable with limited computational resources. It is considered possible to control more areas with limited computational resources by combining these two methods. Assuming the environment same as the one assumed in Chapter 4, load verification was performed using the process suspension method. Fig. 7 shows the maximum CPU utilization rate, the maximum memory usage rate, and the maximum swap area usage rate when the method was used. Max use rate CPU Memory Number of floors Swap space Fig. 7: Load of the control computer when using combined method Fig. 7 confirms that up to 7 floors can be stably controlled by using the combined method. When 8 floors were

6 Int'l Conf. Artificial Intelligence ICAI' controlled, the CPU utilization rate reached the limit and made stable control difficult. The CPU utilization rate rose due to starting up processes and blocking or resuming communication. This is considered to be because, as the number of areas controlled increases, a CPU load becomes greater because of an increase in processing of the next luminance determination and process start-up. These results confirmed that the use of the combined method is useful for reducing the load on the control computer. 8. Conclusion This study thus verifies the impact of an increase in controlled areas under a cloud-type Intelligent Lighting System on the control computer. It also proposes a method to reduce a load on the control computer. The process suspension method which suspends the process while it is in a sleep mode to reduce a load on the control computer. It was found that, whereas only up to one floor can be stably controlled under the standard method, up to 6 floors can be stably controlled by using the process suspension method, increasing the number of stably controllable floors by 5. The communication blocking method which suspend intelligent lighting control and block communication with the control computer after the illuminance of every illuminance sensor in the area converges. It was found that, whereas only up to one floor can be stably controlled under the standard method, up to 5 floors can be stably controlled by using the process suspension method, increasing the number of stably controllable floors by 4. Up to 7 floors can be stably controlled by using the combined method. References [1] Olli Seppanen, William J. Fisk, A Model to Estimate the Cost- Effectiveness of Improving Office Work through Indoor Environmental Control, Proceedings of ASHRAE, 2005 [2] Peter R. Boyce Neil H. Eklund S. Noel Simpson, Individual Lighting Control: Task Performance, Mood, and Illuminance, Joyrnal of the Illuminating Engineering Society, pp , Winter 2000 [3] M.Miki, T.Hiroyasu and K.Imazato, Proposal for an intelligent lighting system, and verification of control method effectiveness, Proc. IEEE CIS, 1, (2004). [4] M.Miki, K.Imazato and M.Yonezawa, Intelligent lighting control using correlation coefficient between luminance anc illuminance, Proc.IASTED Intelligent Systems and Control, 497[078], (2005). [5] Fumiya Kaku, Mitsunori Miki, Tomoyuki Hiroyasu, Masato Yoshimi, Shingo Tanaka, Takeshi Nishida, Naoto Kida, Masatoshi Akita, Junichi Tanisawa, Tatsuo Nishimoto, Construction of intelligent lighting system providing desired illuminance distributions in actual office environment, Artifical Intelligence and Soft Computing, vol. 6114, pp , [6] S.Tanaka, M.Miki, T.Hiroyasu, M.Yoshikata, An Evolutional Optimization Algorithm to Provide Individual Illuminance in Workplaces, Proc IEEE Int Conf Syst Man Cybern, 2, (2009). [7] Shohei MATSUSHITA, Sho KUWAJIMA, Mitsunori MIKI, Hisanori IKEGAMI and Hiroto AIDA, Reducing the Number of Times Lighting Control is required to reach Illuminance Convergence in the Intelligent Lighting System, The 2014 International Conference on Wireless Networks(ICWN2014)