Electricity Usage Research Articles

Characterizing residential sector load curves from smart meter datasets

Understanding how and when residential electricity is used throughout the day is integral to the successful implementation of potential residential demand management strategies. Our analysis characterizes the daily hourly load profiles of approximately 160,000 residential electricity customers across the Southern California Edison (SCE) service area during the period spanning 2015 to 2016 and 2018 to 2019 across weekends, weekdays, seasons, and climate zones. We find that total daily electricity usage was highest in the hottest months of the year compared to milder months, particularly for households located in the hottest climate zones. The most energy-consumptive hours occurred during the mid-afternoon during the hottest months, in contrast to early evening high consumption in cooler months. We find that customers with average daily consumption at or above the 80th percentile cumulatively consume over 40% of electricity during the hottest months of the year residential load, while the bottom half of customers cumulatively consume <25% of the total residential load. The disparities in electricity usage across SCE households are higher in the mid-day, especially in milder months across all regions, and in mild climate zones compared to hotter climate zones since loads are not as dependent on high HVAC loads.

Sustainable energy consumption – Empirical evidence of a household in Poland

There is an expected increase in the demand for electricity from households in European countries. The outcome of the growing proportion of renewable energy sources in the energy supply mix should result in improved customer awareness regarding their electricity usage. Additionally, they must assume a more proactive role in overseeing their electricity usage. Innovation and pollution management have become crucial catalysts for promoting sustainable economic development and environmental protection. This is a response to the increasing threats posed by climate change. This study addresses this existing research gap in sustainable energy consumption. The research aims to examine the advantages of replacing traditional sources of heating with renewable ones. The empirical case study of households in Poland serves as an illustration of the theoretical foundation. The purpose of the study entails the construction of a multi-equation econometric model describing the mechanisms of electricity consumption in a specific household. The model hypothesis indicates that the model should take the form of a system of interdependent equations. The following will be described: the monthly volume of electricity consumption, the cost of this energy payment (in PLN), and the price per one MWh in a given month. Monthly time series from September 2015 to June 2023 were used. The resulting time series amounted to a count of 94 statistical observations. The results in the form of a multi-level empirical econometric model indicate that the economic, environmental, and social benefits of using renewable sources of heating can make a significant contribution to reducing fossil fuel consumption and CO2 emissions. In the context of growing environmental awareness and the need to reduce greenhouse gas emissions, heat pumps are becoming an increasingly important element in the discussion of the future of heating. Their role in transforming the energy sector in the direction of sustainability is crucial.

Geographic Equity and Environmental Sustainability of Conference Models: Results of a Comparative Analysis.

We conducted a comparative analysis of in-person, virtual, and hybrid conferences on tuberculosis hosted by Keystone Symposia and examined the number of participants, their country of residence, carbon dioxide equivalents (CO2e) produced, and participant impressions regarding scientific quality. Data were available from three in-person meetings, one virtual meeting, and one hybrid. The virtual conference hosted 2.5-fold more participants compared with the in-person conferences (842 versus an average of 328) from more than double the number of countries (68 versus an average of 33). The virtual conference attracted 4.5-fold more participants from countries with a high burden of tuberculosis, compared with the average in-person conference (209 versus an average of 46). For in-person meetings, an average of 79% of participants were based in high-income countries. For the virtual meeting, 53% of participants were from high-income countries, and 47% from low- and middle-income countries. For the hybrid conference, there were 465 participants from 43 countries, of which 289 attended in person from a total of 20 countries, and 176 participated virtually from 34 countries. Of those who took part in person, 91% were from high-income countries. The average CO2e emissions from an in-person conference was 696 tons of CO2e, with 96.0% from air travel. The virtual meeting produced 0.48 ton of CO2e from electricity usage, a 1,450-fold decrease compared with in-person events. Virtual conferences scored a content quality rating of 87.3% to 90.8% compared with a range of 86.4% to 92.2% for in-person conferences.

Evaluating battery electric vehicle usage in the EU: A comparative study based on member state energy mixes

The transport sector is undergoing a major transformation, as battery electric vehicles (BEV) are gaining ground. Therefore, assessing the sustainability aspects of their use is crucial to obtaining a clear picture of the sector. This article aims to meet this requirement by using European Union (EU) data for the period 2011 to 2021 and focuses not only on EU-27 aggregates but also on each member state separately. For the evaluation, a well-to-wheel (WTW) method was used to calculate two parameters: energy-specific CO2 emissions (ε) and total efficiency of energy conversions, transmission, and battery (ηtotal). For these values, the annual electricity mixes of the countries were tracked in 5 + 1 categories (combined cycle gas turbine (CCGT), thermal power plant, biofuels, nuclear power plant (NPP), renewables, and imports). The calculated results were illustrated by sustainability matrices describing the former and current positions of the countries. The EU-27 aggregate achieved a 0.04 increase (from 0.37 to 0.41) in total efficiency and a 29 gCO2/MJmotion decrease (from 113 to 84 gCO2/MJmotion) during the period. This ε value for 2021 was around half the world average. However, very significant differences were identified between member states, which are also assessed in the article with special emphases on the five most populated EU countries (Germany, France, Italy, Spain, and Poland).

Enhancing smart grid electricity prediction with the fusion of intelligent modeling and XAI integration

This study examines the vital role of accurate load forecasting in the energy planning of smart cities. It introduces a hybrid approach that uses machine learning (ML) to forecast electricity usage in homes, improving accuracy through the extraction of correlated features. The accuracy of predictions is assessed using loss functions and the root mean square error (RMSE). In response to increasing interest in explainable artificial intelligence (XAI), this paper proposes a framework for predicting energy consumption in smart homes. This user-friendly approach helps users understand their energy consumption patterns by employing shapley additive explanations (SHAP) techniques to provide clear explanations. The research uses gradient boosting and long short-term memory neural networks to forecast energy usage. In the context of sustainable urban development, it emphasizes the importance of conserving energy in homes. The paper explores AI and ML methods for predicting residential energy use, aiming to make socially meaningful impacts. It highlights the need to understand the factors affecting predictions to improve the accountability, reliability, and justification of decisions in energy optimization. Explainable AI techniques are used to gain insights into the prediction models and identify factors influencing household energy consumption. This research aids in decision-making processes related to electricity forecasting, advancing discussions on intelligent decision-making in power management, especially in smart grids and sustainable urban development.

Anomaly Detection Model of Time Segment Power Usage Behavior Using Unsupervised Learning

&lt;p&gt;In Taiwan, the current electricity prices for residential users remain relatively low. This results in a diminished incentive for these users to invest in energy-saving improvements. Consequently, devising strategies to encourage residential users to adopt energy-saving measures becomes a vital research area. Grounded in behavioral science, this study introduces a feasible approach where an energy management system provides alerts and corresponding energy-saving recommendations to residential users upon detecting abnormal electricity consumption behavior. To pinpoint anomalous electricity usage within specific time segments, this research employs an unsupervised machine learning method, developing an anomaly detection model for the overall electricity consumption behavior of residential users. The model focuses on analyzing 2-hour intervals of electricity consumption, enabling more effective detection of abnormal usage patterns. It is trained using power consumption data collected from five actual residential users as part of an experimental study. The results indicate that the proposed anomaly detection model achieves performance metrics such as Precision, Recall, and F1-score of 0.90 or above, showcasing its potential for practical implementation.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;

Design of the Jlantah Reservoir Mini-Hydro Power Plant, Karanganyar Regency, Central Java Province

An increase in the number of electricity users in Jatiyoso District, Central Java, has occurred in the last few years. In 2020 there were 12,773 families using electricity in Jatiyoso District, where there has been an increase of 79 families using electricity compared to 2019. This increase in electricity demand should be balanced with an increase in sources of electrical energy for the community. Therefore, Jlantah Mini-hydro Power Plant is expected to become a source of electrical energy for the local community. The normal water level of the reservoir is planned at an elevation of +691.00 MASL and the elevation of the intake structure at +661.00 MASL. The total dead storage of the reservoir is 1.66 million and the total effective storage is 9.16 million. The powerhouse elevation planning is carried out by analyzing the 100-year return period of flood in the river around the powerhouse. The powerhouse is planned at an elevation of +624.00 MASL. The turbines used in the powerhouse are 2 turbines. Turbine 1 with HLA550-WJ-55 series and turbine 2 with HLA678-WJ-50 series. The average power generated in a normal year (2013) is 516.44 kW with a total energy of 4.47 GWh. The construction cost of the Jlantah PLTM is estimated at IDR 18,795,716,076.00 with normal annual income (2013) of IDR 4,060,935,522. With this income, the NPV value is IDR 4,461,454,224, IRR is 12.96%, BCR is 1.24, and PBP is 12.78 years, so this project is said to be economically feasible.

Seoul National University&amp;apos;s Energy Consumption and Greenhouse Gas Emissions Characteristics in Preparation for the 2050 Carbon Neutrality Transition and the 2030 NDC

In this study, the characteristics of energy consumption and greenhouse gas emissions at Seoul National University (SNU) were presented, and Energy·Greenhouse Gas Balance Model of University Campus was proposed based on this analysis. Additionally, the conditions and features required to achieve SNU's 2030 National Determined Contribution (NDC) goals in the building sector and aim for a carbon-neutral campus by 2050 were examined. The Energy·Greenhouse Gas Balance Model of University Campus is expected to be beneficial for preparing nationwide universities for the transition to carbon-neutral campuses. As of 2021, SNU emitted approximately 141 kilotons of CO&lt;sub&gt;2eq.&lt;/sub&gt;, with a greenhouse gas emission ratio of around 8:2 for electricity and gas usage. The total energy consumption at SNU was 809 GWh (69,600 TOE), considering conversion losses, and the electricity usage was 659 GWh. The actual energy consumption on the SNU campus included 247 GWh of electricity and 151 GWh of gas and petroleum usage. Energy consumption in SNU, particularly in engineering and science research facilities, accounted for 46% of the overall energy consumption, emphasizing the need for greenhouse gas reduction through understanding the energy usage characteristics of these facilities. To achieve SNU's goals of reaching the 2030 NDC building sector targets and a carbon-neutral campus by 2050, it is crucial not only to enhance overall energy efficiency and manage total energy consumption but also to decarbonize the electricity used at SNU.

Factors determining people’s use of renewable energy

Although awareness and preference for renewable energy is increasing, users still pay higher prices for renewable energy than for fossil fuels. Therefore, assessing renewable energy demand is necessary to find new opportunities and drivers from buyers in the upcoming flexible and competitive energy market in the context of social and environmental responsibility to enhance consumer market. A clear trend is taking place among electricity users and small-scale electricity generation investors. The development of decentralization in global power production and distribution is also quite similar to this trend. Although many of these studies have noted this trend, there are still limitations that have not been further clarified. One thing to note is that when people invest in renewable energy to make a profit, there may be some risks due to the incomplete legal and technical framework. For example, investors can ignore strict infrastructure standards, use cheap, low quality products or the ability to reduce subsidies and replace them with more market prices; Or they can aim to make a profit immediately. Therefore, people must pay attention and understand their intention to use renewable energy. This reduces business risk by supporting the development of financial and investment solutions for citizens. At the same time, setting standards and establishing a comprehensive communication plan will provide the basis and tools for the sustainable and effective development of renewable energy by the people.

IoT Smart Electricity Energy Meter

Abstract: The escalating demand for electric energy, coupled with concerns over sustainability and resource depletion, underscores the need for innovative solutions to monitor and manage electricity usage. This project proposes an Internet of Things (IoT) based household appliance monitoring system to address these challenges. By integrating hardware, software, and cloud technologies, the system provides real-time insights into energy consumption at both the household and device levels. Additionally, it offers transparency and accountability by displaying energy usage and costs, empowering users to make informed decisions to optimize their electricity expenses. Furthermore, the system extends its application to electric vehicle (EV) charging, enhancing efficiency and convenience in this growing domain. Overall, this IoT-based energy monitoring system represents a crucial advancement towards a more sustainable and efficient energy future.

Energy efficiency in architecture: Strategies and technologies

Energy efficiency in architecture is a critical consideration in the design and construction of buildings, aiming to reduce energy consumption and minimize environmental impact. This abstract explores various strategies and technologies that can be implemented to enhance energy efficiency in architecture. The importance of energy efficiency in architecture lies in its potential to reduce greenhouse gas emissions, lower energy costs, and create healthier indoor environments. Achieving energy efficiency in architecture involves a combination of passive design strategies, such as orientation, shading, and natural ventilation, as well as active technologies, including high-performance insulation, energy-efficient lighting, and renewable energy systems. Passive design strategies are fundamental to energy-efficient architecture, utilizing the natural elements of sunlight, shade, and airflow to minimize the need for mechanical heating, cooling, and lighting. Proper building orientation, effective shading devices, and strategic placement of windows and openings can maximize natural light and ventilation, reducing the reliance on artificial lighting and mechanical ventilation systems. In addition to passive design strategies, active technologies play a crucial role in enhancing energy efficiency in architecture. High-performance insulation materials, such as aerogel and vacuum insulation panels, can significantly reduce heat loss and gain through building envelopes, improving thermal comfort and reducing energy consumption. Energy-efficient lighting systems, such as light-emitting diodes (LEDs) and daylight harvesting systems, can reduce electricity usage for lighting, while renewable energy systems, such as solar photovoltaic panels and wind turbines, can generate clean energy on-site, further reducing reliance on fossil fuels. Overall, energy efficiency in architecture requires a holistic approach that considers both passive design strategies and active technologies. By incorporating these strategies and technologies into building design and construction, architects and designers can create buildings that are not only environmentally sustainable but also comfortable, healthy, and cost-effective for occupants.

Monitoring System for Electrical Energy Use and Charging Electricity Tokens Based on Website and Whatsapp Application

The large number of equipment or electrical loads used causes large amounts of electrical energy usage. To determine the amount of electrical energy used, you can install an electrical energy meter whose results can be monitored via an LCD display. In this research, a website-based application was designed that can monitor electrical energy usage, by providing information in the form of voltage, current, power, and active power usage over time. Apart from that, this website can also top up electricity tokens as a feature to limit the use of electrical energy that will be used by users and is equipped with a notification message for remaining electricity tokens to the Whatsapp application. This system was developed using an ESP32 microcontroller which will provide data, process data, and present data from instrument measurements, then send the measurement results data every 15 seconds to the monitoring website. From the test results, the tool can measure electrical quantities such as current and voltage. The tool can also send measurement results to the website and store measurement data in a database. Test results for sending notification messages, the message was successfully sent to WhatsApp media if the electricity token met the specified conditions.

Geothermal Heat Pump for Space Cooling and Heating in Kuwaiti Climate

Kuwait stands as one of the hottest locations globally, experiencing scorching temperatures that can soar to 50 °C during the summer months. Conversely, in the winter months of December and January, temperatures may plummet to less than 10 °C. Maintaining a comfortable temperature indoors necessitates a substantial amount of energy, particularly during the scorching summer seasons. In Kuwait, most of the electrical energy required for functions such as air conditioning and lighting is derived from fossil fuel resources, contributing to escalating air pollution and global warming. To reduce dependence on conventional energy sources for heating and cooling, this article presents a case study to explore the potential of using geothermal energy for space heating and cooling in Kuwait. The case study involves utilizing a geothermal heat pump (water-sourced heat pump) in conjunction with a vertical-borehole ground heat exchanger (VBGHE). The mentioned system is deployed to regulate the climate in a six-floor apartment block comprising a small two-bedroom apartment on each level, each with a total floor area of 57 m2. Two geothermal heat pumps, each with a cooling capacity of 2.58 kW and a heating capacity of 2.90 kW, connected to two vertical-borehole heat exchangers, were deployed for each apartment to maintain temperatures at 22 °C in winter and 26 °C in summer. The findings indicate that the estimated annual energy loads for cooling and heating for the apartment block are 42,758 kWh and 113 kWh, respectively. The corresponding electrical energy consumption amounted to 9294 kWh for space cooling and 113 kWh for space heating. The observed peak cooling load was approximately 9300 kJ/h (2.58 kW) per apartment, resulting in a power density of 45 W/m2. Moreover, the HP system achieved a 22% reduction in annual electric energy consumption compared to conventional air conditioning systems. This reduction in electric energy usage led to an annual CO2 reduction of 6.6 kg/m2.

Optimal Planning of Battery Swapping Stations Incorporating Dynamic Network Reconfiguration Considering Technical Aspects of the Power Grid

In order to drive electric vehicle adoption and bolster grid stability, the incorporation of battery swapping stations (BSSs) into the power grid is imperative. Conversely, network reconfiguration plays a crucial role in optimizing energy exchange within the power network, ensuring its economical and safe operation. Therefore, this study proposes an optimal planning method for battery swapping stations that integrates dynamic power distribution network reconfiguration while addressing technical aspects of the grid. The proposed method aims to concurrently optimize the placement and capacity of battery swapping stations, along with power distribution network reconfiguration, to enhance grid reliability and efficiency. The optimization model accounts for various factors including power quality, technical considerations, grid limitations, and operational expenses. A multi-objective optimization framework is devised to simultaneously reduce system losses, improve voltage stability, and mitigate environmental impacts of the power distribution network incorporating DG units. Case studies are conducted to illustrate the efficacy of the proposed approach in enhancing overall grid performance while accommodating the integration of battery swapping stations. The findings underscore the significance of considering technical factors and grid reconfiguration in battery swapping station planning to achieve optimal system operation and maximize benefits for electric vehicle users and grid operators alike.

A comprehensive study on electricity utilization and CO2 emissions in Sami Laris Swalayan, Cilacap, Indonesia

In today's world, with an increased focus on environmental awareness and sustainability, mitigating carbon footprint has emerged as a crucial goal for businesses across various sectors, including supermarket chains. This research centers on Sami Laris Swalayan, a prominent and widely recognized supermarket chain, to comprehensively analyze its electricity utilization patterns and the corresponding carbon dioxide (CO2) emissions. By delving into electricity utilization data spanning a specific period, this study identifies peak demand periods, explores energy-efficient opportunities, and assesses the potential for integrating renewable energy sources. The research underscores the significance of reducing CO2 emissions, advocating sustainable energy practices within the supermarket chain, contributing to environmental conservation efforts, and fostering a culture of responsible energy management. The investigation begins with a thorough analysis of Sami Laris Swalayan's electricity utilization, seeking to understand the varying trends and patterns in energy consumption. The data above shows the fluctuating electricity consumption of the company, with the highest figure recorded in April, reaching 26,965 kWh. The high electricity usage is attributed to the total operational hours from 09:00 to 21:00 and the addition of other facilities such as the accessory room, bakery outlet, and coffee outlet. In contrast, the lowest consumption was observed in February, with 23,796 kWh, which was influenced by the implementation of Large-Scale Social Restrictions (PSBB) at the end of January 2021. During this period, the operational hours were shortened from 09:00 to 19:00, leading to a decrease in electricity consumption the following month.

LR-GLASSO Method for Solving Multiple Explanatory Variables of the Village Development Index

Sustainable Development Goals (SDGs) are developments that maintain sustainable improvement in society’s economic, social, and environmental welfare. Kemendes PDTT RI has issued the Village Development Index (VDI) to provide information and the status of village progress to support village development to improve the National SDGS. Modelling with multiple explanatory variables causes a high correlation between explanatory variables, multicollinearity, and coefficient estimation results, which have major variance and overfitting in prediction results. The modeling solution uses LASSO and GLASSO. Binary categorical response data uses binary logistic regression (LR), so LR-LASSO and LR-GLASSO are used. North Maluku Province has a VDI ranking that tends to fall in 2018-2022. Based on the mean and variance of the coefficient estimation results and misclassification errors, LR-GLASSO is better than LR-LASSO and LR. LR-GLASSO is recommended for analyzing VDI data because it has many explanatory variables, and the correlation between them is relatively high. The recommendation given by the Indonesian government, if it is to increase the status of VDI in Indonesia, especially in North Maluku province, is to increase the number of electricity users, food and beverage stalls, and other cooperatives. The Indonesian government also needs to pay attention to villages relatively far from the regent's office, between food and beverage stalls, and supporting health centres, because they still need to be developed compared to other villages, and more than 50% of villages are underdeveloped. If the Village SDGs are formulated through increasing VDI status, it will support the achievement of SDGs goals nationally.

Enhancing Electric Vehicle Performance with a Hybrid PI-Sliding Mode Controller for Battery Supercapacitor Integration

Nowadays, most of the works are based on electric vehicle usage for sustainable transportation using traditional energy storage device, such as battery. Usage of batteries in electric vehicles is having several disadvantages, for example, life span, temperature, and charge estimation. In this paper, a novel control scheme for battery and supercapacitor- (SC-) based hybrid energy storage system (HESS) using hybrid proportional and integral- (PI-) sliding mode control (SMC) for electric vehicle (EV) applications is introduced and implemented. This HESS with hybrid controller proves the usage of batteries in EVs to its fullest potential. The conventional control strategy for HESS follows two-loop voltage and current PI controllers with low-pass filter (LPF) and involves tuning of multiple control parameters with variations of source and load disturbances. Performance of the system is affected by tuning PI controller constants. A slow response time with linear PI controllers is long which is not advisable for starting and sudden jerk conditions of EVs. Moreover, the PI controller performance is affected by the system parameter variations during load changes. And these parameters are dynamic in nature due to nonideal conditions. In this paper, a hybrid PI-sliding mode controller (SMC) scheme is designed to control the bidirectional DC-DC converters to overcome the drawbacks of aforementioned issues. The combined PI-SMC controller reduces the tuning effort and reduces the effect of shift in operating point in controller performance. Linear modeling is done using small signal analysis for each subsystems. Permanent magnet synchronous machine (PMSM) is used as electric vehicle. The entire system and its controllers are simulated using MATLAB-Simulink, and detailed comparison is carried between conventional PI and proposed hybrid PI-SMC scheme to regulate the DC link voltage. The results are tabulated and show that the hybrid PI-SMC scheme outperforms in transient and steady-state conditions than the traditional PI controller. A scaled hardware prototype of 48 W set-up is developed using dSPACE-1104, and the experimental results have been carried out to verify the proposed system’s feasibility.

A data-driven ensemble technique for the detection of false data injection attacks in the smart grid framework

The major component of the smart grid (SG) is the advanced metering infrastructure (AMI), which monitors and controls the existing power system and provides interactive services for invoicing and electricity usage management with the utility. Including a cyber-layer in the metering system allows two-way communication but creates a new opportunity for energy theft, resulting in significant monetary loss. This article proposes an approach to detecting abnormal consumption patterns using energy metering data based on the ensemble technique AdaBoost, a boosting algorithm. Different statistical and descriptive features are retrieved from metering data samples, which account for extreme conditions. The model is trained for malicious and non-malicious data for five different attack scenarios, which are analyzed on the Irish Social Science Data Archive (ISSDA) smart meter dataset. In contrast to prior supervised techniques, it works well even with unbalanced data. The efficacy of the proposed theft detection method has been evaluated by comparing the accuracy, precision, recall, and F1 score with the other well-known approaches in the literature.

On-Grid Photovoltaic (PV) - Battery - PLN for Smart Home System

Electricity is one of basic human needs. However, PLN's ability to meet customer demands is hampered by its limitations. On the other hand, the sunny geographical advantage of Makassar city can be utilized as a new renewable and environmentally friendly energy source in a smart home. Smart house is a family residence that is able to synergize electricity usage based on the habits of its residents with the help of smart technology so that comfort, safety and efficiency of using electrical energy are obtained. The utilization of solar cell hybrid power – battery – PLN can be implemented in addition to meeting the needs of electricity load in the smart home, it can also contribute excess energy to fulfill off-grid building load. Monte Carlo Simulation (MCS) is carried out at the beginning of data processing by randomly generating 24-hour models of solar irradiance and smart home load requirements along with weather conditions. PLN not only takes over fulfilling the needs of the smart home load when there is less and or no sunlight and minimum battery capacity conditions, but also it will charge the battery capacity up to 100% every midnight. On average, the daily load requirement for a smart home is almost half the energy produced by PVs, which are 12,439 kW and 24,509 kW respectively. Furthermore, the smart home hybrid power is capable of producing 8,946 MW of excess energy in a year to serve the off-grid building load needs.

Empowering Homes with Esp-32

Smartboard system utilizing an ESP32 microcontroller integrated with various sensors and electric sockets. The system aims to enhance safety, convenience, and energy efficiency in residential environments. Initially, an electric switch box was fitted with two sockets, and an ESP32 microcontroller was integrated to control them. The system further includes a smoke sensor for gas leakage detection, water level magnetic sensors for automatic water pump control, a DHT sensor for temperature and humidity monitoring, a display for real-time data visualization, and a Miniature Circuit Breaker (MCB) for protection. Through integration with the Blynk app, users can remotely monitor and control the system, including supplying power to sockets and detecting gas leaks. Additionally, Bluetooth connectivity is incorporated for added convenience, particularly for electric scooter users in complex living environments. Experimental results demonstrate the efficacy of the integrated system in providing remote control, safety monitoring, and automation features. KeyWords: Smartphone-based, Mobile application, an automated system (ESP32), Wifi Network