Maximum Energy Efficiency Research Articles

Harnessing Deep Learning and Reinforcement Learning Synergy as a Form of Strategic Energy Optimization in Architectural Design: A Case Study in Famagusta, North Cyprus

This study introduces a novel framework that leverages artificial intelligence (AI), specifically deep learning and reinforcement learning, to enhance energy efficiency in architectural design. The goal is to identify architectural arrangements that maximize energy efficiency. The complexity of these models is acknowledged, and an in-depth analysis of model selection, their inherent complexity, and the hyperparameters that govern their operation is conducted. This study validates the scalability of these models by comparing them with traditional optimization techniques like genetic algorithms and simulated annealing. The proposed system exhibits superior scalability, adaptability, and computational efficiency. This research study also explores the ethical and societal implications of integrating AI with architectural design, including potential impacts on human creativity, public welfare, and personal privacy. This study acknowledges it is in its preliminary stage and identifies its potential limitations, setting the stage for future research to enhance and expand the effectiveness of the proposed methodology. The findings indicate that the model can steer the architectural field towards sustainability, with a demonstrated reduction in energy usage of up to 20%. This study also conducts a thorough analysis of the ethical implications of AI in architecture, emphasizing the balance between technological advancement and human creativity. In summary, this research study presents a groundbreaking approach to energy-efficient architectural design using AI, with promising results and wide-ranging applicability. It also thoughtfully addresses the ethical considerations and potential societal impacts of this technological integration.

Enhancing data rate and energy efficiency of NOMA systems using reconfigurable intelligent surfaces for millimeter-wave communications

In this article, we employ reconfigurable intelligent surface (RIS) to aid a non-orthogonal multiple access (NOMA) system utilizing millimeter-wave (mmWave) communications. Different to recent works on the RIS-supported NOMA systems where the certain carrier frequency was often normalized (fc=1 Hz), we analyze specific impacts of high carrier frequencies in the mmWave band (fc≥30 GHz) on the performance of the RIS-supported NOMA system. In particular, we derive the exact expressions of achievable data rate (ADR) and energy efficiency (EE) of the RIS-supported NOMA system using mmWave band (RIS-mmWave-NOMA system). We compare the ADR and EE of the RIS-mmWave-NOMA system with those of mmWave-NOMA system without RIS, where there are only source-user channels. Numerical illustrations clarify the benefits of utilizing RIS as well as the great impacts of high fc in mmWave band on the ADR and EE of the RIS-mmWave-NOMA system. More specifically, when fc increases from 50 to 100 GHz, the transmit power of source should be increased 8 dBm to achieve the same ADR. Meanwhile, the maximal EE with fc=100 GHz is greatly lower than the maximal EE with fc=50 GHz even though higher transmit power is used. In other words, when fc increases, higher transmit power can be used for the ADR aims but this method cannot be used for EE purposes. In these circumstances, utilizing a larger number of reflecting elements (REs) in the RIS is a good method. By using N≥200 REs, the ADR and EE of the RIS-mmWave-NOMA system are significantly higher than those of mmWave-NOMA system without RIS. Finally, we confirm the exactness of the derived formulas through Monte-Carlo simulations.

Cooling load prediction of a double-story terrace house using ensemble learning techniques and genetic programming with SHAP approach

Since the cooling systems used in buildings in hot climates account for a significant portion of the energy consumption, it is very important for both economy and environment to accurately predict the cooling load and consider it in building designs. This study aimed to maximize energy efficiency by appropriately selecting the features of a building that affect its cooling load. To this end, data-driven, accurate, and accessible tools were developed that enable the prediction of the cooling load of a building by practitioners. The study involves simulating the energy consumption of a mid-rise, double-story terrace house in Malaysia using building information modeling (BIM) and estimating the cooling load using ensemble machine learning models and genetic programming. Categorical Boosting (CatBoost), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Random Forest (RF) models have been developed and made available as an online interactive graphical user interface on the Streamlit platform. Furthermore, the symbolic regression technique has been utilized to obtain a closed-form equation that predicts the cooling load. The dataset used for training the predictive models comprised 94,310 data points with 10 input variables and the cooling load as the output variable. Performance metrics such as the coefficient of determination (R2), root mean squared error (RMSE), and mean absolute error (MAE) were used to measure the predictive model performances. The results of the machine learning models indicated successful prediction, with the CatBoost model achieving the highest score (R2 = 0.9990) among the four ensemble models and the predictive equation. The SHAP analysis determined the aspect ratio of the building as the most impactful feature of the building.

Energy-Efficient Resource Optimization for IRS-Assisted VLC-Enabled Offshore Communication System

In this paper, a downlink energy efficiency maximization problem is investigated in an intelligent reflective surface (IRS)-assisted visible light communication system. In order to extend wireless communication coverage of the onshore base station, an IRS mounted on a unmanned aerial vehicle (UAV) is introduced to assist an onshore lighthouse with simultaneously providing remote ship users wireless communication services and illumination. Aiming to maximizing the energy efficiency of the proposed system, a resource allocation problem is formulated as the ratio of the achievable system sum rate to the total power consumption under the constraints of the user’s data requirement and transmit power budget. Due to the non-convexity of the proposed problem, the Dinkelbach method and mean-square error (MSE) method are adopted to turn the non-convex origin problem into two equivalent problems, namely transmit beamforming and reflected phase shifting. The Lagrangian method and semidefinite relaxation technique are used to obtain the closed-form solutions of these two subproblems. Accordingly, an alternative optimization-based resource allocation scheme is proposed to obtain the optimal system energy efficiency. The simulation results show that the proposed scheme performs better in terms of energy efficiency over benchmark schemes.

Optimal Selection of Distribution, Power, and Type of Luminaires for Street Lighting Designs Using Multi-Criteria Decision Model

This article introduces an innovative design method for public lighting systems that surpasses the limitations of conventional approaches, which rely on predefined lamp characteristics and spatial arrangements. By employing a linear additive model to solve a multi-criteria decision model, our study proposes an optimal design methodology considering several key aspects, including the distance between lamps, their type, power, and light distribution. The goal is to achieve optimal illumination that enhances visibility on public roads for drivers and pedestrians while simultaneously minimizing glare and installation costs and maximizing energy efficiency. The proposed methodology is implemented through an algorithm developed in MATLAB R2023b, with results validated through simulations in DIALux evo 12.0. This information is used to construct a decision matrix, assessed using the CRITIC method across 180 different scenarios within a specific case study. The findings demonstrate the effectiveness of multi-criteria decision-making as a tool for significantly improving the planning and design of lighting in public illumination systems, allowing for selecting the optimal combination of parameters that ensure the best lighting conditions.

Exploring the impact of green logistics practices and relevant government policy on the financial efficiency of logistics companies

The coexistence of carbon neutrality and capitalism can be realized if environmentally friendly management is proven to enhance profitability. The focus of green logistics practices (GLPs) is on maximizing energy efficiency in logistics services, potentially leading to cost reductions for companies. Such practices can be supported through public reports, such as Environmental, Social, and Governance (ESG) reports, and government policies, including government certification. The primary objective of this study is to investigate whether the financial efficiency of logistics companies can be improved through the implementation of GLPs and a relevant government policy. To achieve this objective, a two-stage analysis was conducted using Data Envelopment Analysis - Slack-Based Measure (DEA-SBM) and Tobit regression analysis. The DEA-SBM was employed to assess whether the financial efficiency of logistics companies adopting GLPs and a relevant government policy was superior to that of companies not adopting such practices. Additionally, Tobit regression analysis was employed to analyze the effects of GLPs, ESG reports, and a government policy on the financial efficiency of logistics companies. The findings of the study indicate the necessity for collaboration between the private and public sectors to implement GLPs in a financially positive manner. In conclusion, the research demonstrated that the implementation of GLPs, coupled with public disclosure through ESG reports or equivalents and a government policy, had a positive impact on the financial efficiency of logistics companies.

Distributed DQN-based network-wide energy efficiency maximization for 5G NR-U and Wi-Fi coexistence environments

To effectively manage the escalating traffic, attention is drawn to the coexistence mechanism of cellular and Wi-Fi networks which offload the cellular traffic from the licensed band to the unlicensed band. For the coexistence of 5G new radio-unlicensed and Wi-Fi, we tackle the energy efficiency maximization problem by sequentially determining the duty cycle and transmit power through the use of distributed deep Q-network (DQN) techniques. When utilizing the existing unlicensed band, the proposed method minimizes the impact on Wi-Fi networks while achieving optimal energy efficiency. Simulations validate the superior energy efficiency of the proposed coexistence mechanism over various benchmark methods.

Toward Cooperatively Caching in Multi-UAV-Assisted Network: A Queue-Aware CDS-Based Reinforcement Learning Mechanism With Energy-Efficiency Maximization

Toward Cooperatively Caching in Multi-UAV-Assisted Network: A Queue-Aware CDS-Based Reinforcement Learning Mechanism With Energy-Efficiency Maximization

Evaluation of degradation efficacy and toxicity mitigation for 4-nitrophenol using argon and air-mixed argon plasma jets

This paper investigates the effects of argon (Ar) and that of Ar mixed with ambient air (Ar–Air) cold plasma jets (CPJs) on 4-nitrophenol (4-NP) degradation using low input power. The introduction of ambient air into the Ar-Air plasma jet enhances ionization-driven processes during high-voltage discharge by utilizing nitrogen and oxygen molecules from ambient air, resulting in increased reactive oxygen and nitrogen species (RONS) production, which synergistically interacts with argon. This substantial generation of RONS establishes Ar–Air plasma jet as an effective method for treating 4-NP contamination in deionized water (DW). Notably, the Ar-Air plasma jet treatment outperforms that of the Ar jet. It achieves a higher degradation rate of 97.2% and a maximum energy efficiency of 57.3 gkW−1h−1, following a 6-min (min) treatment with 100 mgL−1 4-NP in DW. In contrast, Ar jet treatment yielded a lower degradation rate and an energy efficiency of 75.6% and 47.8 gkW−1h−1, respectively, under identical conditions. Furthermore, the first-order rate coefficient for 4-NP degradation was measured at 0.23 min−1 for the Ar plasma jet and significantly higher at 0.56 min−1 for the Ar–Air plasma jet. Reactive oxygen species, such as hydroxyl radical and ozone, along with energy from excited species and plasma-generated electron transfers, are responsible for CPJ-assisted 4-NP breakdown. In summary, this study examines RONS production from Ar and Ar–Air plasma jets, evaluates their 4-NP removal efficacy, and investigates the biocompatibility of 4-NP that has been degraded after plasma treatment.

Dynamic Wireless Charging of Electric Vehicles Using RFID Protection

Abstract: Electric vehicle (EV) development in transportation is growing rapidly, offering clean and sustainable transportation. The key challenges in the EV are high cost, limited range, battery recycling, charging infrastructure, and many more. Among these challenges, charging presents a double difficulty: limited availability and slow charging times. To address these challenges, charging infrastructure needs to be expanded, develop wireless charging technologies and advancements in battery technologies. In wireless charging there is static, charging while the vehicle is at rest, and dynamic, charging when the vehicle is in motion. EVs can be charged dynamically while they are moving, maximizing energy efficiency and reducing downtime for charging. In this paper a small-scale prototype for dynamic wireless charging with Radio Frequency Identification (RFID), is discussed. RFID integration enables convenient and secure user access and prevents energy theft by unauthorized charging. RFID tag is positioned at the start of the charging path. The dynamic charging system with RFID protection is shown to be feasible and effective by the prototype. The prototype has to demonstrate its capacity to safely and effectively charge EVs in real time while reducing the possibility of unwanted access or energy theft.

ECO-INNOVATIVE TRANSFORMATION OF THE URBAN INFRASTRUCTURE OF UKRAINE ON THE WAY TO POST-WAR RECOVERY

The study is aimed at summarizing the processes of eco-innovative (green) transformation of urban infrastructure and researching possible prospects for the development of Ukraine in this context. In the course of the research, the possibilities of "green" transformation of urban infrastructure were considered and it was noted that the use of the principles of eco-innovative transformation in the post-war period can only take place under the condition of proper planning, state support and the creation of favourable market conditions. The authors noted that the success of such a transformation requires the establishment of green goals in all aspects of the development of Ukrainian cities. Auto-frame considered the financial possibilities of the development of urban infrastructure and proposed the location of support offices for the eco-innovative transformation of urban infrastructure at the regional level. The principles of achieving eco-innovative transformation of urban infrastructure are revealed, namely maximum energy efficiency, energy transition, "zero waste", environmental sustainability of buildings, adaptation to climate change, popularization of a green lifestyle, resource conservation, citizen involvement and circular economy. It is proposed to create a platform that would unite architects, builders, urban planners, citizens, artists and other interested persons. This platform should contribute to the search for answers to the question of how to ensure a quick, ecological, attractive and safe "green" transformation of urban infrastructure. Ukraine should cooperate with the European Union within various green platforms and networks that help cities in green transformation. All the above-mentioned tools and solutions should contribute to the creation of green, sustainable and people-oriented cities in Ukraine. The authors have considered the possibilities of financing the restoration of Ukrainian cities after the destruction in terms of the necessary financial resources, donor countries, and reconstruction expenditures.

DESIGN AND ANALYSIS OF GO-KART STEERING SYSTEM

This study includes theory, design, and analysis of go-kart steering systems. Usually, owners who want to improve the handling of their go-kart or vehicle will need to buy the latest wheels, tires, and other equipment, only to eventually realize that these things make it worse to drive. The first step to a kart with good handling and maximum energy efficiency is to focus on what is essential. The most common steering arrangement is to rotate the front wheels using a manual steering wheel placed in front of the driver via a steering column. The steering column may contain universal joints (sometimes part of a collapsible steering column design), which can cause slight deviations from a straight line, the Ackerman angle is 12.43°, and the turning radius is 1.7m, resulting in a high steering effect. Key Words: Go-Kart, Steering, Tripod, Solid works (2022), Ansys student version.

Optimization of unsteady jet control flow method for aerodynamic drag reduction of heavy truck model

The aerodynamic drag reduction of ground vehicles is crucial for meeting the increasingly strict emission standards. In this context, a potential approach to achieve this objective is to use active flow control techniques. Many active flow control techniques have been applied for ground vehicles, and the unsteady jet device has been proven to have better control energy efficiency than some other flow actuators. Therefore, this study focuses on applying this unsteady jet technique to a realistic truck model. The main parameters of the control flow device, including jet amplitude, jet angle, and control frequency were optimized to reduce the aerodynamic drag of the truck model and achieve the maximum control energy efficiency. Numerical studies were performed based on the CFD method with the k-ω SST turbulence model. A 25-level optimal Latin Hypercube design experimental study was conducted. The optimization method based on the Kriging response surface model and genetic algorithm was chosen to determine the optimal solution. The results show that the maximum energy efficiency is 9.174 % using the unsteady jet devices with the optimal parameters on the small-scale truck model.

Energy efficiency maximization for active RIS-aided integrated sensing and communication

This paper investigates energy-efficient communication within an integrated sensing and communication system. The system employs a dual-function radar-communication base station. This base station concurrently serves multiple mobile users for communication purposes while also performing target sensing within a designated range cell. An active reconfigurable intelligent surface (RIS) is utilized to enhance communication efficiency. The focus of this work is to optimize the communication energy efficiency by jointly allocating the transmit power and configuring the RIS elements. This optimization is achieved while satisfying constraints on both the probing power required for target sensing and the quality-of-service demands of the communication users. Two novel optimization methods are proposed, combining techniques from alternating optimization, sequential programming, and fractional programming. Through numerical simulations, the effectiveness of the developed algorithms is validated. Additionally, the performance of the system utilizing an active RIS is compared to that of a system with a passive RIS, specifically in terms of their respective communication energy efficiencies.

Crossover Boosted Grey Wolf Optimizer‐based framework for leader election and resource allocation in Intrusion Detection Systems for MANETs

AbstractMobile Ad hoc Networks (MANETs) is a self‐organizing networks without having a fixed infrastructure for making them susceptible to security threats. Intrusion Detection Systems (IDS) promotes security in MANETs by identifying malicious activities. Leader election is a fundamental aspect of IDS deployment, impacting resource allocation and system efficiency. This article presents a novel approach, the Crossover Boosted Grey Wolf Optimizer (CBGWO), for leader election and resource allocation in MANET‐based IDS. The proposed CBGWO algorithm integrates the Grey Wolf Optimizer (GWO) with innovative crossover operators that have an ability to enhance the capabilities of exploration and exploitation in the optimization process. The leader election problem is solved through applying multi‐objective optimization by considering energy consumption, reputation, and communication overhead. Objective functions are defined to maximize energy efficiency while maintaining a balanced distribution of leadership roles. Extensive simulations are conducted, varying network densities and the percentage of selfish nodes. Results demonstrate the effectiveness of the CBGWO‐based model in balancing energy consumption, prolonging network lifespan, and enhancing intrusion detection by comparing different state‐of‐the‐art models such as PCA‐FELM, CTAA‐MPSO, FLS‐RE, LEACH, DCAIDS, WOA‐GA, and VOELA. The proposed model achieved an energy consumption of 4.31 J, network lifetime of 560.482 ms, and average intrusion detection latency of 0.12 s, respectively. The proposed model outperforms than existing random and connectivity‐based leader election methods that is evaluated by taking main consideration of energy efficiency and network survivability. This research contributes to the field by introducing a robust algorithm for leader election in MANET‐based IDS, addressing challenges posed by network dynamics and resource constraints. The CBGWO‐based approach showcases its potential to achieve effective leader election and efficient resource allocation, thereby enhancing the security and sustainability of MANETs.

Dynamic resource allocation for energy-efficient downlink NOMA systems in 5G networks

Non-Orthogonal Multiple Access (NOMA) is a promising energy-efficient technology designed to satisfy the demands of future networks by efficiently sharing radio resources. In NOMA, the same radio resource is simultaneously assigned to two users at different power levels based on the NOMA-power domain. Resource allocation in NOMA presents a non-convex challenge, characterized as a non-deterministic polynomial (NP-hard) problem. This involves user and channel assignment and power allocation, making it an extraordinarily complex task to achieve an optimal solution. In this work, Simulated Annealing (SA) is proposed as an optimization technique for resource allocation in an energy-efficient downlink NOMA system. This resource allocation scheme addresses user and channel assignment, as well as power allocation, using SA as an efficient standalone approach to maximize energy efficiency in NOMA. SA is utilized to execute the assignment of users to channels, distribute the necessary power for each channel, and determine the power ratio among users sharing the same channel. The results obtained demonstrate a significant improvement in energy efficiency, outperforming the existing numerical methods by 22 %. The proposed SA scheme not only achieves a close optimal solution but also in less computational time, offering sufficient reliability in terms of energy efficiency enhancement when compared to the existing numerical method.

An experimental study on the effect of CO2 laser powers on melting characteristics of clear ice − part I: Horizontal irradiation

Icing phenomenon widely exists in traffic and industrial fields, and causes a lot of safety accidents. Laser deicing technology is a typical high-efficiency and non-contact deicing method. To accurately predict and control the laser-induced ice melting process, an experimental study on melting characteristics of clear ice under horizontal CO2 laser irradiation and natural convection is carried out, with the laser power varied at a range of 20 ∼ 60 W. As resulted, in the first 27 s of the melting process, the axial melting rate is around 1.2 mm/s at each power. After 27 s, the laser power effect on the melting rate is obviously different, and the maximum melting rate reaches 5.41 mm/s at 50 W. The higher the laser power, the more the ice melts, and the easier the path of melting ice deflects. At 20 W and 50 W, the maximum angles of melting height are 1.14° and 4.18°, respectively. The laser power effect on the maximum melting height is obvious. At 20 W and 50 W, the peaks of maximum melting height are 12.84 mm and 22.59 mm, respectively. The higher the laser power, the larger the volume melting rate of ice, but the greater the energy loss, and it directly influences the energy efficiency. The ice-melting energy efficiency decreases with the increase of laser power. For laser at 20 W and 60 W, the maximum energy efficiency are 81.5 % and 54.6 %, respectively. This study is meaningful for the optimization of laser deicing technology.

Energy-efficient UAV communication: A NOMA scheme with resource allocation and trajectory optimization.

This work investigates a downlink nonorthogonal multiple access (NOMA) scheme with unmanned aerial vehicle (UAV) aided wireless communication, where a single UAV was regarded as an air base station (ABS) to communicate with multiple ground users. Considering the constraints of velocity and maneuverability, a UAV energy efficiency (EE) model was proposed via collaborative design resource allocation and trajectory optimization. Based on this, an EE maximization problem was formulated to jointly optimize the transmit power of ground users and the trajectory of the UAV. To obtain the optimal solutions, this nonconvex problem was transformed into an equivalent convex optimization problem on the basis of three user clustering algorithms. After several alternating iterations, our proposed algorithms converged quickly. The simulation results show an enhancement in EE with NOMA because our proposed algorithm is nearly 99.6% superior to other OMA schemes.

Optimizing Energy Efficiency in Commercial Buildings through Advanced Building Strategies

The pressing issue of climate change and its correlation with greenhouse gas emissions has propelled a heightened emphasis on energy-efficient building practices, particularly within the commercial sector. This study delves into advanced design strategies aimed at maximizing energy efficiency while upholding quality and comfort standards. By scrutinizing a functioning commercial building in Gurugram, Haryana, data was gathered to construct an energy model using eQUEST. Analysis encompassed various building facets such as envelope components (e.g., walls, roofs, and windows), HVAC systems, lighting, and occupancy patterns. The outcomes furnish valuable insights into pivotal factors influencing energy efficiency and propose tailored strategies for integration into commercial building design. This methodical approach, informed by case evaluation, seeks to optimize energy performance and cost-effectiveness across diverse building types and climates.

Proactive Response Model for App Centric Attack Recognition

Safeguarding strong security while reducing energy usage is still a top priority in the world of smartphone applications. In addition to addressing these important concerns, this work presents a revolutionary paradigm that advances attack modeling and intrusion detection in a proactive manner. Taking advantage of the novel framework of Application-based Behavioral Model Analysis (ABMA), our approach goes beyond traditional security protocols by deeply examining the complex behaviors of mobile applications. Through the smooth integration of advanced intrusion detection systems with ABMA, we present a comprehensive solution that maximizes energy efficiency while strengthening application security. We enable both users and developers to proactively prevent possible threats, including communication-based assaults, by rigorous analysis and modeling. This comprehensive strategy ushers in a new era of mobile security and energy efficiency by protecting sensitive data and user privacy while also ensuring an effective and sustainable ecology for smartphone applications.