Energy saving based lighting system optimization and smart control solutions for rail transportation: Evidence from China

Journal Pre-proof Energy saving based lighting system optimization and smart control solutions for rail transportation: evidence from China XiaoDong Lai, MengYun Dai, Raufdeen Rameezdeen PII:

S2590-1230(20)30002-5

DOI:

https://doi.org/10.1016/j.rineng.2020.100096

Reference:

RINENG 100096

To appear in:

Results in Engineering

Received Date: 22 October 2019 Revised Date:

3 January 2020

Accepted Date: 4 January 2020

Please cite this article as: X. Lai, M. Dai, R. Rameezdeen, Energy saving based lighting system optimization and smart control solutions for rail transportation: evidence from China, Results in Engineering, https://doi.org/10.1016/j.rineng.2020.100096. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier B.V.

Energy saving based lighting system optimization and smart control solutions for rail transportation: evidence from China XiaoDong Lai1*, MengYun Dai2, Raufdeen Rameezdeen3, 1. Environmental economic research center, School of Economic and Management, South China Normal University, Guangzhou, 510631, China 2. School of Management Science and Real Estate, Chongqing University, Chongqing，400000，China 3. School of Natural and Built Environments, University of South Australia, Adelaide 5000, Australia Abstract: As the natural resources are becoming exhausted, energy consumption by metro systems dominates internal transportation resources in urban areas. The comprehensive exploration of energy improvements in lighting system energy is necessary. To evaluate the energy-saving potential and identify the efficiency improvement opportunities for lighting operations in metro systems, an intelligent energy management system for metro stations is examined through a case study in Nanchang city of China. First, the study explores the main factors influencing the lighting energy consumption of metro systems and analyses the lighting distribution in different station regions. Second, DIALux software is employed to optimize and monitor the best illuminating effect for hall lighting in the selected station. Third, an intelligent model is proposed for the lighting system based on the energy-saving scheme and solution using BECH energy analysis software combined with DIALux software. A thermal model is proposed to verify the energy and load performances. Results show that (1) the proper layout

by means of DIALux software, can not only

meet the functional demands of lighting but also reduce energy consumption; (2) intelligent lighting control system can improve the lighting energy-saving design, and the lighting control framework is capable of refined control; and (3) based on the performance analyses, the solution with the adopted DALI digital light adjustment is helpful for increasing passengers' comfort and realizing the goals reduction.The novelty is to integrate the lighting energy saving solution with software within an intelligent management and verified its valuable application, it is practical for construction emission control

*

Corresponding author: Associate Pro. of. Dr. Xiaodong Lai. Environmental economic research center School of Economic & Management, South-China Normali University; Email: [email protected],tel.: +86-20-39310072, fax: +86-20-39310072; Address: No.378 Waihuan West Rd. of Campus City, Panyu Guangzhou, China.

Key words: Lighting System; Influence Factors; Artificial Intelligent Control Technology; Energy Management System

1. Introduction Having benefited from rapid economic development for almost 40 years, China finds itself coping with the relatively new phenomenon of rapid urbanization. In terms of practical, global urban progress, the urban rail system has become a major mode of urban transportation in developed countries (Huang and Xia, 2011). Meanwhile, in 2016, 43 cities received approval for the construction of urban rail transit compared to 35 cities in 2012. During the 13th Five-Year Plan, the newly constructed mileage of the urban rail transit totalled 5,357 km, registering an annual newly increased mileage of 1,071 km, which is almost 3.8 times higher than that of the 12th Five-Year Plan (Huaon, 2016). By 2020, the total metro mileage is expected to reach 8000 km, with an estimated investment of two trillion Chinese Yuan, which is equivalent to $307.7 billion USD (China Association of Metros, 2016). As natural resources are being exhausted, researchers and practitioners are striving to identify an optimal way to manage corporate resources to achieve sustainability (Lee, Wu and Tseng, 2018). The complication of subway engineering and uncertainty of influencing factors, it requires an overall energy management strategy to optimize the energy using while guaranteeing energy supply for both the electricity and heat demand (Casals, Gangolells and Núria Forcada, et al., 2016). To transform underground spaces into efficient, flexible,

safe and user-friendly environments, hygiene and comfort requirements should also be met. Given all of these aspects and combined with the fact that nearly all spaces are below ground, underground metro networks are extremely high energy consumers (Ansuini, 2012). The lighting of spaces consumes a substantial amount of the world’s energy resources. According to 2011 statistics, lighting consumes 7.2% of the developed world’s primary energy resources and is responsible for 430x109 kg of carbon emissions (Powell, 2011). Traffic has a significant influence on energy consumption by dynamic lighting; based on a field investigation, Casals (2014) found that a lighting system accounted for 37% of the power energy consumption, while ventilation, air conditioning and escalators accounted for 63% of the power energy consumption. Artificial lighting provides a major source of lighting for these operations, as most metro rail systems are constructed underground. According to regulations, the metro lighting system should account for 14.2% to 16.1% of the total equipment loading of the metro system

(Mu, et al., 2010). However, the actual lighting energy consumption of the metro is as high as 20% to 30% based on 2016 statistical data reported by the Ministry of Transport of China. Electricity consumption by metros accounts for more than 30% of the operation costs in Beijing, Shenzheng and Shanghai, and the annual energy consumption in these locations is approximately 100 million kWh (Nie, 2017). With such a large amount of energy use and high carbon emissions, there is a need to consider lighting efficiency in every aspect of life to save energy and reduce carbon emissions (Belizza and Claudia, 2010; Zuo and Zhao, 2014; Sheinbaum et al., 2011; Zuo, Pullen and Rameezdeen et al., 2017). Therefore, this paper tries to serve two purposes: (1) evaluate energy-saving potential and identify energy efficiency improvement opportunities for the lighting system in metro operations through DIALux intelligence software and (2) recommend intelligent energy-saving solutions for planners by means of design improvement and artificial intelligence (AI) management control of the lighting system. This study integrates a traditional lighting analysis software with an energy performance analysis software to enhance the design effects. The rest of the paper is organized as follows. Section 2 is a literature review that explores existing researches on lighting energy reduction in metro transportation, areas for potential environmental lighting improvement, factors influencing lighting electricity consumption in metro systems and the trend of AI technology and the schematic of its working system operations. Section 3 describes the research method selected for this study and analyses the artificial lighting system distribution design from a technical perspective (i.e., the AI control system) and then calculates the energy and loading performance. Section 4 presents the simulation and result analysis based on the above methodology and proposed solution, verifying the energy-saving and load performance results of the design from a management perspective. Section 5 discusses the findings and implications against the current research and the case study outputs, while the last section, Section 6, provides conclusions that can be derived from this research and forecasts future research directions. This study attempts to accurately review energy consumption in metro station halls and hopes that the energy-saving plan can provide references for future technological advancements and energy-saving management options from an aspect of artificial intelligent.

2. Literature Review 2.1 Environmental lighting management solution

A metro not only provides passengers with quick and convenient commuting services but also brings a sense of pleasure to a passenger’s journey. A survey shows that environmental comfort within a metro is mainly influenced by the lighting environment of the metro station (Burnett and Pang, 2004). A comfortable lighting environment can, to some extent, release passengers’ stresses after a hard day at work and even cheer them up (Ahn, Chung & Cho, 2016). In the design process, environmental elements, layouts, passengers’ movement behaviours and visual balance should be considered based on the metro lighting standards of different functional spaces (SAC, 2009). In practice, it has several methods to monitor and manage the environmental light. DIALux is one of the solutions. The DIALux software takes the distribution of different light sources into account, tracing light accurately,

so

that

it

can

calculate

the

required

illuminance

more

accurately(Meshkova and Budak,2013). Refined lighting design is a major feature of this software (Mangkuto,2016). It can be used to determine lighting solutions for various types of construction projects and adjust the lighting system to reduce the overall energy consumption of the building and create a comfortable light atmosphere(Vizeu Da Silva) et al. 2017. And its visual design and analysis module helps to achieve intelligent control, playing a vital role in building energy conservation (Kurian et al, 2018).

2.2 Factors influencing lighting electricity consumption Underground construction limits the adoption of natural lights; therefore, artificial lighting becomes a major source of metro operations. Existing studies have shown that luminance and illumination, colour temperature, light and darkness adaptation, as well as the comfort of the lighting environment are major factors influencing electricity consumption (SBTS, 2004; CABR, 200). These factors are discussed as follows. Luminance and illumination: Luminance is a physical quantity representing the surface luminous intensity of an illuminant and is measured by candela/square metre (cd/m2). Illumination refers to the illuminated degree of an object and is measured by lux (Huang et al., 2017). In a metro station lighting system, illumination is usually adopted as a quantitative index of the lighting degree. According to the evaluation index system, the lighting degree of an object should resonate with the requirements of the human eyesight and design. This observation highlights the necessity of

controlling luminance within a reasonable scope to provide adequate lighting and energy-saving measures within a friendly circumstances to avoid waste caused by excessively high illumination (Han et al., 2016; QTSB and CSA, 2008) Colour temperature: Colour temperature is a combination of the function and artistic effects of lighting. When providing the lighting function, it creates a comfortable lighting environment for passengers. Colour temperature is an essential factor in lighting design. The lighting function and artistic design can be fully integrated according to the colour temperature requirements in different station areas. A light source below 3,000 Kelvin offers warmness and a feeling of leisure to humans. Transitional areas should preferably maintain a natural colour temperature (approximately 4,000 Kelvin). The platform rim should choose a colour temperature of approximately 5,700 Kelvin. A light source above 6,500 Kelvin is refreshing and suitable for underground commercial activities (İsmail et al., 2017). Glare: Glare refers to the uncomfortable illumination distribution in vision or a sharp contrast of luminance in time or space, thus causing visual discomfort and impairing the visibility of an object. When luminance is disproportionally distributed and the contrast is sharp, unpleasant disorientation occurs from the lights. This unpleasantness can be fixed by increasing the background luminance or decreasing the surface luminance of the light source. The luminance, quantity, position and surface size of a light source can be adjusted as well (Chew, 2016). Brightness adaptation and darkness adaptation: the visual adaptation to changes in environmental light from dark to light is called brightness adaptation, while the opposite is called darkness adaptation. Both are uncomfortable to passengers, and as a result, many accidents have occurred at the entrance or exit areas (Chen, 2013). Research shows that lighting conditions should be slightly higher than eye level for people to adapt is and avoid accidents. This observation indicates that it takes approximately 30 to 40 minutes for passengers to reach darkness adaptation but only one minute to reach brightness adaptation (Chew, 2016). Fig. 1 shows the change curve for brightness and darkness adaptation. In the entrance area, transitional lighting can be designed to facilitate eye adaptation and reduce the occurrence of accidents.

Fig. 1 Lightness and darkness adaptations (Chew et al., 2016)

2.3 Artificial intelligence lighting energy saving control technology Along with the rapid development of computer and sensor technologies, metro station lighting systems have become increasingly smart. AI control technology has received widespread recognition and applications, which poses a great challenge to traditional control theories (Mohammad, 2014). Traditional lighting control technology can no longer satisfy the current operational requirements and meet the energy conservation demands of green development. Lighting control technology has experienced three main development periods: the manual control period, automatic control period, and intelligence control period (Miki M, 2006). In the first period, manual technology is used to control lighting equipment. In the second period, along with the development of electrical technology, lighting technology is improved and replaced with an automatic lighting control system. In particular, the use of sensors and sensitive parts equips lighting control to become intelligent, digitized and modernized. Users can realize lighting self-management through the central control system. In the third period, the intelligent lighting control system greatly improves energy conservation, systematization and digitalization, including an independent system structure, simple and stable operation system, and automatic monitoring and alarming system (Wang, 2013). An intelligent lighting control system features a distributed bus structure. The sensor and driver within the system are equivalent to each other. The system central processing unit (CPU) is independent, and the faults of any other sensor and driver will not influence

the operation of other components. The exchange of maintenance and components is easy and convenient, with no need for re-cabling (Yuan and Damld, 2007). An AI lighting control system is composed of system components, input components and output components (Xiang, 2007). The system controls the component signals along with the sensor components and lights via the terminals. The working mode of lamps in different circuits is adjusted, and the operation principle is shown in Fig. 2. In the AI control system, the monitoring centre is composed of a computer with the following components: an integrated closed wireless network communication technology, called the ZigBee terminal, and global system for mobile communications (GSM) module. The data transmission between ZigBee and GSM is controlled through the computer centre: transfer to the route cluster for decoding, respond to the command from the control centre and deliver instructions to the ZigBee terminal.

Fig. 2. Schematic diagram of the intelligence lighting control system operations

From the above literature review, it can be seen that energy consumption in the metro system is a critical area for energy reduction, specifically in the lighting area (Belizza and Claudia, 2010; Sheinbaum et al., 2011; Nie, 2017; Casals, 2014). Most traditional designs on lighting systems focus on functional fulfilment and environmental improvement for passengers. Therefore, there are three research questions: 1) Does the design use energy-saving technology? 2) Apart from the difficult design of lighting systems, are there any soft solutions to improve energy reduction? 3) How does the lighting design software integrate with energy saving and energy efficiency improvements? All of the above questions have yet to be answered and remain open for further exploration.

3. Methodology and novelty

To answer the above three research questions and achieve our research aim, a three-stage study was carried out based on the factors affecting energy consumption during the operational stage of a lighting system. This paper employs the “technology plus management” method to conduct an integrated analysis on energy saving and efficiency improvements via the selected case. This paper firstly employs DIALux software integrated with the AutoCAD framework to optimize the station lighting performance in accordance with the requirements of the lighting environment. DIALux is a well-known free software frequently used for the design of lighting installations (Gómez-Lorente, 2013). Secondly, to appropriately reduce electricity consumption while maintaining a comfortable lighting service in the underground transition space, a smart intelligence control mode is proposed to monitor lighting management. Thirdly, the authors integrated the proposed DIALux framework into the energy analysis software BECS, in addition to the load analysis of BECH made by Thsware†, to create the overall energy-saving analysis model and verify the energy performance. As an emerging energy analysis tool, BECH has the characteristics of intelligent operation and integrated functions. Based on the accurate geographical location of the building, BECH can comprehensively and accurately calculate energy that is difficult to perceive from different complex sources. In addition to detailed analysis of energy consumption sources, it also refines the point in time when energy consumption occurs, which helps to realize intelligently optimized the space and time management of lighting energy saving. This paper established an intelligent lighting control system integrating DIALux and BECH software. The simulation data of BECH is used as the statistical ground for the model system. The comparison between the actual test results and the optimization results of the model system verifies the effectiveness and practicality of the system. Based on the system perspective, the intelligent lighting control system analyzes the overall energy consumption of the building according to the lighting design plan, performs intelligent analysis, feedback and control, and further adjusts and optimizes the lighting mode. The design of this system not only focuses on modules with large

†

Thsware is a high-tech enterprise focusing on green building software and solutions and related system software, including energy saving design, energy consumption calculation, heating load, sunshine analysis, daytime lighting analysis, building ventilation, and building sound and hot environments for residential areas.

energy consumption expenditures, but also pays more attention to detailed energy consumption. As a system, energy consumption is systematically studied and solved through system optimization. The intelligent lighting control system not only provides ideas for the intelligent management of subway lighting, but also provides a valuable building energy analysis solution for the engineering community to a certain extent. The structure of the intelligent lighting control system is shown in Figure 3.

Figure 3. The structure of the intelligent lighting control system

3.1 Stage one of the study: light source distribution optimization The distribution of luminaires in exterior or interior lighting installations is normally calculated with software applications based on the finite element method, Monte Carlo method, or the linear optimization (Pachamanov & Pachamanova, 2008). The necessity to reduce the energy requirements of lighting systems should encourage engineers towards a more mature and conscious approach, for example, their main goals should remain visual comfort and reduction in energy and maintenance costs (Salata et al., 2016). Urban rail systems are facing increasing pressure to minimize energy consumption and thus reduce operational costs and environmental impacts (Gonzalez-Gil et al., 2015). Therefore, a good design for light source distribution through artificial intelligent software or technology arrangement is critical for energy-saving and environmental improvements.

Functionally, a metro station can be divided into four areas, namely, the entrance and exit area, the station hall area, the platform area and the working area. The design focus is different between lighting design and settings. Illumination should not be excessive since sustainable lighting installations in each area should minimize electricity consumption. The lights in working area are designed to serve the functional requirements of the employees’ daily work and are not linked to the passenger use. Therefore, the working area is excluded from this analysis. The following sub-sections describe the functions of the areas related to the passenger use requirements. (1) Entrance/exit area The entrance and exit area functions as a transitional area between the underground space and above ground space. Comparatively, light adaptation from the underground space to above ground space is much shorter and takes approximately one minute. Therefore, light source adaptation in entrance and exit area should combine natural and artificial light, and natural light should as far as possible be the major light source. When natural lighting is inadequate, artificial lighting, such as spotlights and downlights, can be added as supplements. To facilitate the intake of natural light, building materials in the entrance and exit area are often made up of strong light-transmitting materials, such as the glass. Apart from prioritizing the intake of natural light, the directional effect of lamps should also be considered in entrance and exit areas. The array of lamps can strengthen the passengers’ sense of direction during the lighting transition period. (2) Station hall area The station hall is large and can be further divided into many subsidiary functional areas. These subsidiary areas differ from each other due to their respective functions. Different types of lamps can be mixed to highlight the different features and the three-dimensional lighting layout. In addition, the lighting decoration in the transitional area should be directional and play a transitional and link role among these subsidiary spaces, it requires the lighting layout to be more harmonious and remains focuses. These subsidiary areas can be well connected with clear layers of division. The standard illumination of the station hall area is 100 lux to 200 lux. The evenness of illumination, namely, the gap between minimum and maximum illumination, should not be lower than 0.7 (CIE, 2014), while the recommended value is 0.8. The colour temperature should be controlled between 3,000 and 6,000 Kelvin

(CABR, 2009). (3) Platform area The platform area is a transitional space connecting the waiting area, and the entrance /exit areas. To create a comfortable waiting environment, the illumination and colour temperature of the light source are adjusted. From a safety perspective, the shielding door between the platform and train operation area must be set up with primary lighting. For example, the upper part of the shielding door is set up with fluorescent lamps to develop a light belt. In this way, passengers can be informed of the boundary between the platform and the train to prevent accidents.

3.2 Stage two study: AI lighting energy saving management system The Nanchang Metro Line 1 intelligent lighting energy saving management system features the digital light adjustment (DALI) model. According to the lighting requirements of different areas, refined control is implemented to reduce lighting energy consumption and increase passengers’ comfort levels. Based on the artificial intelligent lighting energy management control technology review in Section 2.3 and the technology design analysis in Section 3.1, a proper intelligent control system for the station is proposed.

3.2.1 Lighting area distribution The Nanchang Metro Line 1 lighting system is divided into different functional areas: the metro station lighting area, platform lighting area, and entrance and exit lighting area. Every lighting system has its own functional requirements. The metro station lighting area is further divided into areas of passenger flow, ticket service, advertising, and staircase and escalators. The platform lighting area includes the areas of passenger flow, passenger rest, metro shielding door light belt, and high-low exchange. The entrance and exit lighting area includes the passenger flow area and outdoor high-low exchange area (Fig. 4).

Lighting areas

03

02

3. Exit and entrance lighting areas: passenger flow area and outdoor high-low exchange area (stairs and escalators). 2. The metro platform lighting areas: passenger flow area, passenger rest area (benches), shielding gate light belt, and high-low exchange area (stairs and escalators).

01

1. The metro station lighting areas: passenger flow area, functional area (ticket service and automatic TVM), advertising area, and high-low exchange area (stairs and escalators). Fig. 4 Nanchang Metro Line 1 lighting distribution diagram

3.2.2 System function design The Nanchang Metro Line 1 intelligent control system is composed of a central control machine, corresponding control gateway, switch module for power distribution, DALI module, illumination sensors, and infrared sensors. According to the differences between the application scope and functions, the lighting system can be further divided six sub-systems for the area of passenger flow, metro subsidiary equipment, advertisement, passenger rest, door lighting belt and outdoor area. The function areas are refined by means of refinement technology to control the lighting distribution. In order to meet the passengers’ basic lighting demands and guarantee the normal use of the metro public devices, illumination of the lighting system is intelligently controlled. In the passenger flow area, the lighting system can automatically change its illumination according to the amount of users. In the subsidiary function area, illumination is automatically adjusted in accordance with the movements of passengers. In the advertising area, illumination is modulated based on illumination near the light box. In the passenger rest area, illumination is adjusted based on whether this area has passengers. In the shielding door light belt area, illumination is modulated to remind passengers of getting on or off the train. In the outdoor area, the indoor sensor can identify the adequacy of natural lighting and decide whether the indoor lighting equipment should be adjusted.

3.3 Stage three study: load energy consumption calculation DIALux is recognized as a professional tool for lighting design. However, the solution frame cannot indicate the energy-saving potential or efficiency. Thus, the authors decided to employ both DIALux and BECH energyanalysis software as the

module of the intelligent control to further verify the overall energy-saving performance in the station hall. BECH software is a professional solution that provides detailed analysis for building energy conservation. According to the HVAC standard (He, 2008), the heat load, cold load and annual load energy consumptions can be measured as follows: (1) Heat load consumption The heat load includes the following items: heat consumption during the heat transfer process from retaining structure, heat consumption for heating infiltrated cold air or fresh air, heat consumption from other uses, and heat correction for household metering and intermittent heating. The heat consumption of the retaining structure can be calculated according to Eq. (1):

Q = αFK(tn − twn )

(1)

where Q stands for the heat consumption of the retaining structure (W),

α

represents the temperature difference correction factor of the retaining structure, F represents the area of the retaining structure ( ㎡), K refers to the heat transfer coefficient of the restraining structure ( W （㎡ / • ℃）), t n represents the indoor temperature ( o C ), and

twn represents the temperature of the outdoor or adjacent

room ( o C ). The aperture method of the infiltration calculation is adopted in the calculation process of the heat load, which includes heat consumption for cold air in the room and should be defined based on the internal partition, structure of doors and windows, orientation of doors and windows, indoor and outdoor temperature and wind speed. Eq. (2) is the function for this calculation:

Q = 0.28CP ρwnL(tn − twn )

(2)

where Q stands for the heat consumption heating cold air from the aperture (W),

CP represents the specific heat capacity at a constant air pressure CP =1KJ / (kg •℃) ,

ρwn

represents the air density of the outdoor design temperature for heating (kg/m3),

t n stands for the indoor design temperature for heating ( o C ), twn represents the outdoor design temperature for heating ( o C ), and L refers to the consumption of heating the infiltrated cold air (m3/h). Other methods of obtaining heat consumption include heat consumption via water evaporation, consumption for the heating of cold materials, and heat loss for the heating of pipes. (2) Cold load consumption

The cold load of air conditioners is calculated based on the following items: heat gained from outdoor sunshine; heat transferred from the retaining structure; heat released from the occupants, lights and equipment; incoming heat combined with fresh outdoor air; and heat gained from other channels. The cold load includes heat from sunshine, the retaining structure, fresh air, occupants, light, equipment and other channels. The total cold load in an air-conditioned rooms is taken as the hourly comprehensive maximum value of the above items. A metro station hall is an underground construction; thus, this paper only considers loads via equipment cooling, light cooling and human cooling. a. Equipment cooling load consumption The equipment cooling load can be calculated using Eq. (3):

Qt = Qs Xτ −T (3) where T refers to the moment that the heat source is in use (o’clock), τ − T stands for the period from the moment that the heat source is in use to the calculation

Xτ −T refers to the cooling load coefficient of the equipment and lamps during the period of τ − T , and Qs represents the amount of heat release from the time (h),

heat source (W). b. Lighting cooling load The lighting cooling load

Qt (W) of the lighting equipment heat release can be

calculated from the counting moment, as shown in Eq. (4).

Qτ = Qs Xτ −T where T represents

(4) the g moment of light (o’clock),

τ − T stands for the period

Xτ −T refers to the cooling load coefficient of light dissipation during the period, and Qs represents the amount of from the moment of lighting to the calculation time (h),

heat released by the equipment (W). c. Occupant’s cooling load The radiation heat occupies 2/3 of the total sensible heat dissipation of human body because of the heat storage lag problem. The cold load of sensible heat for occupants ( Qt ) can be calculated based on Eq. 5:

Qτ = Qs Xτ −T

(5)

where T represents the moment when people come into the space (o’clock),

τ − T stands for the period from when the people are in the space to the calculation

Xτ −T refers to the cooling load coefficient for people during the period of τ − T , and Qs represents the amount of heat released by the people (W).

moment (h),

The latent cooling load of the human body can use the real-time load, namely, the real-time load equals the latent heat release. Thus, the full heat-cooling load by people is the sum of sensible heat and latent heat.

4. Result and Discussion The following section provides the optimization and simulation results by means of DIALux software based on a computer aided design (CAD) structure for the case of the Nanchang No. 1 metro station in China and the energy performance analysis. This could enhance the redesign plan for the lighting layout in key areas of the metro station.

4.1 Optimized artificial intelligent lighting distribution simulation To explore the energy-saving status and related design of the lighting environment, the authors selected the Shuang-gang metro station hall for Line No. 1 as an empirical example and utilized the intelligent lighting software DIALux to optimize the lighting distribution. DIALux is recognized as a professional tool with functions of environment simulation and precise data analysis. 4.1.1 Existing energy consumption and management model The original design of the lighting system is refined within an indoor space of 80 m long, 30 m wide and 5 m high. Based on the on-site investigation, the lamps and lanterns in the Shuang-gang metro station hall are extremely orderly, and they have not been designed based on the lighting demand from different functional areas and cannot play a good guiding function to passengers. The light fittings of this space are as follows: all lamps and lanterns are adopted with the same type of light-emitting diode (LED) light; the colour temperature is positive white light (3000-6000 Kelvin, 49 wattage, 2 m long and 0.2 m wide). A lamp is arranged 5 m across each row with 15 lights. A lamp is arranged at vertical intervals of 1 m, where each string has 10 lights, totalling 150 lights for the space under consideration. The simplified model is shown in Fig. 5.

Fig. 5. Lamp layout in the Shuang-gang metro station

Based on the DIALux output, the authors imported the original scheme into the DIALux system for further analysis. Fig. 6 shows a 3D effect diagram, from which the light arrangement and lamps selected in the original scheme are not perfect; the extremely ordered lamps cannot fulfil the need to distinguish functional lighting demands between different spaces and the guidance required for passengers. The intensive lamp arrangement makes illumination (250 lux) on the working platform higher than the standard illumination value (100-200 lux), which causes uncomfortableness to passengers and employees, as well as energy wastage.

Fig. 6. A 3D effect diagram of the lamp arrangement in the Shuang-gang metro station

When the original file is converted into DIALux software with a light distribution curve, the related effect drawing can be shown, as revealed in Fig. 7 and Fig. 8, from which it is easier to calculate the total energy consumption per year to be 42336 kwh (150 lights*49 wattage/ea*16 h/day*360 days).

Fig. 7 Effect of the lamp arrangement when adding the distribution curve of light

Fig. 8 3D effect diagram of the lamp arrangement when adding the light distribution curve

4.1.2 Lamp arrangement optimization simulation To obtain the best light configuration for energy savings, it is necessary to review the entire lighting system arrangement. Firstly, the metro station hall indoor space under consideration are 80 metres long, 30 metres wide and 5 metres high, which is the same size as the actual space. The materials of the ceiling, floor, wall and working surface are established. Meanwhile, the related positions and material of the gate machine, ticket vending machine (TVM), security check and staff service area are all confirmed (see Fig. 9). Some structures, such as pillars, might be chosen as areas for commercial purposes and pasted with wall paper. Therefore, the surface materials of such structures are set to be paper-made. The materials of other structures are set according to current commonly used materials.

Fig. 9 Position diagram of the metro station hall settings

Secondly, the lighting equipment type and space layout (see Table 1) are chosen. In accordance with the principle of illumination and colour temperature requirements of the metro station hall, the intelligent lighting equipment is laid out in primary areas including the entrance and exit, TVM, security check and transitional areas to meet

the primary lighting requirements. Table 1. List of metro hall settings No.

QTY

Areas

No.

QTY

Areas

1

1

Security check (right)

6

1

Security check (left)

2

1

Security check (left)

7

1

Inquiry office (right)

3

1

Exit gate (right)

8

1

Inquiry office (left)

4

2

Exit gate (left)

9

1

Entrance gate

5

1

Ticket vending machine (right)

Finally, the artificial intelligent lighting design results are demonstrated by means of the light distribution curve (see Fig. 10). The light distribution curve is used to show how light is emitted by a lamp or light source and distributed into a space. In practice, the distribution curve depicts the illumination intensity and light distribution of a lamp, which essentially shows the distribution status within a given space. This method has been used to record the illumination intensity of lamps in different directions and reflect the luminous flux, light source quantity, power factor, size and efficiency of a lamp. The intelligent light distribution curve is closely connected with the selection of lamp types, namely, the shape of the transmitter, transparent parts and light source, and the position. This curve provides three ways to represent the light distribution curve: the polar coordinate method, rectangular coordinate method and isocandela curve method.

Fig. 10 Light distribution curve

The lamp type is selected based on the lighting requirements in each area. Energy efficiency can be maximized under the prerequisite of basic functional demands. Apart from adopting efficient light sources, the luminous flux rate, namely, the lamp efficiency, should be smartly measured as well (defined as the ratio of the valid luminous flux to the light source luminous flux). The light distribution curve can be used to judge the facula attribute and calculate the illumination of the selected area and lamp efficiency. Therefore, a proper intelligent lamp type can be chosen to meet the goal of energy conservation.

The illumination contour map shown in Fig. 11 is a detailed reflection of the partition and connection of the metro station light environmental design. The optimized design scheme can realize the zoning and convergence effects of the light environment design in the station hall. The average illuminance in key areas is kept at more than 150 lux, which is at the middle and high ends of the standard illuminance value. The illuminance in the entrance or exit, security check and inquiry window areas remains within 140 lux to 210 lux, while the average illumination is 175 lux. In the region of the TVM, for passengers’ ticketing convenience and security considerations, the lighting focus scope is expanded, and the intensity of illumination is slightly higher, with a range of 140 lux to 280 lux (the average value is 210 lux). In addition, on the left and top right sides of the illumination contour map, the area connecting the aisle and station hall implements an accent lighting, with illuminance at 140 to 210 lux, which plays the role of guiding passengers and helping them adopt to the lighting environment at the possibly earliest time. While the average illuminance in the transition zone is kept under 150 lux, which is lower than the illumination standard, this arrangement provides a good combination of lighting requirements and energy conservation.

Fig. 11 Illumination contour map

The pseudo-colour chart can more vividly reflect the illumination of the platform station (see Fig. 12). This chart uses special digital image processing technology to transform the grey-scale image into a full-colour image. The greater the illumination intensity is, the deeper the colour becomes. Otherwise, the lower the illuminance is, the lighter the colour becomes. The light colour changing from white to black indicates that the illumination gradually increases from 25 lux to 250 lux, and the distance between contour lines is set at 50 metres. Based on the pseudo-colour image, the pseudo-colour is deeper in the lighting entrance/exit, security, artificial consult window, ticket vending machine (TVM) and other areas. The colour changes from

orange to purple, and the illumination along the edge of the key area is 150 lux and gradually increases to 200 lux in the centre of the station hall. The pseudo-colour is black in the TVM area, and the value is up to 250 lux, which can better balance brighter light from the ticket machine screen and reduce the passengers’ discomfort when obtaining a ticket. The pseudo-colour in the transition zone changes from red to orange within the range of 100 lux to 150 lux to indicate the energy-saving effect on the conditions of meeting the lighting demand.

Fig. 12 Pseudo-colour chart

Summarized by the above illumination contour map and pseudo-colour image analysis, the optimized design scheme has realized the light distribution in the key functional area and the distributional coordination in the transitional areas. This scheme combined the lighting function and aesthetics in an appropriate manner. The optimized design not only provides the appropriate brightness, illumination and colour temperature but also eliminates glare and improves the comfort level of the light environment.

4.2 Operating the intelligent lighting energy saving control model Based on the model built in Section 3.2, the metro illumination distribution can be confirmed. Then, the calculation software can be employed to determine the model coefficient, and indoor intelligent lighting control can be realized (Samuel Tang et al., 2017). Currently, natural lights are combined with an intelligent lighting control algorithm to reduce electricity consumption, creating a favourable light environment with visual relaxation and increasing staff work efficiency. The intelligent energy saving mamangemnt model is based on the time and lighting distributions. In different periods, the system will automatically adjust the luminance in each functional area to achieve the refined lighting purpose. During peak hours, the full luminance model is on，while during the off period, the general model is on. The station illumination in all areas is required to be less than 90%~100%

of the maximum load. The station illumination in different functional areas is not the same (e.g., in the flow and functional area, station illumination requires 60% of the maximum load but requires less than 70% of the maximum load in the high-flow transition area). For example, the advertising area is less than 50%. At night, there are few passengers; therefore, the low-peak model is on. During holidays and festivals, the metro staff can freely change the model according to demand. Meanwhile, the system reserves a blank model for staff to self-customize the luminance in response to different operational demands. The intelligent energy saving management models of the Nanchang Metro Line 1 are presented in Table 2. Table 2. Nanchang Metro Line 1 lighting models Flow Function Advertising High-flow Rest area area area area exchange area Full luminance model Flat peak model Low-peak model No-operation model Firefighting model Maintenance model

Shielding door light belt

Outdoor area

90%

90%

90%

90%

90%

90%

90%

60% 40%

60% 40%

50% 30%

70% 50%

20%~90% 20%~90%

20%~90% 20%~90%

Lighting control

0% (Keep the emergency lights only) 0% (Keep the emergency lights only) Artificial control

4.3 Load energy analysis The authors integrated the DIALux lighting design solution into the BECH software model. According to actual construction, the outer wall is a single-story concrete basement that is 800 mm thick, with walls that are 5 metres high and 20 metres below ground level. Related engineering structures were equipped and divided into different rooms. The fresh air rate, occupant density, lighting power density (LPD) and equipment power are determined based on passenger flow, lamp and lantern distributions, and the running state of equipment. The operation times are identified based on the working days and holiday arrangements. The other parameters are also set, such as the temperature and humidity in different seasons, light specification (DIAL 7 Twinlights, 26 W; PHILIPS TBS471, 49 W; and DIAL 1 SiNOVA, 36 W), and light density (293, 249 and 462 when the temperature is 20 °C); finally, the authors compared the light sources with the imported DIALux software drawing. Fig. 13 shows the recreated model layout.

Fig. 13. BECS energy-saving model for the metro station hall

The relative heat load results are shown in Table 3. Table 3. Heat Load Summary for Different Zones Floor

Total heat (with fresh air)

Zones

Under ground floor

-1001 [Hall service centre] -1002 [Exit gate] -1003 [Exit gate] -1004 [Self-service ticket area] -1005 [Entrance gate] -1006 [Self-service ticket area] -1007 [Security area] -1008 [Security area] -1009 [staff service area] -1010 [staff service area] Sub-total Total

Sensible heat (with fresh air)

Latent heat (with fresh air)

Total heat (no fresh air)

Sensible heat (no fresh air)

Latent heat (no fresh air)

Max (W)

Time

Max (W)

Time

Max (W)

Time

Max (W)

Time

Max (W)

Time

Max (W)

Tim e

377047

22

211720

22

165327

6

225965

22

164606

22

61359

6

13043 12345

22 22

10327 9774

22 22

2716 2570

6 6

10554 9989

22 22

9546 9035

22 22

1008 954

6 6

9452

22

7750

22

1702

6

7893

22

7261

22

632

6

7923

22

6273

22

1650

6

6411

22

5799

22

612

6

8937

22

7328

22

1609

6

7463

22

6865

22

597

6

7248 6932

22 22

5739 5488

22 22

1509 1443

6 6

5865 5609

22 22

5305 5074

22 22

560 536

6 6

3821

22

3133

22

688

6

3190

22

2935

22

255

6

3198

22

2622

22

576

6

2670

22

2457

22

214

6

449945 449945

22 22

270155 270155

22 22

179790 179790

6 6

285610 285610

22 22

218884 218884

22 22

66727 66727

6 22

The curve for the cold load has been plotted, as shown in Fig. 14; the cold load volatility at different moments in a day can be seen clearly. The full load time period is from 5:00 am to 11:00 pm, the peak period occurs at 10:00 pm, and the peak heat load is 389.4 kW/day. The latent heat is very stable (164.5 kW/day), but the sensible heat load is gradually increasing with operation time, and the peak load is 224.9 kW/day.

Fig. 14 Cold Load Curve

(3) Annual load consumption BECH software provides a reference for the choice of cooling and heat sources. When computing the annual load of the metro station, not only building restraint structure, direction, and weather parameters but also the interference parameters of space and fresh air and the air-conditioner operation time need to be considered. During the simulation process, the space interference and fresh air parameters can be set in the “room type” section, and the open and close times of the air conditioner can be set in the “system partition” section. Table 4 shows the reported output. The annual heat and cold consumptions of the station hall are 98,919.918 kWh and 252,311.426 kWh, respectively, with a total load of 351,231.32 kWh. The heat consumption is 33,719.012 kWh in January (i.e., winter), and the cold consumption is 78,867.474 kWh in August, as illustrated in Fig. 15. The monthly cold or heat load can be monitored, including the peak load curve (Fig. 16). Table 4. Annual load summary of the metro station hall Description

Heat load (kW)

Cold load (kW)

Latent heat load (kW)

Heat consumption (kWh)

Cold consumption (kWh)

Annual load Jan. Feb. Mar. Apr. May Jun. Jul. Aug. Sep. Oct. Nov.

343.053 343.053 343.001 305.182 160.421 82.465 0 0 0 0 1.792 102.214

423.687 0.029 3.708 16.673 103.384 267.921 319.153 368.038 423.687 319.503 178.591 19.766

196.127 0 0 4.911 40.029 104.512 143.446 167.641 196.127 144.372 46.149 4.354

98919.918 33719.012 23477.273 18217.166 5377.398 287.481 0 0 0 0 3.469 964.622

252311.426 0.084 23.187 281.44 3915.303 14714.979 38084.015 66011.053 78867.474 39233.642 9776.568 1272.334

Dec.

337.334

5.663

0

16873.497

131.348

Fig. 15 Annual Load: Heat and Cold Consumption

Fig. 16 Annual Load: Peak Load

4.4 Energy-saving results Power consumption-based calculations are carried out using the lighting design software DIALux. Based on the above design proposal, the authors collected data from the Nanchang Metro Line 1 in Shuang-gang Station to estimate the energy conservation capacity of the intelligent lighting control system. There are 776 lights in total in the public lighting area (excluding emergency lights), of which 192 sets form the light belts in shielding doors, with the power of each set being 10 W. The remaining 584 sets of lights are functional lights, and the power of each set is 22 W. Once all data are established, we ran the system for simulation. Based on the above light control models, the use of an intelligent lighting system

is expected to reduce electricity consumption by approximately 100 kW/day. As the metro station is charged 0.584 Chinese Yuan/kWh ($0.0898 USD/kWh), the electricity savings would amount to $3,278 USD/year, while the reduction in carbon emissions would be 997 tons/day, totalling 363,905 tons of carbon emissions reduction/year. The total energy consumption of the Nanchang Metro No. 1 Line is 200 million kWh/year (Peoplenet, 2014). There are 24 stations along the No. 1 metro line; the average energy consumption of each station hall is 22831.1 kWh/year, and the consumption/day is 6849.31 kWh. The consumption of the lighting system accounts for 14.05% of the total energy consumption. Compared to the relevant policies and regulations in China, the subway lighting system of the equipment load is 14.2% to 16.1% on average (Nie, 2017), and the proposed results are within the requirement. The energy consumption rate is even better than the on-site result conducted by Casals et al. (2014), who found that the lighting average consumption is 217.64 kWh/m2·year, which dominates the underground station’s energy consumption (37%). Knowing that the related conditions are different, the solutions proposed above can help to improve the load reduction and saved energy for the metro lighting system to meet average load consumption requirements.

5. Implications This research provides a feasible design proposal and reference for the intelligent control model of a metro lighting system. The following are the major implications of this work: (1) A proper distribution layout of a metro station lighting system based on factors influencing lighting electricity not only meets lighting demands but also reduces energy consumption. DIALux lighting software is employed for the lighting control system design. The distribution of different light sources and energy conservation are taken into consideration. The metro station hall area is adopted as the major portion of the lighting energy design. The 3D diagram of the lighting environment, the light distribution curve, the illumination contour map, and the pseudo-colour chart simulated by DIALux were the generated outputs. All of these tools show that a proper layout of the metro station lighting system in major areas, namely, the entrance area, station area and platform area, not only meets lighting demands but also reduces energy consumption. In addition, the above findings can provide a fresh perspective for designing a more reasonable solution. The design

proposal can properly fulfil the lighting requirement for normal metro operations and improve energy efficiency. (2) Based on the analysis of the intelligent lighting control system, it is found that the lighting control requirements in different areas and different periods can guide lighting energy-saving designs. Namely, the lighting control framework under such different management modes based on the lighting functions of various systems is capable of refined control. The solution adopted into the DALI digital light adjustment is helpful in reducing lighting energy consumption, increasing passenger comfort levels, and realizing the goals of energy conservation and emission reduction. Moreover, intelligent lighting control technology has significant advantages in terms of load control, current testing and switch metering. The development of intelligent lighting control technology can largely facilitate lighting control, maintenance and management. (3) From a methodology perspective, the integration of DIALux lighting software with the energy analysis software BECH is a good way to monitor and measure energy savings quantitatively. DIALux is an excellent tool for simulating illumination and can help the designer know the status before the design implementation, while the energy analysis software can help the designer further understand how energy or load consumption is distributed in different locations. The integration of both software tools can improve the design and operational management of the metro energy-saving potential.

6. Conclusion and Limitations 6.1 Conclusion With the aim of exploring energy-saving potential and identifying energy efficiency improvement opportunities for lighting systems in metro operations, this paper first reviewed the environmental requirements, major factors influencing metro station lighting systems, and trends of intelligent lighting technologies. Based on the major factors influencing the metro station light environment, the lighting requirements in different areas were analysed. Next, DIALux software is used to design and compare the metro station lighting energy-saving plans, and an intelligent lighting control technology is adopted for energy efficiency management purposes. Then, a metro station hall is chosen for empirical study to verify the energy performance. Recent decades have witnessed increasing attention given by policymakers,

industries and academics towards carbon dioxide emissions and energy (Zuo et al., 2012). The above research helps us draw the conclusion that the reasonable use of intelligent lighting systems can effectively reduce the energy consumption of subway station lighting, prolong the lifetimes of lamps and improve the work efficiency of operating personnel. Compared to the traditional method of lighting control, intelligent lighting control technology has obvious advantages in controlling loads, current tests and switch lamps. As a result, the lighting control and maintenance management of metros becomes more efficient and simpler. The novelty of this study is to integrate the lighting energy saving solution together with the energy analysis software within an intelligent management and verified its valuable and application. Which leads a good reference for the energy saving oriented construction like metro. 6.2 Limitations With constant developments in society and technology, there are increasingly more factors influencing metro lighting system energy consumption, and the functioning mechanism has also become increasingly complex. Therefore, the energy conservation of metro lighting systems should be modernized not only to meet energy conservation requirements but also to create a more user-friendly environment. The intelligent lighting system proposed in this paper provides a perspective on energy conservation in different areas of a metro station. Limited by data resources, the selection of energy-saving factors, the energy-saving design calculation method and the energy-saving evaluation are worth more research efforts. This paper proposes and develops a lighting control model with energy-saving potential. Currently, a major bottleneck in metro lighting energy conservation is how to extensively apply an AI lighting control system in practice. Energy consumption by multiple trains on urban metro lines is known to be predominantly affected by operation strategies (Liu and Zhao, 2017), and integrated energy management is needed to realize this goal.

Acknowledgements This research is supported by the Ministry of Education Humanities and Social Sciences Planning Fund (No. 19A10574031); the Social Science Planning Project of Jiangxi Province (12th Five-year Plan) (No.15GL28); the 9th China Postdoctoral Special Foundation (No. 2016T90789) and the Nature Science Foundation of Guangdong Province (No.2018A030313269).

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Highlights An integration of the lighting energy saving solution together with the energy analysis software within an intelligent management is proposed. The traditional lighting energy management combining with energy performance analysis can improve the design effects. •Proper layout of the metro station lighting can meet its functional demands and

reduce the energy consumption;

•Intelligent lighting control system can improve the energy-saving design and the

lighting control management mode is capable of the refined control;

•Solution adopted with digital technology is helpful to increase passengers’ comfort

and realize the goal of energy conservation and emission reduction.

Declaration of Interest Statement

We would like to declare that the enclosed manuscript entitled “Energy saving based lighting system optimization and smart control solutions for rail transportation: evidence from China” submitted to the journal of “Result in Engineering” has NO conflict of interest exits, and manuscript is approved by all authors for publication. I would like to declare on behalf of my co-authors that the work described was original research that has not been published previously, and not under consideration for publication elsewhere, in whole or in part. All the authors listed have approved the manuscript that is enclosed. Sincerely

XiaoDong Lai, Environmental economic research center, School of Economic and Management, South China Normal University, Guangzhou, 510631, China. Email: [email protected]; MengYun Dai School of Management Science and Real Estate, Chongqing University, Chongqing， 400000，China E-mail: [email protected]; Raufdeen Rameezdeen School of Natural and Built Environments, University of South Australia, Adelaide 5000, Australia Email: [email protected]

Credit Author Statement

We would like to declare that the enclosed authors contribution as listed below for the manuscript entitled “Energy saving based lighting system optimization and smart control solutions for rail transportation: evidence from China” submitted to the journal of “Result in Engineering”

XiaoDong Lai : Conceptualization; Funding acquisition; Project administration; Resources; Investigation; Methodology; Supervision; Roles/Writing – original draft; MengYun Dai : Data curation; Formal analysis; Software; Validation; Visualization; Roles/Writing – original draft; Raufdeen Rameezdeen : Writing – review & editing

Sincerely

XiaoDong Lai, Environmental economic research center, School of Economic and Management, South China Normal [email protected];

University,

Guangzhou,

510631,

China.

Email:

MengYun Dai School of Management Science and Real Estate, Chongqing University, Chongqing， 400000，China E-mail: [email protected]; Raufdeen Rameezdeen School of Natural and Built Environments, University of South Australia, Adelaide 5000, Australia Email: [email protected]