• Dorey Orozco-Paz¹
• Emilio Batres-Guillen¹
• Rafael Torres-Escobar^{1. rafael.torrese@anahuac.mx}
• Carlos Alberto Ochoa-Ortiz^{2. alberto.ochoa@uacj.mx}

Introduction

• Mexico City, one of the world's most densely populated metropolises, features numerous urban parks for recreational use.
• Proper lighting is crucial to avoid negative impacts on ecosystems and human health while ensuring safety, preventing accidents, and reducing light pollution.
• This study utilizes the "Monkey Algorithm" to optimize the number and placement of luminaires in Mexico City parks.
• This algorithm, inspired by monkeys' resource-finding behaviors, involves three steps: climbing, watch-jump, and somersault processes. Factors such as lamp position, height, power consumption, and light intensity are considered to maximize coverage

Fundamental Concepts

• Smart Cities are cities that use information and communication technologies (ICT) to improve the quality of life of their inhabitants.
• Lighting in public spaces, such as parks, has a significant impact on the safety of citizens and their ability to enjoy these areas during nighttime hours.
• However, the design, placement, and distribution of such lighting must be meticulously orchestrated to avert potentially dire consequences.

Design and Characteristics of the Public Park

• The public park under examination spans an expansive 30-hectare area, serving as a vital communal space for the local population.
• Within this park, a wide range of activities unfolds, including jogging, leisurely walks with pets, contemplative moments on park benches, and the simple pleasure of connecting with nature.

Existing Lighting Arrangements

• To optimize the lighting, it is crucial to fine-tune the placement and intensity of the luminaires to align with the park's specific needs.

• In the model, we employed a 4-meter-tall luminaire with precise specifications. This luminaire, the decorative urban Simon ALYA Istanium LED (TALYA LH), is constructed from die-cast aluminum and is designed for lateral or suspended installation.
• Notably, this luminaire excels in providing the necessary illumination for public lighting with a remarkable degree of energy efficiency.
• Its outstanding features include: a minimum luminous flux of 1,760 lumens. and an impressive luminous efficiency of up to 138 lumens per watt (lm/W).

Monkey Algorithm Fundamentals

• The Monkey Search Algorithm (MSA) was proposed by R. Zhao and W. Tang in 2007, inspired by the foraging behavior of monkeys as they search for food by scaling mountains.

Implementation of Monkey Algorithm in Luminaire Optimization

• In this case, the Monkey Algorithm is used to adjust the location and intensity of the luminaires iteratively, seeking to maximize energy efficiency and meet the lighting requirements. The following is an explanation of the process that was followed.
• Monkey Algorithm implementation begins with data collection on the current luminaire layout, park topography, and lighting requirements for specific activities.

1. Light Uniformity:

   • To maximize uniformity, we consider the variability in light intensity across the park.

   • Uniformity can be defined as the inverse of the standard deviation of light intensity at different points in the park:

   • Here, represents the standard deviation of light intensity across various points. A lower standard deviation indicates a more uniform light distribution, which we incentivize by maximizing this term.

2. Energy Consumption:

   • Assuming that the consumption of each lamp depends on its height (as greater heights may require more energy to achieve larger coverage), we can include a penalty for total energy consumption:

   • Where represents the energy consumption function based on height (which may be linear or quadratic), and is an adjustable penalty factor to weight energy consumption.

Complete Objective Function

The full function could then be expressed as:

Representation of Solution

• At first an integer is defined as the population size of monkeys.
• And then, for the monkey , its position is denoted as a vector , and this position will be employed to express a solution of the optimization problem, i.e., the position and the decision vector possess the same form, respectively.

Initialization

• The point will be taken as a monkey’s position provided that it is feasible. Otherwise, we re-sample points. Repeating the process times, we obtain feasible points which will be employed to represent the initial positions of monkeys , , respectively.

Climb Process

1. Randomly generate a vector , where

2. Calculate

3. Set respectively, and let
4. Update with provided that is feasible. Otherwise, wee keep unchanged.
5. Repeat steps 1 to 4 until there is little change on the values of objective function in the neighborhood iterations or the maximum allowable number of iterations (called the climb number, denoted by ) has been reached.

Watch-Jump Process

1. Randomly generate real numbers from respectively. Let .
2. Update with provided that both and is feasible. Otherwise, repeat step 1 until an appropriate point is found. We only replace with that whose function value is greater than or equal to .
3. Repeat the climb process by employing as an initial position.

Somersault Process

1. Randomly generate a real number from the interval (called the somersault interval), where the somersault interval can be determined by specific situations.
2. Set

where , respectively. The point is called the somersault pivot.

3. Set if is feasible. Otherwise, repeat steps 1 and 2 until a feasible solution is found.

Flowchart

Results

Parameters:

Benefits of Lighting Optimization in the Park

• Optimizing lighting in the park not only leads to greater energy efficiency by reducing electricity consumption, but also contributes to environmental sustainability by reducing carbon emissions associated with public lighting. As a fact 25% of the energy consumption is caused by lighting systems.
• Community safety is one of the most important priorities. Feeling safety There are studies which mention that better illumination will lead to improved safety. As the user's visibility increases while using common areas. If this cannot be provided, the consequences can be significant.

Light Sensing and Lighting Control

• Centralized lighting management systems further enhance the efficiency of urban lighting.
• These systems use intelligent control platforms to manage lighting networks across the city.
• By centralizing control, it becomes possible to implement precise lighting patterns tailored to the specific needs of different times of the day.

Conclusion

• In this research, we have explored how the Monkey Algorithm can be used to optimize lighting in a 7-hectare urban park in a Smart City.
• We have highlighted the benefits of this optimization, ranging from energy efficiency to improved safety and reduced light pollution.
• We also discussed important ethical and social aspects related to the implementation of this technology.

Future Work

• Using Unity to model the amenity experience in Smart Cities urban parks is a promising approach to meet the needs of Generation Z and improve the quality of life in urban environments.
• As we move towards a more connected and technological future, the adaptation and continuous development of these solutions will play a key role in creating attractive and sustainable urban spaces for future generations

La Mexicana Park

• LA MEXICANA inherits the name of one of the two sand mines that were originally on the land where the park is being built.
• When their extraction was finished, they were expropriated by the government and a few years later they were used for a real estate project that contemplated the construction of around 12 thousand homes.
• Given the lack of public spaces for coexistence, green areas and public services, the settlers of the area proposed to the government the construction of a metropolitan park.

Location La Mexicana Park

• Dorey Orozco-Paz¹
• Emilio Batres-Guillen¹
• Rafael Torres-Escobar^{1. rafael.torrese@anahuac.mx}
• Carlos Alberto Ochoa-Ortiz^{2. alberto.ochoa@uacj.mx}

Some References

1. National Advisory Committee on Standardization for the prevention and rational use of energy resources (2019). NOM-031-ENER-2019, Energy Efficiency for LED Luminaires for Street and Public Outdoor Lighting. Specifications and Test Methods: ENER
2. A. A. Issa. (2015) Energy-Efficient Lighting Control Using Particle Swarm Optimization: IEEE Transactions on Industrial Informatics
3. H. A. A. Yacout. (2013). Lighting Design Optimization Using Genetic Algorithm: Procedia Engineering

4. Kelly, J., Stevenson, T., Melissa Arnold‐Chamney, Bateman, S., Jesudason, S., McDonald, S., Williamson, I. (2022). Aboriginal patients driving kidney and healthcare improvements: Recommendations from south australian community consultations. Australian and New Zealand Journal of Public Health, 46(5), 622-629.
5. Ahmed, M. H. Rehmani. (2016). Smart Cities: A Survey: IEEE Communications Surveys & Tutorials, 3070 - 3086.
6. Zhao, R., & Tang, W. (2007). Monkey Algorithm for Global Numerical Optimization:Journal of Uncertain Systems, Vol.2, No.3, pp.165-176, 2008.