Energy Efficiency Optimization Research Articles

Ultra-wideband mm-wave remote antenna unit for radio-over-fiber distributed antenna systems in silicon photonics

This article presents what we believe to be a novel chip-scale 25-45-GHz re-configurable mm-wave remote antenna unit (RAU) for radio over fiber (RoF) distributed antenna systems. The proposed RAU architecture optimizes energy efficiency by operating directly at mm-wave frequencies, and spectral efficiency by selecting re-configurable RF photonic filters topology. Additionally, it achieves frequency agility by rejecting interferes and a small form factor by utilizing the SOI photonics process. Two photonic integrated circuits (PICs) that act as downlink and uplink units are presented while occupying a total area of 12.33 mm2. The downlink selects a single channel within the desired frequency range, while the uplink rejects up to four interferes. The building blocks of the proposed architecture are discussed and their design consideration and parameters are shown. Then, a comprehensive system analysis of the proposed RAU architecture including key performance indicators is presented. A scalable 5-channel system is demonstrated each with a 3-dB Bandwidth of 5-GHz. Moreover, this architecture can be continuously tuned and re-configured within a wide frequency range to cover all 5-channels. To the best of the authors’ knowledge, this is the first wideband modular and re-configurable mm-wave RAU that covers the entire mm-wave sub-45-GHz band implemented in silicon photonics.

ReLeC-based clustering and multi-objective optimization for efficient energy optimization in IoT networks

In response to the escalating demand for energy-efficient wireless sensor networks (WSNs) within the expanding Internet of Things (IoT) landscape, we introduce ReLeC-MO, a novel protocol that integrates the ReLeC clustering algorithm with multi-objective optimization. Leveraging reinforcement learning-based clustering, ReLeC optimizes network topology to enhance energy efficiency. Multi-objective optimization further refines this process by identifying non-dominated solutions on the Pareto front, facilitating a balanced trade-off between network lifetime, energy consumption, and data transmission quality. Our comprehensive simulations reveal the remarkable performance improvements achieved by ReLeC-MO over existing techniques. Specifically, ReLeC-MO demonstrates a 39% reduction in delay, a 50% decrease in energy consumption, and a 25% increase in throughput, showcasing its efficacy in enhancing both energy efficiency and network performance. It also increases network lifetime by 20%, surpassing the latest existing model. Furthermore, its implementation in MATLAB ensures ease of replication and adaptation across diverse IoT applications.

Enhancing efficiency in closed agricultural greenhouses: A data-driven predictive model for energy consumption

Abstract Predicting energy consumption in agricultural greenhouses is essential to effectively allocate resources, enhance plant growth, and minimize energy inefficiencies. Various factors affect the energy consumption inside the greenhouse including external climate conditions and internal microclimate. Proper understanding of these factors is crucial for maintaining an ideal growing environment and optimizing energy efficiency. This drives the need to investigate the interaction between these factors and greenhouse energy consumption, encompassing the energy needed for cooling and the supply of water and nutrients. This work aims at developing a dynamic model that predicts the total energy consumption of a closed agricultural greenhouse to improve microclimate control and energy efficiency. The study is conducted within a closed-loop agricultural greenhouse with no natural ventilation. Inside, the air is cooled and continuously circulated without being exchanged with ambient air through a heating, ventilation, and air conditioning (HVAC) system. The data-driven model encompasses external climate parameters such solar radiation, ambient temperature, and relative humidity; along with microclimate parameters such as internal temperature, humidity, and CO2 concentration to predict overall energy consumption. The study examines two machine learning models, deep neural networks (DNN) and extreme gradient boosting (XGBoost), for forecasting energy consumption, and assesses their performance using the coefficient of determination (R2), the root mean square error (RMSE), and the mean absolute error (MAE). Results reveal that the DNN model surpasses the XGBoost model, exhibiting a superior predictive performance with an R2 of 80.9%, RMSE of 171.1 kWh and MAE of 130.3 kWh. This study demonstrates its practicality in assisting with energy consumption analyses and identifying inefficient energy usage patterns within closed agricultural greenhouses.

Exploring the Application of Digital Twin Technology in the Energy Sector using MEREC and MAIRCA Methods

Smart city sustainability initiatives prioritize creating environmentally, economically, and socially sustainable urban environments. Digital Twin (DT) technology creates precise digital replicas of physical assets, systems, or processes. These digital twins play a crucial role in advancing the goals of smart city sustainability. This paper explores the development and application of DT technology for integrated regional energy systems in smart cities, emphasizing its potential to optimize energy consumption, reduce costs, and enhance overall system performance. The CloudIEPS platform, an energy internet planning platform based on digital twin technology, is a great example of how digital twin technology can be applied in practice, helping optimize energy efficiency and reduce costs. Integrating digital twin technology with the Multi-Criteria Decision-Making (MCDM) methods offers a novel approach to managing and optimizing energy systems in smart cities. The paper aims to create a consistent and robust approach to determining the best digital twin solution for energy systems in smart cities. The paper identifies critical factors for decision-making and establishes a method for assessing the significance of criteria using Triangular Neutrosophic Sets (TNS) through the MEthod based on Removal Effects of Criteria (MEREC) and the Multi-Attributive Ideal Real Comparative Analysis (MAIRCA) approach. These methods are used to evaluate and prioritize multiple criteria in decision-making processes. Furthermore, the methods are combined with Triangular Neutrosophic Sets (TNS) to support decision-making for smart cities' energy systems, better accounting for the complex and uncertain nature of energy systems. A case study is conducted to apply and validate the developed methodology and perform a sensitivity analysis of the experimental results. The research outcomes indicated that the proposed methodology is robust and effective in handling the uncertainty and complexity inherent in smart cities' energy systems. The sensitivity analysis further confirms the stability and adaptability of the proposed methodology across different scenarios, making it a valuable tool for policymakers and stakeholders in the energy sector.

A novel efficient energy optimization in smart urban buildings based on optimal demand side management

Increasing electrical energy consumption during peak hours leads to increased electrical energy losses and the spread of environmental pollution. For this reason, demand-side management programs have been introduced to reduce consumption during peak hours. This study proposes an efficient energy optimization in Smart Urban Buildings (SUBs) based on Improved Sine Cosine Algorithm (ISCA) that uses the load-shifting technique for demand-side management as a way to improve the energy consumption patterns of a SUBs. The proposed system's goal is to optimize the energy of SUBs appliances in order to effectively regulate load demand, with the end result being a reduction in the peak to average ratio (PAR) and a consequent minimization of electricity costs. This is accomplished while also keeping user comfort as a priority. The proposed system is evaluated by comparing it with the Grasshopper Optimization Algorithm (GOA) and unscheduled cases. Without applying an optimization algorithm, the total electricity cost, carbon emission, PAR and waiting time are equal to 1703.576 ID, 34.16664 (kW), and 413.5864s respectively for RTP. While, after applying GOA, the total electricity cost, carbon emission, PAR and waiting time are improved to 1469.72 ID, 21.17 (kW), and 355.772s respectively for RTP. While, after applying the ISCA Improves the total electricity cost, PAR, and waiting time by 1206.748 ID, 16.5648 (kW), and 268.525384s respectively. Where after applying GOA, the total electricity cost, PAR, and waiting time are improved to 13.72 %, 38.00 %, and 13.97 % respectively. And after applying proposed method, the total electricity cost, PAR, and waiting time are improved to 29.16 %, 51.51 %, and 35.07 % respectively. According to the results, the created ISCA algorithm performed better than the unscheduled case and GOA scheduling situations in terms of the stated objectives and was advantageous to both utilities and consumers. Furthermore, this study has presented a novel two-stage stochastic model based on Moth-Flame Optimization Algorithm (MFOA) for the co-optimization of energy scheduling and capacity planning for systems of energy storage that would be incorporated to grid connected smart urban buildings.

Hardware-Aware Energy Efficiency Optimization in Wireless Communications Using a Gearbox-PHY

Hardware-Aware Energy Efficiency Optimization in Wireless Communications Using a Gearbox-PHY

Deep Reinforcement Learning-Based Energy Efficiency Optimization for RIS-Aided Integrated Satellite-Aerial-Terrestrial Relay Networks

Deep Reinforcement Learning-Based Energy Efficiency Optimization for RIS-Aided Integrated Satellite-Aerial-Terrestrial Relay Networks

Smart Home Automation

The Smart Home Concept responds to the increasing need for integrating smart appliances and systems within residential environments. It includes a growing array of devices, services, and applications designed to simplify daily tasks and enhance the quality of life. Utilizing various technologies and standards, numerous device suppliers offer a wide range of solutions, including meters, actuators, sensors, and micro systems, which are integrated into the home environment. This advanced system incorporates sensors, artificial intelligence, and machine learning algorithms to develop an intelligent, responsive, and personalized living space. Continuous sensor data collection on environmental conditions and user behaviors allows AI to autonomously manage various home functions. The system emphasizes interoperability and standardization to ensure compatibility with a wide range of devices. Improvements in natural language processing and voice recognition further enhance human-machine interactions. This comprehensive approach aims to optimize energy efficiency, bolster security, and streamline daily activities, providing residents with a more intuitive and adaptable smart home experience in the evolving field of home automation.

Exploring the potential of swarm intelligence for optimal energy efficiency in IoT downlink system

This study delves into the energy optimization problem in Internet of Things (IoT) networks. We consider the downlink from multiple antenna Gateway (GW) and single antenna IoT devices. For this challenging nonconvex problem, we initially introduced the well-known zero-forcing beamforming (ZFBF) to eliminate inter-user interference, thereby transforming the energy efficiency maximization problem into a concave-convex fractional problem. Then, instead of applying a combination of ZFBF with power allocation, we propose the Particle Swarm Optimization (PSO) algorithm to allocate power to find the optimized beamforming matrix. Through extensive numerical analysis, we demonstrate the effectiveness of our proposed scheme in terms of energy efficiency and power achieved at the GW. The results underscore the significant benefits of our approach over conventional methods, paving the way for practical and efficient energy optimization in IoT networks.

Enhanced Wireless Communication Optimization with Neural Networks, Proximal Policy Optimization and Edge Computing for Latency and Energy Efficiency

This research proposes a novel approach for efficient resource allocation in wireless communication systems. It combines dynamic neural networks, Proximal Policy Optimization (PPO), and Edge Computing Orchestrator (ECO) for latency-aware and energy-efficient resource allocation. The proposed system integrates multiple components, including a dynamic neural network, PPO, ECO, and a Mobile Edge Computing (MEC) server. The experimental methodology involves utilizing the NS-3 simulation platform to assess latency and energy efficiency in resource allocation within a wireless communication network, incorporating an ECO, MEC server, and dynamic task scheduling algorithms. It demonstrates a holistic and adaptable approach to resource allocation in dynamic environments, showcasing a notable reduction in latency for devices and tasks. Latency values range from 5 to 20 milliseconds, with corresponding resource utilization percentages varying between 80% and 95%. Additionally, energy-efficient resource allocation demonstrates a commendable reduction in energy consumption, with measured values ranging from 10 to 30 watts, coupled with efficient resource usage percentages ranging from 70% to 85%. These outcomes validate the efficacy of achieving both latency-aware and energy-efficient resource allocation for enhanced wireless communication systems. The proposed system has broad applications in healthcare, smart cities, IoT, real-time analytics, autonomous vehicles, and augmented reality, offering a valuable solution to optimize energy consumption, reduce latency, and enhance system efficiency in these industries.

Performance of Shading Against West Glass Facades to Optimise Daylight, Thermal Comfort and Energy Efficiency of Office Buildings

In tropical urban areas, the vertical facades of buildings often play a crucial role in capturing solar radiation and heat, especially for office buildings facing west during the afternoon. In Dhaka, a tropical city, the construction of major arterial roads connecting the northern and southern regions of the city has led to the emergence of numerous commercial buildings along these roads, predominantly facing in east or west directions. This research investigated the efficiency of different existing fenestration types (glass curtain walls, glass windows with horizontal and vertical louvres, overhangs, vertical fins, and egg crate shading) of west-facing office buildings in Dhaka in terms of daylight, thermal comfort and energy efficiency, and explore strategies to enhance existing performance. From the field survey, a 'case office building' was selected, and simulations were carried out with the six different fenestrations, as mentioned above, while keeping other aspects constant as found during the survey, e.g., floor plan, equipment and number of occupants. In this research, the simulation process encompasses daylighting simulation, energy simulation, and multi-objective optimisation. The study utilised a case model created with Rhinoceros and ClimateStudio, while performance metric optimisation was achieved using Grasshopper and Octopus. Multi-objective optimisation techniques were employed to improve the shading configurations. The TT toolbox facilitated data exportation and Design Explorer assisted in data visualisation. The window with egg crate shading was found to be the most effective fenestration type among the studied shading configurations. Further parametric simulation was conducted to develop an optimised egg crate shading design configuration. It was found that egg crate shading with a 1.0 m depth provided optimum daylight and solar heat gain inside the office building among the studied configurations in the context of Dhaka. A comparison was also made between the optimised egg crate option and a customised egg crate with some detailing (popularly known as brise soleil). Brise soleil showed better performance with 12.2% higher sDA and 98 lux additional average illuminance compared to the previous optimum one. The findings underscore the significance of employing optimisation methodology to devise shading configurations while also revealing substantial opportunities to enhance performance further by tailoring details on the optimised design recommended from simulation analysis.

Investigating enhanced thermal comfort and energy efficiency through strategized airflow in Micro-Zonal Occupant-Centric Control (MZOCC)

The potential of Micro-Zonal Occupant Centric Control (MZOCC) to save HVAC energy consumption in medium sized open-plan offices has been established in literature. This is done by virtually dividing a thermal zone into micro-zones and allowing independent diffuser-level control in each micro-zone when the micro-zone is occupied. In continuation, there is a need to ensure that thermal comfort is achieved within micro-zones under such localised control. This is important because several forms of discomfort such as draft, horizontal thermal asymmetry and vertical thermal gradients may arise in MZOCC. Further, the effect of MZOCC airflow control strategies, such as setback flow and setback temperature, which were proposed in previous studies on thermal comfort of occupants needs to be evaluated. In an attempt to address these research gaps, this study explores thermal comfort for different scenarios of diffuser arrangements, airflow control strategies and occupancy conditions and also explores the effect of varying supply velocities used in setback flow and setpoint temperatures used as setback temperatures on air distribution and thermal comfort.Results show that low PMV values, increased draft discomfort and heavy thermal gradients are observed for certain scenarios of MZOCC. Tuning airflow control by choosing appropriate setback flow or setback temperature helps in restoring thermal comfort. As different forms of discomfort are observed in different cases, it is important to plan micro-zones and control airflow, such that, MZOCC does not violate thermal comfort criteria while optimising energy efficiency. This study gives a framework for selecting appropriate airflow control strategies for a selected diffuser arrangement for a given occupancy condition. Comfort-based MZOCC that uses appropriate airflow control strategy saves about 60% energy when the zone is half occupied and about 85% energy when the zone is quarter occupied. Results also show that, improving the diffuser arrangement improves energy savings and increases the options of airflow control strategies that can be used. Also, different airflow control strategies behave differently for each diffuser arrangement and has to be chosen separately for given diffuser setting and occupancy condition.

Optimizing energy efficiency in multi-chiller systems: A comprehensive Modelica-based approach

To achieve sustainable development of buildings, this paper investigates the energy-saving potential of multi-chiller systems by examining the interactions between sequencing optimization, load optimization, and parameter optimization in systems with varying capacities. A Modelica-based simulation platform, adaptable to various programming languages optimization engines, was developed for this purpose. The study introduces a sequencing optimization threshold function that considers both wet-bulb temperature and cooling load. Additionally, a two-stage optimization strategy that prioritizes parameter adjustments before sequencing is proposed. Case studies demonstrate that this threshold function and reversed optimization order can further enhance overall energy savings. Focusing on a system with three large chillers and one small chiller, the study reveals the energy benefits of activating the small chiller under specific conditions, improving overall system efficiency. The proposed optimization achieved a cumulative energy saving of 650,463 kWh over the summer, with sequencing contributing 5.64 %. Frequency conversion control of the water pump is identified as a significant energy-saving factor. This research provides a comprehensive approach to optimizing diverse chiller systems, highlighting substantial potential for energy conservation.

Regulating cutting fluid parameters for optimal energy and economic performance: Methods for efficient and Low-Energy electrical machining

Scholars today have been dedicated to researching economically viable and efficient methods to enhance the energy efficiency of manufacturing, aiming for sustainable production. Wire Electrical Discharge Machining (WEDM) holds a significant position in current mechanical processing due to its unique operational principle, which results in notably higher energy consumption compared to conventional processing methods. Various factors influencing machining performance are complex, among which the condition of the cutting fluid plays a crucial role. Effectively controlling the parameters of the cutting fluid to improve its discharge state during machining is vital for enhancing energy and economic efficiency. Specifically, this study analyzes the characteristics of the WEDM process, models its resource and energy consumption, and predicts its economy. A method for optimizing control of cutting fluid parameters is proposed. By correlating different cutting fluid parameters with machining performance using the selected machine tool, and recommending combinations of cutting fluid parameters that ensure quality while improving the material removal rate, the study aims to achieve optimal energy efficiency and minimal cost. The results show that the proposed models for energy efficiency and economic prediction have an average accuracy of 96.64 %. An optimized machining scheme selected reduces machining time and energy consumption by approximately 1255.6 s (about 19.14 %) and 2240112.92 J (about 17.48 %), respectively, and saves about ¥ 6.084 (approximately 14.90 %) in machining costs. Additionally, the energy consumption of the pulse system’s proportion of total energy use increased by 1.63 %. The research demonstrates a general method for specifying combinations of cutting fluid parameters aimed at optimal energy efficiency and economy during the process optimization design stage of machining, providing a reference for improving the energy efficiency of wire cutting operations.

Energy efficiency optimization of NB-IoT using integrated Proxy & ERAI technique

The 3rd Generation Partnership Project (3GPP) standardized the NarrowBand Internet of Things (NB-IoT) technology in Release-13, aiming to support applications requiring minimal device power consumption, efficient small data transfer, and tolerance for delays. This paper provides two major contributions: first, it characterizes the NB-IoT architecture and its enhancements in subsequent 3GPP releases; second, it introduces an energy-saving technique for NB-IoT user equipment based on semi-Markov processes. Integrating a Proxy state and an enhanced release assistant indication (ERAI), the proposed technique improves upon the traditional Discontinuous Reception (DRX) method in the NB-IoT standard. This novel approach reduces energy consumption during low data communication, distinguishing it from other power-saving methodologies. The effectiveness of this technique is assessed through the power-saving factor (PSF) under various conditions, such as different eDRX durations (T_eDRX), PSM durations (T_PSM), DRX cycle periods (T_ACTIVE+T_SLEEP), and DRX active periods (T_ACTIVE). Our findings confirm that the proposed Proxy & ERAI technique significantly boosts the power-saving capabilities of NB-IoT devices, especially for small data packet transmissions. Theoretical and simulation results indicate that the PSF can be enhanced by up to 99.4% with optimized eDRX durations (T_eDRX) and up to 99.9% with optimized PSM (T_PSM) durations. These results underscore the significant energy-saving potential of our proposed technique, making it highly effective for NB-IoT applications with low data transmission needs.

Challenges and Strategies for Achieving High Energy Efficiency in Building Districts

Achieving climate neutrality requires reducing energy consumption and CO2 emissions in the building sector, which has prompted increasing attention towards nearly zero energy, zero energy, and positive energy communities of buildings; there is a need to determine how individual buildings up to communities of buildings can become more energy efficient. This study addresses the scientific problem of optimizing energy efficiency strategies in building areas and identifies gaps in existing theories related to passive design strategies, active energy systems, and renewable energy integration. This study delineates boundaries at the building and community scales to examine the challenges of attaining energy efficiency goals and to emphasize the intricate processes of selecting, integrating, and optimizing energy systems in buildings. The four boundaries describe: (B1) energy flows through the building envelope; (B2) energy flows through heating, ventilation, air conditioning and energy systems; (B3) energy flows through individual buildings; (B4) energy flows through a community of buildings. Current theories often treat these elements in isolation, and significant gaps exist in interdisciplinary integration, scalable frameworks, and the consideration of behavioral and socioeconomic factors. Achieving nearly zero energy, zero energy, and positive energy communities requires seamless integration of renewable energy sources, energy storage systems, and energy management systems. The proposed boundaries B1–B4 can help not only in analyzing the various challenges for achieving high energy efficiency in building communities but also in defining and evaluating these communities and establishing fair methods for energy distribution within them. The results demonstrate that these boundaries provide a comprehensive framework for energy-efficient designs, constructions, and operational practices across multiple buildings, ensuring equitable energy distribution and optimized performance. In addition, the definition of boundaries as B1-B4 contributes to providing an interface for energy-efficient designs, constructions and operational practices across multiple buildings.

Readiness of artificial intelligence technology for managing energy demands from renewable sources

The use of artificial intelligence (AI) has gained tremendous popularity in recent years, and it has become ubiquitous for use in the energy sector. The newly emerging digitalised tools are reliant on the use of AI which offers seamless possibilities for improved connectivity across the energy supply chains, trade and end-use. In the near course, the integration of energy supply, demand and renewable sources into the power grid will be controlled autonomously and this will aid in swift decision-making processes. This review focuses on studies that highlight the realm of AI to benefit the energy sector as a key enabler to the growth of renewable energy sources from wind, solar, geothermal, ocean as well as hydrogen-based energy storage. The work presented here alludes to an AI based energy management approach in the context of CO2-neutral hydrogen production and storage landscape. A major intended outcome of this review is that it would allow the readers to compare their AI efforts, ambitions, state-of-the-art applications, challenges, energy efficiency optimization, predictive maintenance control and global roles in policymaking for the renewable energy sector. Finally, observations and ideas for future research, enhancements and investigations through a summary of key discussions are also made.

Optimizing Energy Efficiency in Edge-Computing Environments with Dynamic Resource Allocation

Optimizing Energy Efficiency in Edge-Computing Environments with Dynamic Resource Allocation

Thermally reliable compact electro-optic modulators with a low half-wave voltage

Recent advancements in thin-film lithium niobate have led to the development of high-performance integrated electro-optic modulators, which are crucial for modern optical communication systems. These modulators offer tighter mode confinement, a smaller physical footprint, and reduced modulating voltages. This study presents a Mach-Zehnder modulator (MZM) on a silicon nitride-loaded lithium niobate platform using a few-mode waveguide structure. By harnessing the exceptional thermo-optic and electro-optic effects of LiNbO3, we design and simulate this modulator employing multilayer structures with the BeamPROP solver. The modulator has a length of 3.94 mm, a Vπ value of 0.96 V, and a transition temperature (Tg) of 80 °C at 1.55 µm. This proposed modulator exhibits a crosstalk of approximately -42 dB, an extinction ratio of approximately 24 dB, and a maximum transmission of -28 dB for the first-order phase shift. These findings demonstrate the significant potential of this modulator for deployment in high-speed optical communication systems, where maintaining thermal stability and optimizing energy efficiency are paramount.

Energy efficiency evaluation and optimization for wastewater treatment plant

Wastewater treatment plants (WWTPs) are considered as energy-intensive industries. A comprehensive assessment of energy efficiency in sewage treatment reveals issues of energy waste, offers insights into the energy consumption structure, fosters optimization of energy management, and enhances overall energy utilization. However, the mathematical modeling for energy efficiency evaluation becomes challenging due to the ambiguity and uncertainty of the parameters regarding energy consumption and various indicators. In this paper, the newly constructed evaluation method was used to quantify the energy efficiency. This method employs dimensional reduction, projecting the numerical values of unit energy consumption for various pollutants onto a lower-dimensional space, and then computing the projected eigenvalues. Larger eigenvalues indicate higher energy efficiency. Based on the evaluation results, the back propagation (BP) neural network is used to simulate the sewage treatment process. The results demonstrated that when the inflow load is small and the concentration of pollutants is high, the energy efficiency of sewage treatment plants performs well. When the inflow load is high and the concentration of pollutants is low, the energy efficiency of sewage treatment plants is poor. Meanwhile, the energy consumption could be decreased by 11.61 %, 14 %, and 15.26 % respectively in different scenarios.