Sustainable Energy Management Research Articles

Role of education and awareness programs in fostering energy conservation behavior in cities: Empowering urban sustainability using deep learning approach

This paper introduces a pioneering framework that emphasizes the critical role of education in promoting energy conservation behavior within urban environments. By synergizing deep learning methodologies with education-awareness formulations and algorithm, the framework aims to bolster energy conservation security in cities. A central aspect of this framework is the development of an advanced Intrusion Detection System (IDS) model, utilizing Restricted Boltzmann Machines (RBMs) tailored to enhance energy consumption security. Through six strategic integrations of educational components, the IDS not only identifies but also mitigates potential threats to energy systems with unprecedented efficiency. Additionally, the research introduces a novel evolutionary feature selection method based on Memetic Algorithms (MAs), which strategically identifies crucial features for RBM training, ensuring optimal model performance. The incorporation of Memetic Algorithms facilitates individual learning and problem-specific knowledge transmission, further enhancing the adaptive capabilities of the system. This multifaceted approach underscores the pivotal role of education in fostering urban sustainability, pushing the boundaries of energy conservation security. By proactively responding to emerging threats, the proposed framework contributes significantly to the overarching goal of sustainable and secure urban energy management.

Fostering second-life applications for electric vehicle batteries: A thorough exploration of barriers and solutions within the framework of sustainable energy and resource management

Electric vehicles (EVs) play a crucial role in achieving Sustainable Development Goals (SDGs), specifically focusing on Goal 7, which ensures access to Affordable and Clean Energy, and Goal 9, promoting advancements in Industry, Innovation, and Infrastructure. However, the growing stockpile of used EV Batteries (EVBs) presents challenges. These EVBs, however, hold potential for second-use applications, aligning with energy conversion, resource optimization, and sustainable energy systems. This study employs a rigorous multi-criteria approach to bridge the research-practice gap by systematically evaluates and prioritizes barriers hindering the widespread adoption of second-use EVBs. The study's overarching objectives are to overcome these barriers, aligning with broader sustainability goals, and to offer insights shaping policies addressing environmental, social, and economic impacts, in coherence with SDGs 8, 11, 12, and 13. The research unfolds through a three-phase approach involving literature reviews, field visits, and expert interviews for barrier identification, theoretical framework development through literature and Delphi studies, and prioritization using the Fuzzy Best-Worst Method (FBWM). Results indicate that addressing Cost and Technological Challenges is pivotal for successful second-use EVB adoption, with proposed solutions involving regulations, recycling, and awareness. The study underscores the circular economy perspective, connecting identified barriers to specific SDGs. The FBWM proves effective in prioritizing barriers, providing actionable insights for policymakers and stakeholders. This research contributes significantly to the existing literature by systematically evaluating second-use EVBs, addressing a crucial research gap, and offering valuable pathways for broad adoption.

Sustainable energy transition in cities: A deep statistical prediction model for renewable energy sources management for low-carbon urban development

This study proposes a novel approach to sustainable energy transition in urban environments, focusing on the prediction and management of renewable energy outputs for low-carbon urban development. We introduce a new intelligent prediction model based on Gated Recurrent Unit (GRU) Networks to forecast the output power of both tidal and biomass units. The GRU model, known for its capability in capturing sequential dependencies, is employed to enhance the accuracy of renewable energy predictions in the context of urban sustainability. To address the interpretability challenges inherent in complex deep learning models, we leverage LIME (Local Interpretable Model-agnostic Explanations). By integrating LIME into our GRU-based prediction model, we enhance transparency and understandability. This approach facilitates the identification of key factors influencing predictions, providing stakeholders and decision-makers with valuable insights for effective sustainable energy management. The proposed hybrid model is applied to the digital model of real tidal and biomass sites in China, allowing for a statistical examination of renewable integration and energy management strategies. The results offer a comprehensive understanding of the dynamics between renewable energy sources and urban development, paving the way for informed decision-making in the pursuit of low-carbon and resilient cities. This research contributes to the broader discourse on sustainable urban development by offering a robust statistical framework for renewable energy integration and management in urban environments.

Research on the Spatiotemporal Characteristics of the Coupling Coordination Relationship of the Energy–Food–Water System in the Xinjiang Subregion

In the Xinjiang region, the sustainable management of water resources, energy, and food is crucial for regional development. This study establishes a coupling evaluation index for energy–food–water (EFW) systems from the perspectives of supply, consumption, and efficiency. Using an integrated EFM-CDD-RDD-CCDM approach, an assessment of the coupling and coordination levels of the EFW systems in 14 cities within Xinjiang was conducted for the period of 2004 to 2020. Additionally, the method of obstacle degree identification was utilized to determine the main barriers affecting the EFW systems. Key findings included the following. (1) In terms of individual system coordination indices, the water resource systems exhibited overall higher coordination (ranging from 0.30 to 0.72) with comparatively minor spatial variability, while the energy (from 0.18 to 0.81) and food (from 0.12 to 0.83) systems showed greater temporal and spatial fluctuations. From 2004 to 2020, improvements were observed in the coordination of food and water resource systems, whereas a decline was noted in the coordination of the energy subsystem. (2) Prior to 2011, the coupling of food–water and energy–food systems showed an upward trend, whereas the energy–water coupling decreased annually by 2.62%, further highlighting the tensions between energy development and water resource constraints in Xinjiang. (3) The comprehensive coupling coordination index of the Xinjiang EFW systems ranged between 0.59 and 0.80; between 2004 and 2020, there was an oscillatory increase. From 2004 to 2016, the coupling and coordination degree across the municipalities generally improved, with the regions on the western side and southern slope of the Tianshan Mountains, the Altai Mountains, and the northwestern edge of the Junggar Basin exhibiting the highest levels, followed by the three prefectures in southern Xinjiang. (4) The EFW obstacle degree posed by the food systems in Xinjiang and its divisions showed a decreasing trend from 2004 to 2020, with the energy system identified as the main factor affecting the coupling and coordination degrees of the EFW systems (increasing by 44% to 52%). Therefore, it is imperative to accelerate the energy transition and optimization in the lead energy development and production areas of Xinjiang. This research provides a scientific basis for Xinjiang’s sustainable development strategies and highlights potential directions for the future optimization of resource management.

An IoT-Enabled Smart Energy Management System to Improve Energy Efficiency in University Laboratory

Efficient energy management remains a challenge in university laboratories. The aim of this study is to develop and deploy an Internet of Things (IoT) system that can automatically adjust electronic devices to improve the efficiency of power consumption in the university laboratory. An advanced system is urgently needed to support sustainable and effective energy management. A smart IoT-based energy management system can improve energy efficiency, reduce operating costs, and reduce negative environmental impacts. The system enables both human and automated control of lighting and air conditioning using scheduling and occupancy detection. This development-based research uses a product design methodology that includes both software and hardware. The software product in this research is an IoT platform website for managing IoT devices. The essential processes include defining project goals and requirements, designing a system architecture, creating a user experience design, developing and integrating components, and conducting tests. The system is successful in automatically monitoring and controlling electronic devices based on certain parameters such as scheduling and presence detection. The system meets most of the specified functional and non-functional requirements as demonstrated in experiments, although it is somewhat limited by hardware limitations. Ultimately, the system increases the energy efficiency of the laboratory and thus successfully fulfills the research goal. This innovative project could be a blueprint for other smart energy control efforts.

Decoding Energy Usage Predictions: An Application of XAI Techniques for Enhanced Model Interpretability

The growing complexity of machine learning models has heightened the need for interpretability, particularly in applications impacting resource management and sustainability. This study addresses the challenge of interpreting predictions from sophisticated machine learning models used for building energy consumption predictions. By leveraging Explainable AI (XAI) techniques, including Permutation Importance, SHapley Additive exPlanations (SHAP), and Local Interpretable Model-Agnostic Explanations (LIME), we have dissected the predictive features influencing building energy usage. Our research delves into a dataset consisting of various building characteristics and weather conditions, applying an XGBoost model to predict Site Energy Usage Intensity (Site EUI). The Permutation Importance method elucidated the global significance of features across the dataset, while SHAP provided a dual perspective, revealing both the global importance and local impact of features on individual predictions. Complementing these, LIME offered rapid, locally focused interpretations, showcasing its utility for instances where immediate insights are essential. The findings indicate that 'Energy Star Rating', 'Facility Type', and 'Floor Area' are among the top predictors of energy consumption, with environmental factors also contributing to the models' decisions. The application of XAI techniques yielded a nuanced understanding of the model's behavior, enhancing transparency and fostering trust in the predictions. This study contributes to the field of sustainable energy management by demonstrating the application of XAI for insightful model interpretation, reinforcing the significance of interpretable AI in the development of energy policies and efficiency strategies. Our approach exemplifies the balance between predictive accuracy and the necessity for model transparency, advocating for the continued integration of XAI in AI-driven decision-making processes.

Comparative Analysis of Machine Learning Methods for Predicting Energy Recovery from Waste

In the context of escalating energy demands and the quest for sustainable waste management solutions, this paper evaluates the efficacy of three machine learning methods—ElasticNet, Decision Trees, and Neural Networks—in predicting energy recovery from municipal waste across the European Union. As renewable energy sources increasingly dominate the energy production landscape, the integration of Waste-to-Energy (WTE) processes presents a dual advantage: enhancing waste management and contributing to the renewable energy mix. This study leverages a dataset incorporating economic and environmental indicators from 25 European countries, spanning 2013–2020, to compare the predictive capabilities of the three machine learning models. The analysis reveals that Neural Networks, with their intricate pattern recognition capabilities, outperform ElasticNet and Decision Trees in predicting energy recovery metrics, as evidenced by superior performance in key statistical indicators such as R-value, Mean Squared Error (MSE), and Mean Absolute Error (MAE). The comparative analysis not only demonstrates the effectiveness of each method but also suggests Neural Networks as a pivotal tool for informed decision-making in waste management and energy policy formulation. Through this investigation, the paper contributes to the sustainable energy and waste management discourse, emphasizing the critical intersection of advanced technologies, policy considerations, and environmental stewardship in addressing contemporary energy challenges.

Production and assessment of UV-cured resin coated stearyl alcohol/expanded graphite as novel shape-stable composite phase change material for thermal energy storage

Expanded graphite-phase change materials (PCM) structures are reinforced to polymers with various methods to fabricate advanced thermal energy storage materials. However, these methods still suffer from processing time and product efficiency challenges. In this study, the UV-curing method was used to produce shape-stable EG-PCM-reinforced resin composites with fast curing and low process temperature of the resin. The composite material, comprising UV-curable resin (30 %), stearyl alcohol (65 %), and Expanded graphite (5 %), was synthesized. This synthesis aimed to address the limitations of traditional PCMs, such as low thermal conductivity and leakage. Differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA) were used to characterize the materials' phase change behavior and thermal stability. Scanning electron microscopy (SEM) and Fourier-transform infrared spectroscopy (FTIR) analyses were conducted to elucidate the microstructure and crystallinity of composite materials. The composites, exhibiting near-perfect impermeability with leakage as minimal as 0.89 %, not only enable the attainment of cooler environments by 2–3 °C under hot air conditions but also demonstrate exceptional thermal stability up to 207 °C, as evidenced by TGA results. Additionally, they offer a remarkable melting enthalpy value of 153.1 J/g. These composites, with their shape-retention ability during phase transitions and high thermal energy storage capacity, are a versatile and efficient option for sustainable energy management. This research contributes to the development of innovative materials for renewable energy integration and reducing carbon emissions.

Ten years of sustainable energy planning and management

With this volume 40 the International Journal of Sustainable Energy Planning and Management can celebrate its 10 years anniversary. In this editorial we take a closer look at the journals impact over its first 10 years of existence and highlight the articles that have made the largest impacts in the scientific community from a citation perspective. The topic of the journal – sustainable energy planning and management – has not decreased in importance over the first ten years of the journal’s existence – and fortunately the results published in the journal has garnered interest and the impact of the journal has significantly increased. In terms of citation, the journals 275 published articles, has received 1484 citations as per March 2024 and nine articles have 40 or more citations in Scopus. CiteScore ranks the journal in the top 10% out of 700+ journals within the topic of Geography, Planning and Development.

Role of microbial electrolysis desalination cell in sustainable water and energy management: Performance assessment of platinum and nickel foam cathodes and simulating integration with reverse osmosis systems

This research introduces the microbial integrated cell (MIC), a bioelectrochemical system that combines microbial electrolysis and desalination for sustainable energy and water purification. Efficiency evaluations of nickel foam and Pt/CC cathodes for hydrogen production and desalination showed nickel foam achieving a 91% desalination efficiency, closely following Pt/CC's 99%, with hydrogen generation rates of 0.96 m3H2/m3/d for Pt/CC cathodes and 0.72 m3H2/m3/d for nickel foam. Despite marginally lower performance, the cost advantages of nickel foam suggest its feasibility for large-scale deployment. Further exploration into MIC integration with reverse osmosis (RO) system aims to enhance the sustainability of the combined system. Simulation outcomes indicate that MIC-RO integration can significantly reduce energy consumption and brine concentration discharged into the sea, highlighting its potential to improve desalination plants' efficiency and environmental impact. This research facilitates the transition of these green technologies to practical applications, optimizing efficiency and environmental benefits.

Advancing Sustainable Energy Management: A Comprehensive Review of Artificial Intelligence Techniques in Building

Advancing Sustainable Energy Management: A Comprehensive Review of Artificial Intelligence Techniques in Building

Smart landscaping design for sustainable net-zero energy smart cities: Modeling energy hub in digital twin

In our study, we introduce an innovative energy management system optimized for energy hubs in net-zero energy smart cities, emphasizing sustainability as a core objective. Our approach intricately integrates distributed generation systems and storage facilities within a digital twin framework, incorporating landscape considerations to promote environmental harmony. By prioritizing sustainability, our strategy aims to minimize operational costs while maximizing energy efficiency and reducing carbon emissions. We employ specialized energy flow algorithms for each grid, designing energy hubs that leverage renewable resources and mobile storage to enhance sustainability. To address complexity, our model utilizes a non-convex mixed-integer nonlinear programming structure and robust adaptive optimization techniques, combining the Gray Wolf and Krill Herd algorithms. Through simulations, we demonstrate significant improvements in power system efficiency, including notable reductions in power loss, peak voltage fluctuations, and temperature variations. Our research underscores the critical role of smart landscaping in advancing sustainable energy management for net-zero energy smart cities, facilitating seamless integration of energy hubs with the surrounding landscape within a digital twin paradigm.

Optimizing Energy Consumption in Agricultural Greenhouses: A Smart Energy Management Approach

Efficient energy management is crucial for optimizing greenhouse (GH) operations and promoting sustainability. This paper presents a novel multi-objective optimization approach tailored for GH energy management, aiming to minimize grid energy consumption while maximizing battery state of charge (SOC) within a specified time frame. The optimization problem integrates decision variables such as network power, battery power, and battery energy, subject to constraints based on battery capacity and initial energy, along with minimum and maximum energy from the battery storage system. Through the comparison of a smart energy management system (EMS) with traditional optimization algorithms, the study evaluates its efficiency. Key hyperparameters essential for the optimization problem, including plateau time, prediction time, and optimization time, are determined using the ellipse optimization method. Treating the GH as a microgrid, the analysis encompasses energy management indicators and loads. A simulation conducted via Simulink in MATLAB software (R2021b) demonstrates a significant enhancement, with the smart EMS achieving a more than 50% reduction in the objective function compared to conventional EMS. Moreover, the EMS exhibits robust performance across variations in the load power and irradiation profile. Under partial shading conditions, the EMS maintains adaptability, with a maximum objective function increase of 0.35553%. Aligning the output power of photovoltaic (PV) systems with real-world conditions further validates the EMS’s effectiveness in practical scenarios. The findings underscore the efficiency of the smart EMS in optimizing energy consumption within GH environments, offering promising avenues for sustainable energy management practices. This research contributes to advancing energy optimization strategies in agricultural settings, thereby fostering resource efficiency and environmental stewardship.

Current Status and Future Prospects of Small-Scale Household Biodigesters in Sub-Saharan Africa

Despite the age of the technology, sub-Saharan African (SSA) countries have numerous challenges that hinder biogas from being widely adopted. This review investigates the current gap between holistic use and the need for small-scale household biodigester adoption and effectiveness in rural and semiurban households of SSA. It reviews the present situation and potential of small-scale household digesters for sustainable future energy and biofertilizer use, with a focus on SSA. A literature survey was performed on small-scale household biogas digesters, and issues relating to the distribution, use, and implementation status with their implications on the future of small-scale household digesters in SSA were briefly reviewed. In recent years, the overall number of domestic biogas digesters installed across SSA countries has shown a significant increase due to the efforts of the National Domestic Biogas Programs such as the African Biogas Partnership Program with the Netherlands Development Organization and the Humanist Institute for Development Cooperation. However, based on an extensive literature review on small-scale household biodigesters in SSA, the study highlights that the success of biogas technology as a clean domestic cooking fuel has been relatively low. The findings of this review show that SSA countries still face a number of hurdles, the most significant of which can be boiled down to the need for technological advancement according to local context, social acceptance, and large initial investment costs. In order to overcome these obstacles and advance technological capability, social acceptance, financial benefits, and environmental impacts in order to improve its use and widespread dissemination as a renewable energy source, a highly effective organic fertilizer, and economic benefits for the betterment of SSA communities, more well-organized work and adequate research activities should be initiated and supported. The findings may be useful to researchers, practitioners, and policymakers who support/promote sustainable energy and waste management strategies in low-resource settings.

Novel enhanced thermal capabilities of Cu-Al2O3/water hybrid nanofluid in an oblique U-shape cavity: On the molecular behaviour through vorticity analysis

This paper presents the numerical investigation of natural convection heat transfer of copper-alumina/pure water hybrid nanofluid in an oblique U-shaped enclosure. The dimensionless governing equation is formed in the stream function-vorticity formulation using dimensionless variables. The Galerkin weighted residual finite element method with the damped Newton-Raphson iteration method is applied to the problem. The heat transfer performance for the system is investigated by varying the Rayleigh numbers, nanoparticle volume fractions and their ratios, and the oblique angles of the enclosure. The fluid flow, heat transfer, and vorticity analysis are conducted extensively for the Rayleigh number up to 106. It was found that the oblique enclosure dwindled the overall heat transfer performance. Correspondingly, the maximum vorticity is at the lowest with zero obliquenesses. The results are used to gain a deeper understanding of the fluid flow and thermal behaviour of the system to achieve more sustainable and greener energy management.

Decomposed particle swarm optimization for optimal scheduling in energy hub considering battery lifetime

The energy hub has become pivotal in optimizing multi-carrier energy systems, aiming to enhance the flexibility and efficiency of the overall infrastructure. By incorporating energy storage within these hubs, it becomes feasible to dynamically coordinate the balance between energy generation and consumption. This coordination considers diverse energy pricing structures and local renewable energy generation, leading to substantial reductions in energy expenditures. To further amplify the long-term utilization of batteries, it is essential to account for the associated costs related to their lifespan during the optimization process. Nonetheless, optimizing the operation of an interconnected energy hub system while factoring in battery lifespan introduces a complex challenge, encompassing various limitations, spanning multiple time frames, and involving non-convex aspects. This article introduces an inventive strategy termed the “decomposition technique,” which entails amalgamating particle swarm optimization with a numerical approach. By disassembling the complex optimization predicament into more manageable sub-problems, particularly the organization of storage and other elements within the energy hub system, this approach capitalizes on the individual strengths of particle swarm optimization and the interior point method. To assess the efficacy of this proposed method, it is applied across various scenarios, encompassing a two-hub system and a three-hub system spanning a 24-h timeframe. Through comparison with analytical methods, the results obtained affirm the technique's capability to converge remarkably close to the global minimum. Moreover, the computational efficiency of this approach enables its online implementation, with a reduced time horizon, and ensures real-time optimization. The success of this approach opens up potential applications in diverse energy hub systems and emphasizes its adaptability and scalability. Using the strengths of particle swarm optimization and the interior point method, this technique provides an effective solution for optimizing energy hub systems while considering battery lifetime costs. It enables near-optimal performance while overcoming limitations related to convergence and computation time. The findings of this research contribute to the broader field of energy optimization and pave the way for more efficient and sustainable energy management strategies. The decomposition technique yielded a significant 12% reduction in total energy cost compared to conventional particle swarm optimization. In the three-hub system, the decomposition technique showcased a 15% increase in computational efficiency, affirming its adaptability for larger systems. Considering battery lifetime costs, excluding them led to a 6% boost in energy savings but resulted in a 2% rise in overall system cost, emphasizing the need for a balanced optimization approach.

Year-round experimental analysis of a water-based PVT-PCM hybrid system: Comprehensive 4E assessments

In this year-long study conducted in Cairo, Egypt, an innovative water-based photovoltaic-thermal (PVT) system coupled with phase change material (PCM) was evaluated for its ability to enhance building energy efficiency. The system utilizes 22 copper capsules filled with RT35 paraffin wax and a water-cooling duct to ensure optimal electricity generation and heat storage. The proposed system underwent detailed 4E (energy, exergy, economic, and environmental) assessments to compare its performance against traditional uncooled PV panels. The results indicated significant improvements: a reduction in cell temperature by up to 8.3 °C, an increase in electrical efficiency by 4.0–13.3%, and a boost in power output by 4.1–15.9%. Overall, the system demonstrated efficiencies between 27.23 and 67.33%, along with an exergy efficiency peak at 74.64% in July, substantially outperforming the uncooled panel. Economically, the PVT-PCM system achieved a 1.58-year payback period, and carbon emissions were reduced by 32%, highlighting its viability and environmental benefits. The potential of the PVT-PCM system to advance sustainable building energy management was strengthened by this study, providing a significant step forward in integrating renewable energy within architectural designs.

Smartgrid-based hybrid digital twins framework for demand side recommendation service provision in distributed power systems

Electricity consumers face challenges in selecting an optimal energy-saving plan, and this is a sustainability problem. To set consumers focus on sustainable energy management, developments around ”Industry 4.0” are needed to achieve an optimal balance between cost and energy consumption with a focus on cutting-edge machine-learning models and smart services introduction. Energy modelling is crucial for confronting the challenges introduced by the energy transition. This research is to promote digital twin (DT) technology in smart energy grids. The hybrid digital twin model is developed based on smart grid end-users’ electrical components. This approach emulates the component specification of the reference smart grid system and encourages a reduction in net energy consumption. Additionally, the application of AI connects smart meters, IoT devices, and the assets of the smart grids to the DT recreation of demand-side end-users for energy efficiency recommendation provision; this setup improves energy management, energy efficiency, and the usage of renewable energy resources. The novelty of this study is that the recent scope of demand-side recommendation schemes has been extended in the direction of cutting-edge Industry 4.0 hybrid DTs. Additionally, a prototype system and its functional characteristics have been proposed with the potential to pave the path for utilization in microgrid environments. Simulation results show a unique level of parallelism between the reference system and the developed model, along with a 36.8% reduction in net energy consumption following implementation of the recommendation advice.

A Unified Data Profile for Microgrid Loads, Power Electronics, and Sustainable Energy Management with IoT

Controllable devices in a classical centralized grid work independently, providing desired functionalities for the owner only, making grid stability and efficient energy management challenging. Therefore, the dynamically developing communication infrastructure has been crucial in improving local energy management and stability, introducing the Internet of Things and, finally, creating micro- or smart grids. Communication technology already allows for exchanging data and information with high bandwidth in practical industrial and grid applications. However, considering the increasing number of electrical devices with different purposes, parameters, and possibilities to work as an energy source or storage, the challenge is device profile standardization, especially for power electronics devices. As many devices as possible should be able to exchange information with the grid operator or local area/home energy management device, like industrial agents, energy routers, or smart transformers. The number and types of parameters, outputs, and input signals available in the devices from the communication point of view are significantly different, making it extremely difficult to apply the advanced energy management algorithm. Therefore, the unified data profile for modern loads was developed and discussed. The proposed device model was standardized, including mandatory information about the device’s work and parameters, with the possibility of reading the control commands from the supervisor device. The proposed unified model was studied in simulation research, integrating self-balanced and self-control areas.

Enhancing the performance of sustainable energy management of buildings in smart cities

Energy utilization has been the most influential parameter in recent decades, especially in the smart city model. The energy management system has been a more attractive research problem due to its utility, ability, and applications. This paper has an objective that the article discusses innovative energy management methods for sustainability and highlights the potential for integrated smart energy sources. The discussion also touches on the understanding of energy management and production, various storage systems, and their potential future applications. This paper explores challenges in sustainable smart energy management, focusing on methodologies like smart energy systems, PV calculations, electric grid models, and energy management strategies in smart cities. The passive infrared receiver (PIR) sensor has been used in real-time energy management systems to integrate these methodologies into the city's infrastructure. The energy management design aims to coordinate electrical appliances such as fans and lights to minimize energy consumption. The article proposes new energy management and security techniques based on data sources to enhance city intelligence, adaptability, and sustainability by reducing human involvement in controlling electrical appliances in residential buildings. The proposed design and development system optimizes energy utilization more efficiently and effectively than conventional systems, meeting real-time energy management objectives.