Device for Solar Powered Mobile Charging and Water Purification in

Bus Terminus with AI Monitoring

R. Senthilmurugan, B. Pradeep Kumar, K. Yokeshwaran and C. Parthiban

Department of Mechatronics, K.S.Rangasamy College of Technology, KSR Kalvi Nagar, Tiruchengode, Namakkal‑637215,

Tamil Nadu, India

Keywords: Smart Solar Street Lamp, Simulation‑Based Approach, Rainwater Harvesting, AI‑Based Monitoring, Energy

Efficiency, Sustainability.

Abstract: This article demonstrates the simulation model for a smart solar street lamp, using a rainwater collection

module and an AI monitoring module for enhanced energy usage and sustainability. Well, they show that they

are able to simulate the maximum output that solar panels can deliver in terms of power, while at the same

time they are able to gather the water that falls on the panels and direct it towards a virtual storage system –

making it sustainable for human use (drinking) and also environmental use (irrigation). Under different

environmental conditions, simulation tools are used to validate the energy efficiency of the system, the

effectiveness of water collection, and the operation reliability. An AI-based simulation model is used to

monitor the best way of solar energy output, battery health, water levels, and environmental parameters.

Machine learning algorithms which are used by the AI system to emulate anomaly detection, energy efficiency

prediction and maintenance predictions to ensure the reliability and decision-making. The research conducted

to simulate this assessment is a critical case study that shows valid data for the practicality of the technology

and efficiency of a modular solar-water-smart streetlight. These preliminary findings are conducted toward

future applications in real life, in particular for green energy and smart infrastructure projects.

1 INTRODUCTION

Due to the growing demand for green energy

sources, solar-powered streetlights have emerged as

one of the most widely implemented technologies

today (Sutopo et al., 2020; Hossain et al., 2022). But

the system is inefficient due to limitations such as

dust settle on solar panels and improper energy

storage (Cheng et al., 2020; Anguraj et al., 2022).

This project provides a simulated model for an AI-

based solar powered streetlight with a rainwater

harvesting system to mitigate these disadvantages

(Ahmed et al., 2024; Anitha Vijayalakshmi et al.,

2025). The structure on the roof aims for unlimited

exposure to solar radiation and at the same time

transport rainwater in a harvesting system for both

modeled storage and filtration. For that, the

technique provides the best possible energy

utilization and saved water and thus provides a

multifunctional and a sustainable technology, both

urban and agronomics. You are powered by an AI-

enabled monitoring system that provides real-time

insights into solar generation, battery health, water

levels, and weather conditions (Ganvir et al., 2024;

Mehta & Bhalla, 2024). The system is programmed

to leverage machine learning programs to identify

anomalies, then predict energy consumption, and

through these predictions to autodetect system

performance without the cost of installation

(Mohanty et al., 2024; PK & KRS, 2024). Simulation

also confirms the efficacy of excess solar energy in

secondary applications like charging mobility and

small auxiliary power load and proves that smart

energy management can be a reality (Michail, 2021;

Tran et al., 2021). The simulation study provided here

validates the proposed urban smart solar streetlight

infrastructure and demonstrates to provide

compelling insights on system feasibility,

sustainability, and relevancy towards real-life

implementation. The innovation specially focuses on

improving the reliability and functionality of the

accumulated solar-based infrastructure by using AI-

assisted performance improvement resource

planning; and the milestone is indeed an important

step towards the facilitation of green energy

Senthilmurugan, R., Kumar, B. P., Yokeshwaran, K. and Parthiban, C.

Device for Solar Powered Mobile Charging and Water Puriﬁcation in Bus Terminus with AI Monitoring.

DOI: 10.5220/0013890900004919

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies (ICRDICCT‘25 2025) - Volume 3, pages

10-18

ISBN: 978-989-758-777-1

technology leading to sustainable energy systems

(Meem, 2023; Shanmugasundaram et al., 2025).

2 PROBLEM IDENTIFICATION

Traditional solar streetlight systems never achieved

maximum energy potential of the solar panels as their

main objective is light (Cheng et al., 2020; Sutopo et

al., 2020). As these systems lack an effective energy

management system, surplus solar energy generated

throughout the day is wasted (Archibong et al., 2020;

Chaudhary et al., 2022). Overall efficiency of the

system is reduced since the surplus energy is wasted

instead of channeling or storing it for future use. This

solar underemployment makes available a core

lacuna in utilizing the utmost potential of solar

energy, which could otherwise be diverted towards

different public benefits. The inefficiency of

traditional solar streetlights on cloudy and foggy days

is a core weakness (Hao et al., 2022; Islam et al.,

2021). Solar panels need direct sunlight to transfer

energy, but sunlight that falls on the panels falls

immensely short under overcast, fog, or rainy

weather. Since there is not enough penetration of light

because of thick cloud cover, power generation is

significantly decreased. The underlying cause of

wasteful utilization of solar energy has not been

resolved even after implementing innovations such as

IoT integration and smart monitoring systems for the

improvement of solar street lights (Dwiyaniti et al.,

2022; Khemakhem & Krichen, 2024).

3 METHODOLOGY

3.1 Design

The designed multi-purpose rainwater harvesting

solar streetlight that will overcome the drawbacks of

traditional solar streetlights by efficient use of sun

rays and creative water management. Conical roof of

the building has a two-way advantage: it allows

efficient collection of rainwater and minimizes the

slope of solar panels for improved energy absorption.

Compared to traditional flat-panel designs, the cone's

inclined plane allows the solar panels to be installed

at angles, optimizing sunlight captured throughout the

day. Particularly in regions with fluctuating solar

conditions, the design greatly improves energy

harvests by minimizing losses and maximizing

system efficiency. With the water collection routed to

a central storage tank beneath the pole, the cone is a

rainwater harvesting device. As a reservoir for water,

the bottom ensures that water is collected and stored

in such a way that it is suitable for public sanitation,

irrigation, or consumption. A filter system can be

provided to enhance performance and have the

collected rainwater ready for different uses by the

community.

Figure 1: Design.

3.2 Components

3.2.1 Solar Panel

The project discusses the four-piece 12V, 25W solar

panel which is proposed for use in this multi-purpose

solar streetlight with rainwater harvesting to provide

maximum power output, thereby improving energy

efficiency and sustainability. The solar panels are

positioned on top of a cone structure enabling

maximum sun absorption during the day. It can even

use solar power efficiently to produce electricity

when it is stored in a battery pack and used at night.

The entire system has a capacity of around 100W A

combination of solar power and enables a 247

operation of an outdoor street light even in cloudy

weather. Incorporating a rainwater collector

mechanism, the cone structure performs two jobs in

one, channelling the rainwater that settles into a

vessel at the bottom of the pole. To make that water

pressurized and can be used at the place of drinking

or public hygiene, you need to set up a filtration plant.

Device for Solar Powered Mobile Charging and Water Puriﬁcation in Bus Terminus with AI Monitoring

3.2.2 Solar Charge Controller

In order to have effective control and use of power

produced by four 12V, 25W solar panels, a 12V solar

charge controller must be there. In order to make the

energy storage system long-lasting and work

effectively, the solar charge controller avoids deep

draining, overcharging, and voltage fluctuation in the

battery. With a bid to reduce energy wastage and

maintain a continuous power supply of electricity to

the LED streetlamp as well as other establishments

like water treatment facilities or charging facilities, it

controls the supply of electricity from the solar panels

into the battery storing facility. By delivering the

maximum power output by the solar panels, the PWM

(Pulse Width Modulation) or MPPT (Maximum

Power Point Tracking) technology of the solar charge

controller delivers maximum energy conversion

efficiency. As it enables the maximum energy to be

stored and harvested, the feature proves useful on

rainy and cloudy days when there is little or no sun

exposure. In order to avoid draining back of battery

to the panels at night, the controller further

deactivates the reverse flow of current. For smooth

and uninterrupted operation of the streetlight system,

it can also have a digital display for real-time

measurement of battery voltage, charging condition,

and load current. The system is highly sustainable and

dependable streetlighting solution because it

possesses a good solar charge controller that is also

energy efficient, prolongs the battery lifespan, and

provides a steady power supply.

3.2.3 Charger Converter

12V charger converter to take advantage of excess

energy offered by four 12V, 25W solar panels to

charge cell phones. The solar panels collect the

energy from the sun during the daytime and charge a

battery unit. A solar charge controller controls the

battery unit not to overcharge and to deliver

maximum transfer of power. For the purpose of

converting 12V DC voltage of the battery into the

required charging voltage of cellular phones and other

low-voltage electrical appliances (usually 5V DC),

the charger converter is required. The unit is equipped

with an onboard DC-DC step-down converter, i.e.,

12V to 5V USB charger module, that supplies stable

and consistent power output to enable safe and

efficient charging of the devices. For application in

sparsely populated towns with poor power supply,

rural areas, and spaces, the function is quite useful. It

facilitates smooth functioning in the supply of energy

being provided by solar panels to mobile use charging

points to drive the supply of electricity and

facilitation of use of renewable energy. Through its

integration of energy storage, cell phone charging,

and road lighting, the building optimizes the

harvesting of solar energy and is an exemplar of a

larger functional system that provides intelligent and

sustainable infrastructural services.

3.2.4 Battery

To provide efficient storage and utilization of energy,

the proposed multi-purpose solar streetlight with

rainwater collection system is equipped with a 12V,

100Ah deep-cycle battery. The system requires a

strong and reliable battery to save excess solar power

during the day since the system is comprised of four

12V, 25W solar panels. Even during rainy and cloudy

days when the solar power is low, the stored solar

energy in the battery gives an uninterrupted supply of

power to the LED streetlight and mobile charging

system. The best battery for this application is a deep-

cycle lead-acid or lithium-ion battery since it can be

recharged and discharged time and again without any

loss of efficiency and without causing any damage to

the battery, yielding long-term reliability and life.

With proper regulation of power input to prevent deep

discharge and overcharging, the solar charge

controller (12V, 10A) prolongs battery life. The

chosen 12V, 100Ah battery can provide power to

further accessories such as public charging stations

and powering the streetlight day and night.

3.3 Sensors

3.3.1 ACS712 Sensor

The major component of this AI-based monitoring

mechanism in simulated solar streetlight, defines as

ACS712 current sensor. Figure 1 shows a real-time

data connection with a meter sensor that will read the

flow of current towards the load/battery from the

solar inverter. The ACS712 is based on Hall-effect

sensing principles, in which the passage of an

electrical current produces an associated magnetic

field that is translated into a voltage signal that we

can measure. Staying updated on active output from

the solar panels, the setup can analyze solar

efficiency, battery capacity, and consumption trends.

The AI surveillance system employed machine

learning algorithms to analyze the unprocessed data

collected from the ACS712 sensors and detect power

flow anomalies, overcurrent conditions, and

inefficiencies in the system. Abnormal drops in

current are detected, which could be due to solar

panel aging, wire failure, or deteriorating battery

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COMMUNICATION, AND COMPUTING TECHNOLOGIES

performance. In another example, the AI system can

send warnings and predictive maintenance tasks upon

overcurrent or short circuiting to avoid risk of system

failure. The integration of the ACS712 sensor also

adds intelligence to the solar streetlight with

intelligent monitoring, real-time diagnostics,

improved energy usage and more reliable systems.

The simulation also allows us to experiment with the

performance of the sensor in tracking solar energy

flow and power management under different

environmental and operational conditions, and

therefore it is an essential tool for optimizing

sustainable urban and rural lighting systems.

3.3.2 State of Charge (SOC) Sensor

SOC (State of Charge) sensor is the main component

of the AI-based monitoring system of the solar

streetlight simulation and accurately measures the

charge and health of the battery. SOC sensor

estimates how much battery capacity is left in time,

making sure energy can be used, also prevents

overcharging or deep discharge that may shorten the

battery life. It does this either through voltage-based

estimation or coulomb counting or machine learning

algorithms to accurately estimate the cycles of

charge-discharge of the battery. Its SOC monitoring

integration optimizes energy management for a

longer battery life and safer operation of solar-

powered lights. The SOC sensor data is monitored by

predictive models in detecting charging

inefficiencies, unexpected drop-offs, and impending

battery failure in an AI-based monitoring system. It

can foresee battery performance patterns and

identify their deterioration or maintenance indicators

based on the AI platform. Moreover, the smart power

distribution algorithms use the SOC information to

evenly distribute power so that the solar power stored

can be used efficiently for street lights, mobile

charging and the rain water filtering system. Various

aspects of solar-powered infrastructure can be

simulated through simulation-based analysis, such as

varying weather conditions, varying energy loads,

and varying patterns of charging → SOC status

becomes an important factor towards establishing

sustainable, intelligent and autonomous solar-

powered infrastructure.

3.3.3 Ultrasonic Water Level Sensor (HC-

SR04)

One of the important sensors of the artificial

intelligence-based monitoring system of simulated

rainwater harvesting solar streetlight is the Ultrasonic

Water Level Sensor (HC-SR04). To use the

harvested water for drinking water purification,

irrigation, and public use, the module is used to sense

and monitor the water level that is stored in the

rainwater storage tank. The HC-SR04 works based

on the ultrasonic waves, which sends out a high-

frequency sound wave through the air, which reflects

off of the water level and back to the sensor. By

processing the time of echo return, it can monitor

storage capacity and water level in real time. The

water level is continuously checked against the HC-

SR04 sensor in identification of low levels of water,

overflow hazards and outsider usage with an

integrated AI powered IP cloud based surveillance

system. AI systems are used to analyze past history

of water level movement as per trends for analyzing

likely consumption in future so that water is fitfully

controlled without any wastage. Moreover, the

system also can generate alerts and performing

automated control actions like turning on a water

pump when a water level drops below a defined

threshold or closing an inlet valve when the tank

nears-to-full. Simulation is used to model and

optimize the performance of the rainwater harvesting

system for different weather, rainy patterns and water

use conditions. The system is therefore not only

sustainable, resource conserving and independent,

but it is also a smart choice for urban as well as rural

infrastructure with the incorporation of HC-SR04

sensor and AI surveillance.

3.3.4 TDS (Total Dissolved Solids) Sensor

One of the key sensors in the AI-based monitoring

system of the simulated solar streetlight rainwater

harvesting, monitoring the quality of water stored for

drinking, irrigation and other public use is that of the

TDS (Total Dissolved Solids) sensor. The sensor

measures total dissolved solids, a measure of

minerals, salts, and impurities, in harvested

rainwater. It operates on the principle of the

conductivity of water, which increases with increased

dissolved material. Continuously monitored TDS is

maintained by the system to ensure its drinking water

is safe and complies with quality. As soon as TDS

sensor readings are available in an AI system,

machine learning algorithms are utilized to

recognize trends of contaminants in the water,

predicting filter schedule maintenance, and

generating real-time water quality reading. The AI

system may be programmed to provide automatic

alerts in case TDS levels go beyond safety limits,

which may activate warning or filtration or

purification systems. It can also scan historical trends

to determine the expected changes in water quality as

Device for Solar Powered Mobile Charging and Water Puriﬁcation in Bus Terminus with AI Monitoring

a function of climate variables including rainfall

trends, and storage periods. Using simulation testing,

the various scenarios of contamination and the

models of filtration efficiency can be analysed and the

most sustainable rainwater harvesting system

designed and optimised for self-sufficient water

management. The incorporation of the TDS sensor

with AI-based monitoring ensures that the solar

streetlight system not only promotes public health but

also water conservation and better ecological

conditions, distinguishing it as unique and adaptable

infrastructure for cities.

3.3.5 DHT22 (Digital Temperature &

Humidity Sensor)

The DHT22 (Digital Temperature & Humidity

Sensor) is an important part in the AI monitoring

system of simulated solar streetlight with rainwater

harvesting to provide real-time reading of

environmental conditions. This sensor can provide

accurate temperature and humidity measurements

which is important for measuring solar energy

generation and rainwater harvesting efficiency. Using

high-accuracy digital outputs, DHT22 works on

principles of a capacitive humidity sensor, and a

thermistor. By continuously measuring temperature

changes and humidity in the air, the system could

assess how weather acted on the efficiency of its

solar panels and its ability to collect water. This is

where the AI-monitoring system comes on board and

the DHT22 sensor data is filtered through ML

algorithms to check the performance of the system on

varying weather conditions. Solar Panels Efficiency

Management: The AI system can detect efficiency

losses in solar power panels due to high temperature

and adapt energy management to optimize the

battery usage. Similarly, humidity levels help analyze

if it will rain, so rainwater collection systems are not

out of service. By utilizing both historical weather

data and real-time sensor data, the AI model is able to

forecast trends in solar energy generation as well as

water storage capacity, ultimately leading to

enhanced reliability and sustainability of the system.

As such, through simulation analysis, different

climate conditions can be modelled, creating a more

robust smart solar streetlight under changing

environmental conditions. It creates a good

infrastructure tool for monitoring climate with

renewable energy optimization, resource

minimization through AI based user friendly

platform integrated with DHT22 sensor for sense

detection and monitoring.

3.4 Raspberry Pi 4

The AI-based monitoring system of the rainwater-

harvesting solar streetlight simulator has a Raspberry

Pi 4 Model B as the central processing unit. Being a

key element in system performance improvement, it

is responsible for sensor data collection, AI-based

anomaly detection, and cloud remote monitoring.

Need the calculation power to handle real-time sensor

data from the ACS712 current sensor, battery SOC

sensor, HC-SR04 water level sensor, DHT22

temperature sensor, PIR motion sensor, TDS water

quality sensor, quad-core ARM Cortex-A72

processor (1.5 GHz)+4GB/8GB LPDDR4 RAM. The

Raspberry Pi’s 40-pin GPIO interface makes it easy

to connect a variety of sensors to ensure monitoring

of solar generation, battery performance, water levels

and weather. Raspberry Pi 4 supports built-in Wi-Fi

(802.11ac) and Gigabit Ethernet that allows

streaming of data to cloud interfaces in real-time like

firebase, thingspeak or AWS IoT for remote

monitoring and predictive analysis. Other features

like USB 3.0 ports and microSD or SSD expandable

storage make it suitable for efficient data logging and

AI model execution. Machine learning libraries such

as TensorFlow and OpenCV also support Raspberry

Pi 4 for anomaly detection and predictive

maintenance to ensure system reliability. Raspberry

Pi 4 can handle real-time conditions though

simulation-based analysis in an optimized manner

promotes to system performance, energy

management, and looks after the sustainability of the

smart solar streetlight system. Thus, all these with

the integration of edge computing, AI monitoring,

along with IoT connectivity make Raspberry Pi 4

Model B a powerful and efficient monitoring system

that is highly scalable, and provides smart

infrastructure for urban and rural amenities to

generate a strong infrastructure.

3.5 Circuit Diagram

Below Figure 2 is a circuit diagram of how a

monitoring system is set up in a rainwater harvesting

solar streetlight based on AI using Raspberry Pi 4

Model B as the processor. The system incorporates

ACS712 (current sensor), Battery SOC sensor,

DHT22 (temperature and humidity sensor), and TDS

water quality sensor, along with an MCP3008

analog-to-digital converter (ADC) that offers an easy

interface to the components with Raspberry Pi. The

current taken from the load and battery to the solar

panel is measured using ACS712 current sensor

where best energy management is well catered for.

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SOC sensor is designed to monitor and measure the

state of charge which helps the system compute the

battery life and performance. DHT22 sensor offers

real time temperature and humidity that are extremely

important for the process of environmental

monitoring and how they contribute in solar energy

generation. Also, TDS sensor aids in judging how

pure the rainwater collected is so one can determine

if it can be used for irrigation and drinking.

There is no on-board analog input in Raspberry Pi

4; therefore, MCP3008 ADC helps in the conversion

of analog sensor outputs into digital values in order to

make it easy for interfacing with the Raspberry Pi

GPIO pins. SPI protocol is used for Raspberry Pi to

talk to MCP3008 in an effort to move data effectively

and at high speed. The system also includes real-time

cloud monitoring and data logging, realized through

ESP8266/ESP32 Wi-Fi module uploading sensor

data to IoT platforms such as Firebase or Thingspeak

for remote processing and AI-driven predictive

analysis. This smart circuit design is enabled to

facilitate smart street lighting, smart energy metering,

and water quality monitoring, thereby making it an

immensely useful and sustainable smart

infrastructure solution. Leveraging the AI-driven

insights, the platform is enabled to identify power

instability, predict battery ageing, maximize energy

from solar panels, and track water resource planning,

thereby leading the green and sustainable city

infrastructure.

Figure 2: Circuit Diagram.

3.6 Proposed Solution

This design aims to create a multi-purpose solar

streetlight that combines rainwater harvesting, AI-

based monitoring, and increased solar energy output

using simulation-based analysis. The system includes

four solar panels, each with a 12V, 25W rating,

placed in four disparate directions on a cone-shaped

tip atop the pole. This innovative design has two

functions by providing optimum light absorption for

energy production while, at the same time, harvesting

rainwater into a storage tank. The rainwater stored

can be employed for business use in public spaces,

and in urban and rural areas, an intrinsic filtration unit

renders it fit for human consumption. To guarantee

effective system operation and resource utilization, an

AI-powered monitoring system is integrated to

monitor solar energy generation, battery condition,

water levels, and weather conditions. The AI platform

uses virtual sensor data from modules like the

ACS712 current sensor (solar panel and battery

monitoring), battery SOC sensor (charge level

monitoring), HC-SR04 ultrasonic sensor (water level

sensing), TDS sensor (water quality analysis), and

DHT22 temperature & humidity sensor (environment

monitoring). The data is processed through machine

learning algorithms to identify anomalies, project

power consumption patterns, and enhance system

efficiency in a simulated environment.

The double-chambered pole, one for water

passage and another for electrical cable, provides

appropriate storing, filtering, and secure energy

transfer. A 12V solar charge controller is emulated to

manage power flow between the solar panels, 12V

battery, and the loads. The controller limits

overcharging, deep discharge, and voltage

Device for Solar Powered Mobile Charging and Water Puriﬁcation in Bus Terminus with AI Monitoring

fluctuations, and offers real-time power status

indicators for AI-based performance analysis. The

accumulated solar power is thereafter diverted for

mobile charging and street lighting by a DC-to-DC

converter for stepping down the voltage from 12V to

5V to facilitate the charging of mobile phones and

other small electronic equipment through USB

interfaces. Through simulation tests, AI-based

models evaluate energy efficiency, environmental

adaptability, and power optimization and enable

performance checks under varying weather

conditions and load conditions. This makes the

system scalable, sustainable, and optimized prior to

actual implementation in the real world. By

integrating AI monitoring with renewable energy and

water resource management, this smart solar

streetlight system provides a smart, green, and

community-focused infrastructure solution.

Figure 3: Block Diagram.

A hybrid simulation-based arrangement of

utilizing solar energy and rainwater with AI based

monitoring that helps in effective utilization of

resources is depicted in form of a Figure 3 block

diagram. A solar panel generates electricity, with a

controller that regulates power to prevent excess

variation, while a rainwater collector directs water

from a filtration unit into a storage unit. Electronic

appliances and lighting are supplied with stable

power by means of a DC-to-DC converter. The AI

monitoring system use real-time analytical data to

test energy efficiency, battery levels, water levels,

and power consumption to improve energy flow,

detect abnormalities, and predict maintenance needs,

Guide. To implement the support of the system, I

constructed the system you can see now with the help

of some specific data. This approach enhances

sustainability and efficiency before actual

implementation.

4 RESULTS AND EVALUATION

The solar streetlight system with integrated AI was

simulated and tested successfully, showcasing

immense energy efficiency, reliability, and

sustainability. Simulation-based methodology

proved the system effective in maximizing the use of

solar energy, managing power distribution, and

effectively handling water resources. The system

comprising four 12V, 25W solar panels captured and

converted solar energy efficiently under changing

environmental conditions and provided continuous

power supply even at low light. The 12V battery

storage system, regulated by a solar charge

controller, held the optimal charges, avoiding deep

discharge and overcharging. Simulation of the DC-

DC converter ensured that the surplus energy was

effectively diverted to mobile charging use,

promoting multi-functionality to the system. The

rainwater harvesting system, simulated through the

use of HC-SR04 ultrasonic sensors for monitoring

the water level and TDS sensors for water purity

monitoring, accurately proved efficient collection

and purification procedures.

Figure 4: Simulated Graph.

The AI monitoring system with the aid of

machine learning algorithms successfully processed

sensor readings from ACS712 (current), SOC

(charge of the battery), HC-SR04 (level of water),

and TDS (purity of water). The AI model

successfully identified anomalies in performance,

regulated the flow of energy, and scheduled

maintenance, thus providing reliability and long-term

functionality to the system. The successful

simulation outputs in Figure 4 ensure that this AI-

driven solar streetlight system is scalable and

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applicable to smart urban and rural infrastructure

projects.

5 DISCUSSION

Results of these simulations show that, due to the

high technology usage of the proposed IoT-enabled

solar streetlight system, an efficient, smart,

specialized, and sustainable solution in comparison

with the existing solar streetlights can be realized.

With AI-based monitoring in real time, the system

effectively optimizes energy consumption while

improving power management, guaranteeing that

resources are used optimally. Unlike traditional

models with merely basic lighting application, the

proposed system adopted multi-functional features

such as mobile charging, rave water collection and

predictive analytics, therefore a self-supported and

intelligent infrastructure solution. Incorporation of

machine learning algorithms enables the system with

a much larger coverage area for inefficient

generation of solar energy, predictive maintenance

for battery deterioration and optimizing water

availability.

Conducting AI-based analysis is for simulation

validation to show that system can deployed for real-

time applications allowing reduced usage of fossil

fuels and espoused solar energy. More renewable

energy and use of sustainable resources would

increase while providing a true alternative using AI-

tech simulation in solar systems. The simulated

validated AI-enabled solar streetlight system is thus

a more efficient, reliable and sustainable scalable &

future-proof solution for smart city developments and

rural electrification ventures.

6 CONCLUSIONS

The suggested AI-integrated solar streetlight system

promotes energy efficiency, dependability, and

multifunctionality using simulation-based analysis.

Using four 12V, 25W solar panels mounted on a

cone-shaped design, the system offers maximum

solar absorption with the integration of rainwater

harvesting for public and commercial applications.

The monitoring system powered by AI ensures

optimized flow of energy, battery storage, and water

management, providing real-time performance

observation and predictive maintenance. A 12V solar

charge controller and storage battery ensure a

continuous power supply, even during cloudy

weather. The DC-DC converter allows maximum

utilization of excess solar power for mobile charging,

and hence the system is ideal for urban as well as

rural applications. The double-compartment pole

design enhances functionality and maintenance by

ensuring effective electrical wiring and water

storage.

The AI platform detects anomalies, controls

power distribution, and predicts maintenance needs,

thus achieving sustainability and resource-efficient

use. Simulation results confirm that the system

effectively reduces energy wastage, optimizes

renewable energy utilization, and enables smart

infrastructure development. Equipped with lighting,

mobile charging, and water conservation features,

this AI-powered solar streetlight is an innovative and

scalable solution for sustainable urban and rural

development.

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