Issue 
Sust. Build.
Volume 7, 2024



Article Number  1  
Number of page(s)  8  
Section  Advanced Daylight Systems and Lighting Performance  
DOI  https://doi.org/10.1051/sbuild/2024001  
Published online  22 April 2024 
Original Article
Research on energysaving lighting control of highrise building by the PID control algorithm
School of Architecture Engineering, Xuzhou College of Industrial Technology, Xuzhou, Jiangsu 221000, China
^{*} email: yuezxuan@outlook.com
Received:
25
December
2023
Accepted:
21
March
2024
The lighting of highrise buildings consumes a significant amount of electricity, making it essential to implement energysaving measures. In this paper, the lighting of highrise buildings was briefly analyzed, followed by a description of the proportion, integration, and differentiation (PID) control algorithm. To improve the efficiency of lighting control for energy conservation, the fuzzy PID control algorithm was analyzed. The selftuning of parameters was achieved by utilizing the whale optimization algorithm (WOA) to develop a WOAfuzzy PID control algorithm. Finally, experimental analysis was carried out. The simulation findings showed that the WOAfuzzy PID algorithm had the shortest stabilization time (6.77 s), the smallest maximum overshoot (3.12%), and better antiinterference capability compared to the PID and fuzzy PID algorithms. Finally, it was found from practical application that the use of the WOAoptimized algorithm resulted in a 43.7% reduction in monthly electricity consumption. The findings suggest the effectiveness of the WOAfuzzy PID algorithm in energyefficient lighting control and its applicability to realworld highrise buildings.
Key words: Highrise building / lighting control / LED lamp / energysaving / PID control algorithm
© Q. Yang and Z. Yue, Published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
1 Introduction
With the accelerated progressing in both economy and society, the issue of energy saving is becoming prominent [1]. There is a significant amount of energy waste in different fields. Therefore, how to save energy and how to maximize the energy utilization rate is always a challenge [2]. Due to the demand of housing for social development, the real estate industry continues to expand, and the quantity of highrise buildings is experiencing an increase [3]. In the overall energy usage of society, a significant portion is attributed to building energy consumption [4], and lighting energy consumption is one of its main components [5]. Most of the current buildings employ manual lighting control, leading to unnecessary high light level and considerable energy inefficiency. If realtime adjustment of lamp light output is implemented based on task illuminance, energy could be effectively saved.
Energysaving control of the lighting system has been widely studied. Sun et al. [6] put forward an indoor lighting control approach based on a framework to control lamps and window blinds. They demonstrated the intelligence of the method through computer simulation experiments. Futagami et al. [7] introduced a progressive lighting control approach using motion sensors, which can dim the light levels of the fixtures above the occupants in light of daylight availability for daylight harvesting. They evaluated the potential of that approach for reducing energy use while enhancing occupants' comfort. Farkas et al. [8] investigated the energysaving control of outdoor lighting systems based on power line communication (PLC) technology to achieve energy savings and reduce operation and maintenance costs. Cesari et al. [9] analyzed the influence of the window size and glazing type in buildings on electric lighting use. They found that using broader windows with suitable glazing, along with a specific dimming strategy of the electric lighting, resulted in a significant reduction of approximately 17% in primary energy consumption.
In this paper, a lighting control approach based on an improved proportion, integration, and differentiation (PID) control algorithm was designed for the energyefficient lighting of highrise buildings. This approach integrates the traditional PID control algorithm with fuzzy logic to fuzzify the parameters and utilizes the whale optimization algorithm (WOA) for selftuning of these parameters, thereby further improving the control performance. The effectiveness of the approach in energy saving was proven by experimental analysis. This paper provides a novel approach for lighting control in highrise buildings and offers some theoretical support for implementing the PID control algorithm in other lighting systems.
2 Materials and methods
2.1 Analysis of lighting in highrise building
In the current highrise building, the traditional lighting control method, mostly manual control, is predominantly used. Under manual control, the lighting equipment is regularly switched on or off, and the brightness is kept consistent. In this case, users often forget to turn off the lights. In addition, in the case of good natural light conditions, it becomes impossible to adjust the brightness, resulting in a significant waste of energy. The most crucial aspect of energysaving lighting control is to regulate light brightness based on realtime illuminance measured by the light sensor to minimize lighting energy consumption. Compared with traditional lighting control, energysaving lighting control offers the following advantages.
Energysaving: Lighting brightness is adjusted according to the changes in natural light to achieve energy savings.
Prolonging the lifespan of lamps: The lifespan of lamps is linked to the length of time they are illuminated. By implementing energysaving lighting controls, the duration of lighting can be shortened, thereby extending the lifespan of the lamps.
At present, various algorithms are commonly used for controlling energysaving lighting, including classical control algorithms (such as PID), reinforcement learning methods, neural network methods, and others [10]. This paper mainly studied the PID control algorithm. Taking a highrise office building project in Jiangsu Province as an example, the building is 92 m meters tall, comprising 19 floors, including three podium floors and three underground floors, and its energysaving lighting control was analyzed.
First of all, according to the “Standard for Lighting Design of Buildings” (GB500342013), the lighting standard requirements for highrise office buildings are presented in Table 1.
According to the actual situation, the most prevalent room in this highrise office building was the general office; therefore, this paper primarily focuses on studying the energysaving lighting control algorithm for general offices. When selecting lamps, the commonly used lighting sources are listed in Table 2.
In Table 2, LED lamps exhibit significantly superior luminous efficacy compared to incandescent and fluorescent lamps, as well as high color temperature and color rendering index. Additionally, their service life is sufficient for practical applications. Therefore, this paper used LED lamps as the lighting source for the highrise office building. The dimming methods of LED lamps are as follows.
Segmented switch dimming: LED lamps are divided into different groups. By turning on and off different groups of LED lamps, all on, partial on, and all off are realized.
Analog linear dimming: By adjusting the resistance of the adjustable resistor, the operating current of LED lamps is altered.
Pulsewidth modulation (PWM) dimming: Dimming is achieved by adjusting the average output current.
PWM dimming offers the advantages of swift response and exceptional dimming accuracy [11]. Therefore, PWM dimming technology was used in this study, and the specific scheme is shown in Figure 1. The actual illuminance of the office under natural light was obtained using the illumination sensor and compared with the preset value. The LED dimming was achieved by utilizing the PID control algorithm to generate PWM dimming signals.
Standard values for office building lighting.
Comparison of incandescent, fluorescent, highpressure sodium, and LED lamps
Fig. 1 Energy saving lighting control scheme based on PWM dimming and PID control. 
2.2 PID control algorithm
The PID algorithm is a very common algorithm [12] with simple principle and good control effect, which has been widely employed in the industrial field [13]. Assuming that the preset value of the system is r(t) and the actual output value of the controlled object is y(t). The deviation is:
The output of PID is:
$$u(t)={K}_{p}\left[e(t)+\frac{1}{{T}_{1}}\int e(t)+{T}_{D}\frac{de(t)}{dt}\right],$$(1)
where K_{p} stands for the proportionality coefficient, T_{I} stands for the integration time constant, and T_{D} represents the differential time constant.
The transfer function of PID is expressed as:
$$G(s)={K}_{p}+\frac{{K}_{I}}{s}+{K}_{D}s,$$(2)
where K_{I} stands for the integral coefficient, K_{I}K_{I} = K_{p}/T_{I}, and K_{D} denotes the differential coefficient, K_{D} = K_{p}/T_{D}.
When PID is applied to digital control signal, it first needs to be discretized to obtain digital PID. In this paper, incremental PID [14] was used, and the PID after discretization at time k is written as:
$$u(k)={K}_{p}\times e(k)+{K}_{i}T{\sum}_{j=0}^{k}e(j)+{K}_{d}\frac{e(k)e(k+1)}{T}.$$(3)
The incremental PID control algorithm can be obtained by u(k) − u(k − 1):
$$\Delta u(k)={K}_{p}\times [e(k)e(k1)]+{K}_{i}{T}_{e}(k)+\frac{{K}_{d}[e(k)2e(k1)+e(k2)]}{T},$$(4)
where e(k), e(k − 1), and e(k – 2) refer to the system deviation value at time k, k–1, and k – 2.
2.2.1 Fuzzy PID control algorithm
The PID control algorithm requires parameter reconfiguration for each tuning session, lacking automatic adjustment capability, which can impact the overall effectiveness of energysaving lighting control. Therefore, expert experience is incorporated into fuzzy reasoning to achieve the adjustment of PID parameters, resulting in the fuzzy PID control algorithm [15].
Compared with PID, fuzzy PID fuzzifies all parameters in PID by fuzzy method and takes deviation e between the actual illuminance and the preset illuminance obtained by the lighting sensor and deviation change rate ec as the input. According to the standard value of illuminance in ordinary offices, the fuzzy domain of e is set as [–300,300], and its basic domain is [–3,3]; the fuzzy domain of ec is [–100,100], and its basic domain is [–3,3]. Seven fuzzy subsets are used to represent the input and output parameters of PID:
$$\left\{NB,NM,NS,ZO,PS,PM,PB\right\},$$
which are corresponding to negative large, negative medium, negative small, zero, positive small, positive medium, and positive large.
Given the extensive computational requirements, the input and output of PID use triangular membership function [16], which has good resolution and high sensitivity. Then, in terms of fuzzy rules, considering the stability and overshoot of the system, it is appropriate to increase K_{p} and K_{d} and decrease K_{i} in the initial stage, and then decrease K_{p} and K_{d} and increase K_{i} in the middle and late stages to obtain better steadystate accuracy. Finally, the fuzzy rules for energysaving lighting control are shown in Table 3.
The output obtained after fuzzy reasoning needs to be defuzzified. In this paper, the centroid approach was used:
$${v}_{o}=\frac{\int {\mu}_{j}\cdot {\mu}_{B}\left({\mu}_{j}\right)dv}{\int {\mu}_{B}\left({\mu}_{j}\right)dv},$$(5)
where μ_{B}(μj) is the membership degree when the input is μ_{j}.
After defuzzification, the actual parameters of PID can be obtained:
$$\{\begin{array}{c}\hfill {K}_{p}={K}_{p0}+\Delta {K}_{p}\hfill \\ \hfill {K}_{i}={K}_{i0}+\Delta {K}_{i}\hfill \\ \hfill {K}_{d}={K}_{d0}+\Delta {K}_{d}\hfill \end{array}$$(6)
where K_{p}_{o}, K_{i}_{o}, and K_{d}_{o} represent a set of initial PID parameters determined by trial and error.
Fuzzy rules of K_{p}, K_{i}, and K_{d}
2.2.2 Whale optimization algorithm
In fuzzy PID control, the proportionality factor and quantization factor are generally determined based on experience and may need to be readjusted when the operating conditions change. In order to achieve selftuning of parameters, this paper introduced the WOA [17] to improve the fuzzy PID control algorithm and develop the WOAfuzzy PID control algorithm.
First, the location of the whole population is initialized:
$${X}_{i}=lb+rand\times \left(ublb\right),$$(7)
where ub and lb indicate the upper and lower boundaries of of the search space and r and indicates a random numerical value between 0 and 1.
WOA uses three steps to hunt prey, that is, to find the optimal solution, as follows.
(1) Hunting
Let the best whale position be X^{*}(t) and the position of the other whales be X_{i}(t). Then, the other whales move closer to the best position, which can be written as:
$$D=\leftC\cdot {X}^{*}\left(t\right){X}_{i}\left(t\right)\right,$$(8)
$${X}_{i}\left(t+1\right)={X}^{*}\left(t\right)A\cdot D,$$(9)
$$a=2\times \left(1\frac{t}{T}\right),$$(12)
where X_{i}(t + 1) stands for the search location of the ith whale at the t + 1th time of iteration, D is the distance between the current position and the best position, A and C are random parameters, r is a random number in [0,1], t and T are the current and maximum count of iterations.
(2) Bubble net attack
Whales engage in bubble predation using including contraction encircling and spiral encircling. The probability of whales choosing either of these two methods is 50%. The details of the two methods are as follows:
① contraction encircling: similar to hunting, and $a=2\frac{2t}{T}$;
② spiral encircling: X_{i} (t + 1) = X^{*} (t) − X_{i} (t) e^{bl} ⋅ cos(2πl) + X^{*} (t),
where b indicates logarithmic spiral shape constant and l is a random number in [–1,1].
(3) Random search
When individuals are far away from the best position, they will use random search to get closer to enhance the capability of global search. The formulas are written as:
$${X}_{i}\left(t+1\right)=Xrand\left(t\right)A\cdot D,$$(13)
$$D=\leftC\cdot Xrand\left(t\right){X}_{i}\left(t\right)\right,$$(14)
where Xr and (t) is the current random individual position and D stands for the distance between the current random individual and the best position.
Figure 2 shows the process of obtaining the optimal fuzzy PID parameters using the WOA.
Figure 3 illustrates the flowchart of the WOAfuzzy PID control algorithm.
The illuminance of the general office is preset to be 300 lx, and deviation e and deviation change rate ec are obtained by comparing it with the actual illuminance of the light sensor.
The WOA population is initialized, and the fitness is calculated: ${f}_{ITAE}={{\displaystyle \int}}_{0}^{\infty}t\lefte\left(t\right)\right{d}_{t}$, where f_{ITAE} stands for integral time absolute error [18].
Whether the termination condition is satisfied is determined. If the condition for termination is satisfied, the optimal fuzzy PID parameters are output. If not, the next step is initiated.
The position of the whale is continuously updated using the WOA, and the fitness value is calculated until the optimal parameters are found.
The PWM dimming signal outputted by the PID controller is transmitted to the LED lamp to achieve dimming.
(3) Experiments and results
Firstly, the optimization performance of the WOA was analyzed. It was compared with the particle swarm optimization (PSO) and grey wolf optimization (GWO) algorithms using five unimodal benchmark functions. Table 4 shows these functions.
Each algorithm was repeated 30 times independently. The optimal values obtained by these algorithms are shown in Table 5.
It can be found from Table 5 that the WOA obtained the optimal values when optimizing f_{1 } f_{4}, suggesting significantly better performance compared to the PSO and GWO algorithms. The results revealed that the WOA had better convergence precision and speed, as well as a robust optimization capacity. Therefore, the selection of the WOA for fuzzy PID parameter optimization was dependable.
To assess the usability of the proposed approach for energysaving lighting control in general offices, PID, fuzzy PID, and WOAfuzzy PID control models were developed using the Simulink model in MATLAB. The simulation results are illustrated in Figure 4.
Based on Figure 4, the performance of different models was compared in Table 6.
Combining Figure 4 and Table 6, it can be found that the PID model required the longest stabilization time, tending to stabilize after 12.37 s. In contrast, the fuzzy PID model had a stabilization time of 8.62 s, which was 30.32% shorter than that of the PID model. After optimization by the WOA, the WOAfuzzy PID model achieved the shortest stabilization time of 6.77 s. This time was 45.27% shorter than that of the PID model and 21.35% shorter than the fuzzy PID model. These results suggested that the WOAfuzzy PID model had a good response speed.
The maximum overshoot refers to the maximum deviation from the target value that a system experiences after reaching the target value, expressed as a percentage. The comparison of the maximum overshoots indicated that the maximum overshoot of the PID model was the highest, which was 13.87%, followed by the fuzzy PID model at 11.24%. The maximum overshoot of the WOAfuzzy PID model was the smallest, at only 3.12%, which was 10.75% lower than that of the PID model and 8.12% lower than that of the fuzzy PID model. Through comparison, it can be found that under the same conditions, the WOAfuzzy PID model had a shorter stabilization time and lower overshoot, suggesting a superior control effect.
To determine the interference effect of the model, an interference signal was added at t = 20 s, and the simulation results are presented in Figure 5.
From Figure 5, it can be found that after adding the interference signal, the system under PID control exhibited two oscillations, indicating poor antiinterference ability, while the systems under the fuzzy PID and WOAfuzzy PID models showed only one oscillation. In comparison, the time required for the WOA fuzzy PID model to restore the steady state was shorter, indicating that its antiinterference ability was better than that of the fuzzy PID model. These results demonstrate the reliability of enhancing the fuzzy PID model by incorporating WOA.
Ten ordinary offices with similar natural light levels in the highrise building under investigation were chosen for the experiment. Each office had the same area, and an equal number of LED lamps were installed in identical positions in these offices. Five of them were used as Group A for traditional lighting control, where the lamps were controlled by a switch, and the brightness was not adjustable. Additionally, five rooms were designated as Group B for energysaving lighting control, utilizing the WOAfuzzy PID algorithm. The test period lasted from 8:00 to 17:00 and ran continuously for one month. The results are displayed in Table 7.
Table 7 shows that under the traditional lighting control, the average indoor illuminance was consistently high at 500 lx, indicating excessive lighting, and the monthly electricity consumption reached 40.34 kWh. Under the WOAfuzzy PID algorithm, the average indoor illuminance was 312 lx, meeting the requirements of GB500342013. The monthly electricity consumption was 22.71 kWh, which was 43.7% less than that of Group A. These results demonstrate the effectiveness of the WOAfuzzy PID algorithm in energysaving lighting control.
Fig. 2 The flowchart of optimizing the fuzzy PID parameter by the WOA. 
Fig. 3 The flowchart of the energysaving illumination control based on the WOAfuzzy PID control algorithm. 
Test functions for algorithm optimization performance.
Optimization results of the PSO algorithm, GWO algorithm, and WOA for five functions.
Fig. 4 Simulation results of the PID, fuzzy PID, and WOAfuzzy PID models for energysaving lighting control 
Comparison of stabilization time and maximum overshoot between different models.
Fig. 5 Antijamming simulation results of the PID, fuzzy PID, and WOAfuzzy PID models. 
Comparison between the actual application results of the WOAfuzzy PID algorithm and the traditional lighting control results.
4 Discussion and conclusion
The lighting of highrise buildings consumes a significant amount of energy, making it an increasingly important focus in energysaving research. The reliability of LED lighting in terms of energy efficiency has been demonstrated through the results of 52 pilot projects by Omran et al. [19]. Barkhordar's study [20] also confirms the significant role played by LED lights in energysaving illumination. Therefore, this paper investigated the control of energysaving lighting in highrise buildings using LED lights.
The advancement of intelligent algorithms has enabled the possibility of achieving intelligent control of lighting. Liu et al. [21] utilized genetic algorithms to achieve optimal illumination compensation in dark environments, thereby realizing intelligent light compensation. Yi et al. [22] developed a Bluetoothbased smart lighting controller that enables switch control, brightness adjustment, and various lighting mode transformations through Bluetooth connectivity. Li et al. [23] proposed an intelligent method for illuminance measurement based on binocular stereoscopic vision, allowing for dynamic monitoring and control of illuminance. From current research, it is evident that intelligent lighting control has been extensively studied. However, there is limited discussion regarding the energysaving effects of intelligent lighting control and even fewer studies focusing on highrise buildings as a whole. Therefore, this paper specifically analyzed the energysaving situation in ordinary offices located in highrise buildings within the context of intelligent lighting control. By combining the fuzzy PID algorithm with the WOA, parameter optimization was achieved to further enhance the algorithm's control performance. The results showed that the WOA was a robust optimization algorithm that demonstrates consistent performance in fuzzy PID parameter optimization. The simulation findings indicated that, compared to the PID and fuzzy PID algorithms, the WOAfuzzy PID algorithm demonstrated a faster response and smaller overshoot, proving its effectiveness in light control in terms of speed and accuracy. Lastly, the application results indicated that when using this algorithm for energysaving lighting control, it reduced monthly electricity consumption by 43.7% while still meeting the preset illuminance requirements.
The experimental results demonstrate the effectiveness of the approach proposed in this paper for energysaving lighting control in highrise buildings. This approach can be implemented in real office spaces within highrise buildings and can help reduce building energy consumption. However, there are some limitations in this study. For example, the experimental time was relatively short during practical application experiments, and there are also many other areas in highrise buildings with different illuminance requirements. In future research, it is necessary to simulate more complex lighting scenarios in highrise buildings to further validate the applicability of the approach proposed in this paper.
Funding
This research received no external funding.
Conflict of interest
The authors have nothing to disclose.
Data availability statement
The data that support the findings of this study are available from the corresponding author upon reasonable request.
Author contribution statement
Conceptualization, Qiong Yang; Methodology, Zixuan Yue; Software,Zixuan Yue; Validation, Qiong Yang; Formal Analysis, Zixuan Yue; Investigation, Qiong Yang; Resources, Zixuan Yue; Data Curation, Zixuan Yue; WritingOriginal Draft Preparation, Qiong Yang; WritingReview & Editing, Qiong Yang; Visualization, Zixuan Yue; Supervision, Qiong Yang; Project Administration, Qiong Yang; Funding Acquisition, Zixuan Yue.
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Cite this article as: Q. Yang and Z. Yue: Research on energysaving lighting control of highrise building by the PID control algorithm. Sust. Build. 7, 1 (2024).
All Tables
Optimization results of the PSO algorithm, GWO algorithm, and WOA for five functions.
Comparison between the actual application results of the WOAfuzzy PID algorithm and the traditional lighting control results.
All Figures
Fig. 1 Energy saving lighting control scheme based on PWM dimming and PID control. 

In the text 
Fig. 2 The flowchart of optimizing the fuzzy PID parameter by the WOA. 

In the text 
Fig. 3 The flowchart of the energysaving illumination control based on the WOAfuzzy PID control algorithm. 

In the text 
Fig. 4 Simulation results of the PID, fuzzy PID, and WOAfuzzy PID models for energysaving lighting control 

In the text 
Fig. 5 Antijamming simulation results of the PID, fuzzy PID, and WOAfuzzy PID models. 

In the text 
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