Smart Energy Management Research Articles

Powering Tomorrow: Innovation in Smart Energy Metering and Management

To address the escalating demand for electricity due to population growth and increased reliance on electrical energy, it is imperative to modernize the electrical infrastructure. Leveraging Internet of Things (IoT) technology can revolutionize energy consumption and distribution across various scenarios. This study primarily concentrates on enhancing the electrical system through automatic billing, power card services, theft detection, power optimization, and providing users with pertinent energy consumption data. The IoT-based smart energy meter system comprises three key elements: a controller, Wi-Fi connectivity, and theft detection mechanisms. Whenever an anomaly like theft or malfunction occurs, the theft detector sensor identifies the issue and responds accordingly. The controller plays a pivotal role in maintaining the functionality of all components. By integrating energy meters with the internet through IoT, human involvement in electricity management is minimized. The proposed IoT-based meter reading system is designed to continually monitor meter readings, enabling service providers to disconnect power sources in case of non-payment of monthly bills. Moreover, it ensures accurate meter readings and prevents billing errors while reducing the need for human intervention Key Words: internet of things (IoT), smart energy meter, automatic billing, theft detection.

Towards sustainable urban living: A holistic energy strategy for electric vehicle and heat pump adoption in residential communities

Electric vehicles (EVs) and heat pumps (HPs) are key in reducing carbon emissions from transportation and domestic heating, yet their adoption may increase peak load demands on electrical networks. One of the aims of this research is to assess the potential impact of uncontrolled EV charging on community-scale distribution networks, exploring how this could stress the existing electrical infrastructure. It also explores the role of EVs in Vehicle-to-Grid (V2G) and smart charging applications, aiming to enhance community distribution systems. The study investigates the maximum stabilisation level achievable under various scenarios, highlighting the importance of smart energy management in integrating renewable energy and addressing uncertainties in the modelling process. Additionally, this study discusses the proposed systems' scalability, consumer behaviours' impact on the suggested energy solutions, and the potential implications of recent technological advancements for simulated communities. The research employs a sophisticated, integrative approach, combining stochastic methods with several robust energy software. Key findings suggest that uncontrolled EV charging can lead to grid capacity issues at high EV penetration levels, particularly during colder months. While smart charging and V2G technologies can moderate peak loads in many scenarios, achieving 100 % sustainable technology integration requires enhanced energy management or increased network capacity, especially in winter. Wind and solar power integration demonstrates strategic complementarity, particularly in winter, enhancing the reliability and stability of the community grid. It is also observed that peak solar generation hours misalign with the community's highest demand times, posing challenges for solar energy utilisation in EV charging in residential-based areas.

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.

A Human-Centered Power Conservation Framework based on Reverse Auction Theory and Machine Learning

Extreme outside temperatures resulting from heat waves, winter storms, and similar weather-related events trigger the Heating Ventilation and Air Conditioning (HVAC) systems, resulting in challenging, and potentially catastrophic, peak loads. As a consequence, such extreme outside temperatures put a strain on power grids and may thus lead to blackouts. In order to avoid the financial and personal repercussions of peak loads, demand response and power conservation represent promising solutions. Despite numerous efforts, it has been shown that the current state-of-the-art fails to consider: i) the complexity of human behavior when interacting with power conservation systems; and ii) realistic home-level power dynamics. As a consequence, this leads to approaches that are i) ineffective due to poor long-term user engagement; and ii) too abstract to be used in real-world settings. In this paper, we propose an auction-theory-based power conservation framework for HVAC designed to address such individual human component through a three-fold approach: personalized preferences of power conservation, models of realistic user behavior , and realistic home-level power dynamics . In our framework, the System Operator (SO) sends Load Serving Entities (LSEs) the required power saving to tackle peak loads at the residential distribution feeder. Each LSE then prompts its users to provide bids , i.e., personalized preferences of thermostat temperature adjustments, along with corresponding financial compensations. We employ models of realistic user behavior by means of online surveys to gather user bids and evaluate user interaction with such system. Realistic home-level power dynamics are implemented by our machine-learning-based Power Saving Predictions (PSP) algorithm, calculating the individual power savings in each user’s home resulting from such bids. A machine-learning-based Power Saving Predictions (PSP) algorithm is executed by the users’ Smart Energy Management System (SEMS). PSP translates temperature adjustments into the corresponding power savings. Then, the SEMS sends bids back to the LSE, which selects the auction winners through an optimization problem called POwer Conservation Optimization (POCO). We prove that POCO is NP-hard, and thus provide two approaches to solve this problem. One approach is an optimal pseudo-polynomial algorithm called DYnamic programming Power Saving (DYPS), while the second is a heuristic polynomial-time algorithm called Greedy Ranking Allocation (GRAN). EnergyPlus, the high-fidelity and gold-standard energy simulator funded by the U.S. Department of Energy, was used to validate our experiments, as well as to collect data to train PSP. We further evaluate the results of the auctions across several scenarios, showing that, as expected, DYPS finds the optimal solution, while GRAN outperforms recent state-of-the-art approaches.

Energy Efficiency through Solar Panelled Mobile Workshop for Vocational Education Teaching Factory

Energy efficiency needs to be fundamental norm in education. Vocational education which focuses on tangible outputs of knowledge and science should be taking part in creating sustainable and responsible outcomes. This paper presented a model of adapting the energy efficiency foundation into application of technology and knowledge. As vocational education becomes a widely sought-after alternative for hands-on and output-based education, application of such model is at most importance. A strong and impactful vocational institution requires good and independent teaching factory (TEFA). A TEFA becomes a backbone for practicum and production at the same time. A good TEFA is expensive to create and maintain. Therefore, many vocational schools become ineffective and new and more This paper presented a new model for vocational school teaching factory program (TEFA) based on smart energy management. The model was introduced by SMK Negeri 1 Pringgabaya, a vocational school in East Lombok, Indonesia. A TEFA active learning process and entrepreneurial application by designing solar energy carts an as annex to the mobile workshop. In this present study, the model used an integrated experimental approach between output-based education and a smart energy management. Such model also displayed more equitable vocational outcomes through entrepreneurial aspects of commercialization. The results showed a model of mobile workshop aimed at providing services to motorcycle owners while the solar paneled carts provided electricity for both the mechanics and the customers. Added value was identified because customers could charge their mobile phones for free and enjoy cheap or complimentary beverages. The solar paneled mobile workshop did not only gain revenue but also actively promoted itself through social media platforms. It was concluded that the model provided a more equitable value for the sustainable TEFA.

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.

Deceptive Patch Solutions for Protecting Industrial Control Systems Based on Discovered Vulnerabilities

An increase has been observed in concerns about cyber security threats in smart energy management on a global scale. Industrial Control Systems, or simply ICSs, are frequently present in industries and essential infrastructures, e.g., water treatment facilities, nuclear and thermal plants, heavy industries, power production, and distribution systems. ICS devices are high-risk targets for attacks and exploitation with significant security difficulties for ICS vendors and asset owners. Like many consumer electronics, industrial systems are susceptible to a bevy of vulnerabilities that hackers can exploit to launch cyber attacks. Extensive use of ICSs in Critical Infrastructures (CI) increases the vulnerability of CI to cyber attacks and makes their protection a critical subject. This study first contributes to a novel line of research considering how deception can be used by defenders in strategic terms with the objective of introducing uncertainty into an adversary’s perception of a system patch management process in order to protect ICSs. Thus, we mention the advantages of patch models to improve the vulnerabilities of ICSs. We explore deceptive patch management models for the purpose of providing better insight into developing future cyber security techniques for ICS attacks. We propose deceptive patch management solutions as case studies for common ICS attacks.

Optimal household appliances scheduling for smart energy management considering inclining block rate tariff and net-metering system

Smart grids that integrate household renewable energy sources and share information with households can help create and maintain a smarter data-driven environment. Within this environment, flexible home energy management policies that minimize household energy costs can be adopted. This paper considers a smart home with a renewable energy source that favors satisfying its energy needs at minimum cost. This is achievable by smartly scheduling the use of its domestic appliances to match a given energy grid tariff. Focusing on the case of Egypt in which an inclining block rate (IBR) tariff is imposed, this paper fills a gap in the literature regarding the load scheduling models aiming to minimize energy cost at the household level whenever such a tariff exists. A new mixed integer quadratic programming (MIQP) model is formulated for this scheduling problem, considering the adopted net metering system with installed domestic photovoltaic (PV) systems in Egypt. The model generates the optimal household load schedule and the optimal amounts of energy to exchange with the grid while considering all the system and consumer utility constraints. To assess the applicability of the proposed model, a survey is conducted to identify the diversity and characteristics of using the electrical appliances by the Egyptian households. Based on the collected survey results, the effectiveness of the proposed MIQP model is investigated. Results confirm the effectiveness of the proposed model to minimize energy cost for different categories of the Egyptian households.

Adaptive cloudiness index for enhanced photovoltaic energy prediction and management in low-income smart homes using geographic information system

Solar-powered homes can be an optimal solution for the lack of continuous power sources problem in initial low-income communities. However, the challenge of Photovoltaic (PV) uncertainty can make it difficult to coordinate this vital solar energy in real-time. This paper proposes a new, low-cost solution for assessing the uncertainty of photovoltaic power generation in smart home energy management systems. The proposed index, inspired by the well-known clearness index, is an adaptive deterministic indicator that only requires free Geographic Information System (GIS) models and PV power measurement, without the need for expensive high-tech controllers or expert engineers/programmers. The proposed index successfully predicts the daily PV energy with errors of less than 3% for more than 93% of studied days, according to the 2020 measured solar radiation of the studied case in an African developing location, i.e. Cairo. Egypt.

An integrated energy storage framework with significant energy management and absorption mechanism for machine learning assisted electric vehicle application

Regarding environmental friendliness, low maintenance needs, and statuses as a renewable technology, Hybrid Electric Vehicles (HEV) have become more and more popular around the world. In this, the energy management system is crucial for the effective storage of power and regulation of the energy flow system. As a result, Hybrid Energy Storage Systems (HESS) has increased interest due to their superior capabilities in system performance and battery capacity when compared to solo energy sources. Additionally, the primary problem interaction applications, including such battery electric vehicles, are the energy storage system. Multiple energy storage technologies, including battery packs, flywheels, super-capacitors and fuel cells, are combined into a HESS due to their complementing properties. The goal of this setup is to make renewable energy sources more reliable by storing power generated from intermittent sources or by providing backup energy generation from traditional energy sources. A HESS could be utilized as an alternate energy storage system to help them make up for their lack of power density. HESS needs a smart Energy Management System (EMS) to function properly since it combines the dynamic characteristics of a battery and a SuperCapacitor (SC). The motive of the study is to suggest an actual power management control system to accomplish these objectives. The plan is built using a wavelet transform, deep learning mechanism, and fuzzy logic together. A useful tool for separating the various frequency elements of a load's power requirements to reflect the properties of a battery or supercapacitor is the wavelet transform. It is challenging to immediately apply it in a system, though. Because of this, the traditional optimization-model-based facility energy management system encounters substantial difficulties with online forecast and calculation. To solve this problem, the paper proposes a ML technique dependent on a Long Short-Term Memory (LSTM). The suggested control system structure allows for the separation of the offline and online stages of the LSTM technique. The LSTM is being used to map states (inputs) to decisions (outputs) based on system training during the offline stage. As a result, the supercapacitor receives an online calculation and distribution of the high-frequency power requirement. The SOC of the supercapacitor is kept within the appropriate range via fuzzy logic control. To evaluate the efficacy of the suggested energy management control technique, a 70 V battery with 92 V supercapacitor hybrid energy storage devices for hardware platforms have been created.

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.

Optimizing smart building energy management systems through industry 4.0: A response surface methodology approach

Effective control of inner climatic conditions in a Heating ventilation and air conditioning (HVAC) system based on efficiency of the system is imperative to ensure occupant comfort, energy efficiency, and preservation of indoor air quality. The existing problems with HVAC system includes inefficient adjustment to internal room parameters and limited optimization based on varying loads. The current study investigates the influence of Industry 4.0 by assessing internal room parameters in an indoor ventilated room conditions for system output. Smart systems like AI and IoT, integrated with sensors, dynamically adjust indoor conditions based on external climate, enhancing HVAC system efficiency. Using Artificial Neural Networks (ANN) and Response Surface Methodology (RSM), integrated with AI and IoT sensors, optimally adjust indoor conditions in correlation with external climate, maximizing HVAC system efficiency. The study utilizes input parameters including Dry Bulb Temperature (DBT), Relative Humidity (RH), and Solar Radiation across diverse days to analyze system efficiency. ANN and RSM iteratively optimize system efficiency by training on RH, DBT, and air quality data. They converge on recommending 42% RH, 28 °C DBT, and 25 PM air quality, achieving 0.851 desirability. This approach enhances heat load (75.89%) and ventilation load (69.12%) efficiency significantly. Integration of Industry 4.0 sensors in the HVAC system resulted in a 16% efficiency increase and 15% cost effectiveness in the controlled room compared to prior measurements.

Smart Energy Management for Sustainability: An Exploration of the Potential of IoT and AI Technologies

Smart Energy Management for Sustainability: An Exploration of the Potential of IoT and AI Technologies

Privacy-Preserving Smart Energy Management by Consumer-Electronic Chips and Federated Learning

Privacy-Preserving Smart Energy Management by Consumer-Electronic Chips and Federated Learning

Smart Energy Management Control Based on Fuzzy Logic Controller in a Standalone Photovoltaic/Wind System with Battery Storage

Energy management control (EMC) becomes imperative in hybrid energy systems that involve multiple sources. The main objective of this study is to concentrate on the effective control and utilization of energy in a hybrid system combining photovoltaic solar and wind sources, together with a battery bank. A sophisticated intelligent supervisor, employing a fuzzy logic controller (FLC), adeptly balances the power generated by the proposed system in response to the load demand. Simulation results demonstrate that, despite the initial maximum battery state of charge value being set at 90%, the battery’s state of charge does not exceed 60% during charging. Additionally, during discharge, the battery state of charge remains above 27%, surpassing the initially set minimum value of 25%. This achievement is attributed to the exchange of energy flow facilitated by the implemented intelligent management. The notable advantage of this intelligent management lies in its ability to precisely control the state of charge (SOC) of the battery across five distinct states, a feat not achievable with traditional management practices that rely on only two SOC levels (SOCmin–SOCmax).

Smart energy management in residential buildings: the impact of knowledge and behavior

A new technology called smart energy management makes use of IoT concepts to enhance energy efficiency and lower waste in structures. The goal of this study is to comprehend how household energy management knowledge affects energy usage, user behavior, related expenses, and environmental effect. Through a survey of 100 valid replies in Palestine, the research model assessed the knowledge and consumption habits of building occupants. Smart PLS software was used to analyze the research model using partial least squares structural equation modeling (PLS-SEM). Using path coefficients and behavior as a mediating variable, the structural model connected the latent variables. The mediation hypotheses were tested using the Preacher and Hayes method, and the indirect effect and confidence intervals were estimated and calculated using bootstrapping. The findings demonstrated that by lowering energy use and enhancing overall building performance, residential buildings that implement smart energy consumption management systems may move toward a more sustainable future. Furthermore, the study found that education and awareness campaigns are necessary to increase residents’ knowledge of these systems to promote energy savings. The results also indicated statistically significant indirect effects, supporting the existence of mediation of the behavior construct. Path coefficient values and P-values were presented to further support the study’s hypotheses. Such smart energy management systems represent an important innovation in building management and can help create more sustainable and efficient buildings.

IoT-enabled Smart Energy Management Device for Optimal Scheduling of Distributed Energy Resources

In this paper, a robust Internet-of-Thing (IoT) enabled Smart Energy Management Device (SEMD) is developed for Distributed Energy Resources (DERs) equipped energy consumers. The SEMD communicates with the existing metering infrastructure through serial communication using the Modbus RTU protocol and the RS 485 communication standard. The SEMD’s energy management framework comprises three sequentially operated software modules, viz., data preprocessing, forecasting, and an optimization module, which aims to minimize the energy costs of end consumers. The software and hardware architecture of the developed SEMD is robust enough to fit real-time implementation and makes it user-friendly. The effectiveness of the SEMD has been tested on an experimental test rig at the Indian Institute of Technology Gandhinagar. The demonstration test rig comprises two DERs equipped energy consumers (viz. Energy consumer-A & B). Energy consumer-A comprises a roof-top solar PV system, Battery Energy Storage System (BESS), and conventional flexible/non-flexible household appliances. The Energy consumer-B consists of Vehicle-to-Grid (V2G) enabled Electric Vehicle (EV), lighting, and pump loads. The experimental results obtained in this work demonstrate that using SEMD satisfies all technical constraints and benefits consumers in reducing bill payments. Thus, SEMD is a critical enabler for DER-equipped energy consumers to participate actively in various demand-side energy management programs.

Fuzzy-based smart energy management system for residential buildings in Saudi Arabia: A comparative study

Considering Saudi Arabia's strategy entails economic diversification and a reduction in dependency on oil, a change towards sustainable and efficient energy management practices is required. Effective energy management is critical for optimising energy consumption, lowering costs, and reducing the environmental footprint. Traditional energy management systems are frequently built on rule-based or deterministic techniques that fail to address the complexity and uncertainty inherent in home energy usage patterns and user behaviours. Fuzzy logic-based solutions have emerged in recent years, holding enormous promise for domestic energy management. Fuzzy logic excels at dealing with imprecise and uncertain input, making it ideal for modelling human-centric systems such as domestic energy management. This study presents a cutting-edge fuzzy-based smart energy management system adapted to Saudi residential buildings. Using fuzzy logic approaches, the system intends to optimize energy usage, reduce peak demand, and improve energy efficiency. It automatically adjusts energy-consuming devices such as air conditioning, lights, and appliances depending on user preferences, occupancy patterns, as well as real-time energy prices using fuzzy decision-making algorithms. The research not only substantiates the success of this strategy through a comparative study, but it also highlights its prospective benefits for Saudi Arabian residential constructions. It also covers the obstacles and potential research possibilities for adopting fuzzy-based smart energy management systems, exposing a promising road towards a more sustainable and safe energy future. The study's findings underscore the superiority of the comparative study alternative, revealing it as the most effective means to evaluate the fuzzy-based system's performance and unique advantages in relation to other established energy management approaches.

EneA-FL: Energy-aware orchestration for serverless federated learning

Federated Learning (FL) represents the de-facto standard paradigm for enabling distributed learning over multiple clients in real-world scenarios. Despite the great strides reached in terms of accuracy and privacy awareness, the real adoption of FL in real-world scenarios, in particular in industrial deployment environments, is still an open thread. This is mainly due to privacy constraints and to the additional complexity stemming from the set of hyperparameters to tune when employing AI techniques on bandwidth-, computing-, and energy-constrained nodes. Motivated by these issues, we focus on scenarios where participating clients are characterised by highly heterogeneous computing capabilities and energy budgets proposing EneA-FL, an innovative scheme for serverless smart energy management. This novel approach dynamically adapts to optimise the training process while fostering seamless interaction between Internet of Things (IoT) devices and edge nodes. In particular, the proposed middleware provides a containerised software module that efficiently manages the interaction of each worker node with the central aggregator. By monitoring local energy budget, computational capabilities, and target accuracy, EneA-FL intelligently takes informed decisions about the inclusion of specific nodes in the subsequent training rounds, effectively balancing the tripartite trade-off between energy consumption, training time, and final accuracy. Finally, in a series of extensive experiments across diverse scenarios, our solution demonstrates impressive results, achieving between 30% and 60% lower energy consumption against popular client selection approaches available in the literature while being up to 3.5 times more efficient than standard FL solutions.

Phonon diffraction and interference using nanometric features

Phonon diffraction and interference patterns are observed at the atomic scale, using molecular dynamics simulations in systems containing crystalline silicon and nanometric obstacles, such as voids or amorphous inclusions. The diffraction patterns due to these nano-architectured systems of the same scale as the phonon wavelengths are similar to the ones predicted by the simple Fresnel–Kirchhoff integral. The few differences between the two approaches are attributed to the nature of the interface and the anisotropy of crystalline silicon. Based on the wave description of phonons, these findings can provide insights into the interaction of phonons with nano-objects and can have applications in smart thermal energy management.