Energy Savings Research Articles

Seasonal Thermal Management and Fire-Resistant Aramid Nanofiber/MXene-NH2 Aerogel for Enhanced Safety and Year-Round Energy Saving

Seasonal Thermal Management and Fire-Resistant Aramid Nanofiber/MXene-NH₂ Aerogel for Enhanced Safety and Year-Round Energy Saving

From data silos to seamless integration and coordination: a data-asset centric approach to smart hospital facility management

PurposeExisting hospital building operations involve numerous information technology applications and complex building systems; therefore, an intelligent facility management (FM) platform is required to ensure their continuous operation. To address the persistent issues of data silos, inefficient data interoperability, and workflow incoordination that have been identified in the current body of FM practice and literature, the present study develops a data-asset (DA) centric FM platform specifically designed for hospital buildings.Design/methodology/approachThis study proposes a semi-customized approach to develop the DA-centric FM platform for hospital buildings. To elucidate the precise function requirements of the hospital FM platform, focus group interviews are employed. By seamlessly integrating the as-built BIM model, IoT sensor data and FM workflow data, the BIM-based DA model with a data transfer mechanism is developed. The development of the FM platform with function modules in a case study is guided by a five-tier architecture and the coordination theory (CT). The case study provides an in-depth introduction to the applications of DA management, space management and maintenance management modules.FindingsThe capabilities of the developed DA-centric hospital FM platform are validated through the case application and user satisfaction survey, which assess data quality, automation level, operation efficiency, flexibility and functionality. For hospital FM activities, this DA-centric FM platform realizes data integration and seamless transformation, optimizes workflow coordination and enhances operation performance.Originality/valueThe initial scholarly contribution is the establishment of the BIM-based DA model, which serves as the data middle platform for continuous data integration, transmission and sharing within the FM platform. Subsequently, under the guidance of the CT, the business process of function modules is designed, improving the intra-module and inter-module workflow coordination. The developed DA-centric FM system along with its performance benchmarking application, assists facility managers and decision-makers in implementing smart operations for hospital buildings and achieving the management goals of safety, efficiency, energy savings and convenience.

Energy-efficient FDM printing of sustainable polymers: Optimization strategies for material and process performance

The integration of sustainable polymers in fused deposition modeling (FDM) 3D printing offers a promising pathway toward reducing the environmental impact of additive manufacturing. However, the energy-intensive nature of FDM processes presents a significant challenge to the overall sustainability of this technology. In this study, we explore the use of bio-based and recycled polymers in FDM printing and develop optimization strategies to reduce energy consumption without compromising material and print performance. Our results demonstrate that by systematically optimizing key printing parameters such as extrusion temperature, print speed, and layer height it is possible to achieve up to 20% energy savings. Additionally, we find that novel material formulations and advanced thermal management techniques enhance the mechanical properties of printed objects by up to 15%, all while minimizing energy use. This research not only advances the field of sustainable 3D printing but also provides a framework for the development of next-generation materials and processes that align with the principles of a circular economy.

Techno-economic process design of multi-stage flash- reverse osmosis for water and power production using solar-wind resources

This paper introduces a newly developed hybrid power plant design that combines renewable energy sources to produce both electricity and freshwater. The design offers a higher gain output ratio (GOR) and improved efficiency in power and freshwater production, all at a lower cost compared to existing research papers. To generate power, the power plant incorporates a range of technologies, such as a horizontal-axis wind turbine (WT), photovoltaic panels (PV), parabolic trough collectors (PTC), a steam Rankine cycle, a multi-stage flash (MSF) desalination unit that cleverly harnesses excess heat from the Steam Rankine cycle, and an Reverse osmosis (RO) unit. With the assistance of ASPEN Plus, ASPEN Economic, and HOMER PRO software, the process parameters undergo significant improvement. According to the results, the power plant has the capacity to generate 57.63 MW of electricity and produce 2505 m3h of freshwater. Additionally, it has a GOR of 9.09 and utilizes 7285 m3h of seawater, which has a renewability factor of 97.6 %. In addition, the efficiency of the steam Rankine cycle stands at 32 %, providing notable energy savings. The production costs for electricity and freshwater are 0.037 $kW and 1.38 $m3, respectively, indicating their affordability. The economic analysis reveals promising results for the project, with an anticipated annual profit of 41 % and a relatively short capital payback period of 2.4 years. By implementing the proposed hybrid cogeneration power plant, the region can tackle its water and energy scarcity issues with a sustainable and long-lasting solution.

Development of near-optimal advanced control sequences for chiller plants with water-side economizers in U.S. Climates (ASHRAE RP-1661)

Various advanced control sequences for chiller plants with water-side economizers (WSE) have been proposed in literature, but the evaluation and optimization of those controls is limited. It is possible to maximize energy savings by selecting different sequences and related parameters based on the plant configuration, load, and climate. This paper addresses this gap by developing near-optimal advanced control sequences for chiller plants with WSEs. First, advanced control sequences for chiller plants with WSEs are categorized into condenser water, chilled water, and hybrid controls and representative sequences from each category are identified. Next, 504 different scenarios are optimized. These scenarios represent all possible combinations of two plant configurations, a constant or variable load profile, three advanced control sequences, and seven optimization parameter combinations in six climate zones. The results show the recommended near-optimal sequences can reduce energy consumption by up to 15% relative to the baseline depending on the configuration, load profile, and climate. Specifically, the CW-CHW sequence is recommended for the majority of systems because it is often the most energy efficient and/or reduces the runtime of chillers. The methodology in this paper provides practical guidance for achieving energy savings through near-optimal control of chiller plants with WSEs.

Assessing energy savings and visual comfort with PDLC-based smart window in an Istanbul office building: A case study

Lighting applications in architectural structures have a high share in energy consumption. Inefficient lighting applications and technologies will increase energy consumption and do not guarantee the visual comfort desired by users. Recognizing the importance of user comfort, this study takes a deeply user-centric approach in proposing feasible lighting control methods that can improve energy efficiency in smart and carbon-neutral future buildings and change daylight's benefits in favor of the user. Smart glass technology, which can adjust the color and transmittance of the light in response to external stimuli (applied voltage) and can be dynamically modulated, changing window and door characteristics, has high potential in the market for modern societies. This paper develops an innovative lighting control method to improve energy efficiency and visual comfort and reduce energy consumption. The daily performance of the proposed control methods regarding illuminance, uniformity factor, and energy consumption is experimentally analyzed in a test bench modeling an office environment with IG80 glass (for comparison), artificial lighting source (LED lamp), and polymer-dispersed liquid-crystal (PDLC)-based smart window. The research methodology involved a detailed analysis of the relationship between performance metrics and dimmer percentages and comparing the energy consumption performance of the lighting methods with other design parameters and technological advances. This algorithm prioritizes user comfort and considers the PDLC-based smart window light transmittance and LED lamp dimmer control depending on daylight. The results show that the proposed algorithm can increase energy savings by up to 22 %. While efficient lighting control is closely related to weather conditions and indoor specifications, it has similar energy consumption performance compared to research into design parameters and technological advances, eliminating further efforts.

Estimation of biogas potential from poultry manure to 2040 in Türkiye using time series and the artificial neural network (ANN)

The number of poultry in Türkiye constituted approximately 24 % of Europe’s total in 2020. Although Türkiye has a significant number of poultry animals, it has been relatively unsuccessful in terms of managing the associated poultry waste. The present study focuses on poultry manure-based biogas potential (PMBP), which represents an alternative means of poultry manure management in Türkiye. PMBPs were calculated for each year from 2003 to 2021, for which it was found that for 2021, Türkiye's PMBP had increased by 40 % since 2003, reaching 3469 GWh. The possible electrical energy savings that PMBP could provide for lighting, habitation, business, government offices, and industry throughout Türkiye were calculated, with PMBP spatial distribution maps for 2021 created according to geographical region and province. The province with the highest PMBP was found to be Manisa, at 390 GWh. The projected PMBPs were calculated to 2040 using the artificial neural network (ANN) and autoregressive integrated moving average (ARIMA). According to ARIMA, the PMBP for 2040 could potentially be increased by as much as 32 % from that of 2021, while according to ANN predictions, this same increase could be as much as 72 %. The ability to reduce greenhouse gas emissions resulting from poultry manure management in Türkiye through biogas production was calculated. Investors may need to prepare a feasibility study for the establishment of a biogas plant in a suitable region within Manisa province, as it has the highest PMBP among the regions evaluated.

Impact of Ad Blockers on Computer Power Consumption while Web Browsing: A Comparative Analysis

This study explores the impact of various ad blockers on power consumption during web browsing, focusing on different types of online content. By analyzing power use across ten popular websites, the study assesses the performance of five widely utilized ad blockers: AdBlock, AdBlock Plus, uBlock, uBlock Origin, and uBlock Origin Lite. Power consumption was measured under controlled conditions, comparing scenarios with and without ad blockers to gain insight into their efficiency. The findings indicate substantial differences in power savings, with some ad blockers significantly reducing power usage, particularly on media-heavy sites, while others unexpectedly increased consumption under certain conditions. The study underscores the potential of ad blockers to enhance power efficiency in digital environments, highlighting the importance of optimizing ad-blocking techniques to reduce the environmental impact of online activities. Through comprehensive analysis and comparison, this research offers insights into selecting effective ad blockers to minimize power consumption, promoting more sustainable web browsing practices.

New Lightweight Cryptosystem for IoT in Smart City Environments

Internet of Things (IoT) devices, user interfaces (UI), software, as well as communication networks are all deployed within Smart Cities topology. The security approach designed for Internet of Things IoT should be able to prevent and detect both internal and external attacks. The problem in IoT network that not every linked node or device has an adequate amount of processing power. This means that data encryption and other related activities will be impossible and means that the security of any kind must be lightweight. A trustworthy security solution that stops illegal access to private data on the network is necessary for maintaining the privacy of information on the Internet of Things. Cryptographic processes need to be quicker and more compact without sacrificing security. The aim of this study is to reduce the execution time and power consumption of encryption processes without compromise the complexity of the encryption algorithm. This research presents a new lightweight cryptographic technique to protect various multimedia and real-time traffics across IoT network, by using two S-box in SubByte of encryption process, without affecting its performance. In this study, different audio samples will be used to test the new algorithm efficiency. Comparing the suggested method to the most advanced standard algorithm, it can reduce the cryptography process's execution time as well as energy consumption while maintaining the required security level. The outcomes demonstrate good performance in terms of power usage and delay. The new technique consumed a roughly 0.2 µJ for encryption process while the typical AES algorithm consumed 0.29 µJ, this mean the new algorithm achieved (33% power savings), while maintaining a good complexity level (security) within the process of encryption according to the results in tables I, II, and the comparison in table III. The novelty of this work can be showed by using dual XOR S-box technique which increased the complexity of SubByte process making it more secure without overload the processing performance, in addition to the reduction in encryption rounds which contribute to enhance the performance without compromise the security. Making it more suited for the Internet of Things (IoT) used in smart city environments.

Complementary Analysis and Performance Improvement of a Hydro-Wind Hybrid Power System

Hydropower as a flexible regulation resource is a rare choice to suppress the ever-increasing penetration of wind power in electrical power systems. The complementary characteristics and performance improvement of a hydro–wind hybrid power system based on a mathematical model of the hybrid power system is studied in this paper. This established model takes into account the stochastic variation in wind speeds in the wind power subsystem and the hydraulic–mechanical–electrical coupling characteristics of the hydropower subsystem. The complementary analysis is conducted based on the evaluation variables outputted by the established model, such as the wind power, hydro-regulation power, hydraulic power, and frequency. To make full use of the regulation capability of the hydropower system, the optimization of parameter settings is also carried out to improve complementary performances of the hybrid power system. The results from the complementary analysis show the detailed characteristics of hydro–wind coordinated operation under different types of real wind speeds. Here, 95% of installed hydro-capacity is used to complement the power shortage of the intermittent wind energy under the low wind speed. Alternatively, only around 66% of the installed hydro-capacity can be utilized to cope with the fluctuation in wind power under the medium and high wind speeds before the optimization of parameter settings. The recommended values and change rules of the control, hydraulic, and electrical parameters for the hydropower system are subsequently revealed from the analysis of parameter settings to contribute to a stable and safe hybrid power system. The results show that the optimized parameter can increase the maximal regulating capacity of the hydropower system by nearly 9 MW, approximately a sixth of the total installed hydropower capacity. The method and results obtained in this paper provide theoretical and technical guidance for the safe and economical operation of power stations.

Energy efficient permanence‐based community detection algorithm

SummaryDetecting an accurate community structure is a crucial task in network analysis. With the increasing popularity of social networking sites, it is essential to have a community detection algorithm that is not only efficien but also cost‐effective for running in data centers. There are several metrics for estimating the accuracy of community detection. However, previous research has shown that permanence, a vertex‐centric metric, provides the most precise estimate of a community structure compared to other approaches. Despite this, no study has been conducted on parallelizing a permanence‐based community detection algorithm and analyzing its energy efficiency. This article introduces Amoeba, a task parallel implementation of a permanence‐based community detection algorithm designed for multicore processors. It uses dynamic tasking to schedule the inherent irregular computation, and it can dynamically adapt the total number of parallel threads, which results in improved energy efficiency. We evaluated Amoeba using several real‐world and artificial graphs on a multicore server processor. Our experimental results show that Amoeba achieves a geometric mean speedup of 15.3 over its sequential implementation, and due to thread adaptability, it achieves energy savings of 12.4% and a speedup of 6% over its nonadaptive implementation.

A fast quadrupole-based analytical model for solving transient heat transfer in ventilated double-skin walls and assessing their energy saving potential

This research proposes a fast and accurate method for modeling transient heat transfer in ventilated double-skin façades (VDFs) with forced ventilation to predict their energy-saving potential. Using the thermal quadrupole formalism, the method solves the VDF problem in the Laplace domain while considering the full transient nature of the heat transfer; the only approximation is in space. The solution involves obtaining a general transfer function that predicts heat transfer rates or air temperatures at ventilated cavity's exit. The quadrupole method is validated against computational fluid dynamic numerical simulations conducted under similar meteorological, design and boundary conditions. A very good agreement was found (less than 3 % average deviation) with a significant reduction in computation time (less than 3s against 6h for CFD calculations). The model is computationally efficient and can consider important factors such as the opacity of the walls, construction materials, and air cavity design parameters. Finally, the model allows the assessment of the energy-saving potential of VDFs under various scenarios compared to conventional systems, which helps contributing to more sustainable building design practices. The energy efficiency of a VDF configuration was compared against a conventional wall system. Energy savings of 8.4 and 5.2 % were obtained, in cold and hot climates respectively.

ENERGY AUTOMATION SUPPORTS ENERGY CONSUMPTION INTENSITY IN CLASSROOMS

Automation of energy use to control energy consumption intensity (IKE) in classrooms is carried out using an Arduino Nano-based system. This system utilizes an IR transmitter to send signals to the air conditioning unit, a servo motor to position the IR transmitter to the appropriate air conditioning unit, a relay to control the lights, and a Real-Time Clock (RTC) module for scheduling based on a specified schedule. The main goal is to prevent energy waste and ensure comfort by turning on and off the air conditioning unit and lights according to a preset schedule. This automatic scheduling includes time setting, transmitter IR control, servo motor, relay control, and LCD. Programmed scheduling ensures that the air conditioner and lights only turn on when needed, while the LCD provides real-time information about the status of the air conditioner and lights. With this automation, the system helps reduce excessive energy consumption, drives significant energy savings, and creates a comfortable and energy-efficient learning environment. The automatic monitoring and regulation system also allows users to focus more on teaching and learning activities without having to worry about manual settings for air conditioning and lights.

Green Campus Transformation in Smart City Development: A Study on Low-Carbon and Energy-Saving Design for the Renovation of School Buildings

In the context of increasingly deteriorating global ecological conditions and rising carbon emissions from buildings, campus architecture, as the primary environment for youth learning and living, plays a crucial role in low-carbon energy-efficient design, and green environments. This paper takes the case of Yezhai Middle School in Qianshan, Anhui Province, to explore wind environment optimization and facade energy-saving strategies for mountainous campus buildings under existing building stock renovation. In the context of smart city development, integrating advanced technologies and sustainable practices into public infrastructure has become a key objective. Through wind environment simulations and facade energy retrofitting, this study reveals nonlinear increases in wind speed with building height and significant effects of ground roughness on wind speed variations. Adopting EPS panels and insulation layers in facade energy retrofitting reduces energy consumption for winter heating and summer cooling. The renovated facade effectively prevents cold air intrusion and reduces external heat gain, achieving approximately 24% energy savings. This research provides a scientific basis and practical experience for low-carbon energy retrofitting of other campus and public buildings, advancing the construction industry towards green and low-carbon development goals within the framework of smart city initiatives.

Sustainability Assessments of Living Walls in the Mediterranean Area

The evaluation of the environmental impacts of a living wall (LW) system cannot leave out the effects on the buildings’ sustainability during its life cycle. Consequently, to consider the embodied impacts of all the components, an LCA of the whole system is recommended. Therefore, this paper was targeted to evaluate the environmental performances of four LW solutions considering a combination of energy performances and system LCA. In the first step, the yearly energy consumption of a reference building equipped with LWs was determined through simulations carried out in the EnergyPlus environment. Subsequently, the LCA completed the evaluation of the environmental impact of the different building configurations. To quantify the effects related to the implementation of LWs, the results were compared to those obtained for the reference building without vegetation. The analyses refer to a real university’s four-floor housing located in the city of Reggio Calabria (38°07′21.4″ N 15°39′45.2″ E), which is currently under construction. The results confirm the benefits achievable through the LW integration, measuring both energy savings and the increase in the hours with indoor temperature within the well-being zone. At an annual level, the best LW solution produces 24% of energy saving and LCA highlights a limitation of 2.1 × 105 kg of CO2eq when compared to the reference case. These data confirm the effectiveness of implementing LWs as a reliable intervention to enhance building environmental impact, especially for edifices requiring renovation.

Two‐stage low‐carbon scheduling of integrated energy system based on carbon emission flow model

AbstractUnder the influence of environmental pollution and energy scarcity, integrated energy systems (IES) have received extensive attention in the field of energy supply due to their ability to consume renewable energy and enhance energy utilization. In the context of low‐carbon scheduling for IES, numerous studies calculate the system's carbon emissions based on the carbon emission coefficients of energy devices. However, IES is a multi‐energy coupling system in which a device's energy input can originate from multiple sources with varying degrees of carbon emissions, making it difficult to accurately calculate the resulting carbon emissions using fixed coefficients. Consequently, a carbon emission flow (CEF) model is constructed for the system to calculate carbon emissions. In addition to the basic input–output CEF model, the CEF model for energy storage devices is considered, and carbon emission constraints during system operation are formulated based on the CEF model. Furthermore, many studies on low‐carbon scheduling of IES overlook the uncertainties associated with load and renewable energy. Therefore, a two‐stage scheduling model consisting of day‐ahead stage and intra‐day stage is developed to achieve reliable energy supply. Finally, through experiments, the low‐carbon performance and reliability of the model are validated.

Implementing PSO-LSTM-GRU Hybrid Neural Networks for Enhanced Control and Energy Efficiency of Excavator Cylinder Displacement

In recent years, increasing attention has been given to reducing energy consumption in hydraulic excavators, resulting in extensive research in this field. One promising solution has been the integration of hydrostatic transmission (HST) and hydraulic pump/motor (HPM) configurations in parallel systems. However, these systems face challenges such as noise, throttling losses, and leakage, which can negatively impact both tracking accuracy and energy efficiency. To address these issues, this paper introduces an intelligent real-time prediction framework for system positioning, incorporating particle swarm optimization (PSO), long short-term memory (LSTM), a gated recurrent unit (GRU), and proportional–integral–derivative (PID) control. The process begins by analyzing real-time system data using Pearson correlation to identify hyperparameters with medium to strong correlations to the positioning parameters. These selected hyperparameters are then used as inputs for forecasting models. Independent LSTM and GRU models are subsequently developed to predict the system’s position, with PSO optimizing four key hyperparameters of these models. In the final stage, the PSO-optimized LSTM-GRU models are employed to perform real-time intelligent predictions of motion trajectories within the system. Simulation and experimental results show that the model achieves a prediction deviation of less than 3 mm, ensuring precise real-time predictions and providing reliable data for system operators. Compared to traditional PID and LSTM-GRU-PID controllers, the proposed controller demonstrated superior tracking accuracy while also reducing energy consumption, achieving energy savings of up to 10.89% and 2.82% in experimental tests, respectively.

Unleashing the power of green fiscal policies: Driving energy efficiency in China

This study examines the impact of such policies on energy efficiency by focusing on the “National Comprehensive Demonstration of Energy Saving and Emission Reduction Fiscal Policy” (ESER fiscal policy). Using a difference-in-difference method with multiple time points, the study finds that the ESER fiscal policy has significantly closed the total factor energy efficiency (TFEE) gap. The results are robust, supported by sensitivity analyses such as variable substitutions, dynamic effect tests, exclusion of potential intervening variables, placebo tests, and propensity score matching. Mechanism analysis shows that the pilot program closes the energy efficiency gap by increasing expenditure on environmental protection, improving agglomeration of productive services, and promoting of green technological innovation. Furthermore, the impact of green fiscal policies on TFEE is more pronounced in cities within two-control areas, non-old industrial base cities, large-scale cities, and resource-depleted cities. The analysis of the energy rebound effect found that green fiscal policies can effectively close the energy efficiency gap without generating energy rebound. These findings provide valuable insights and empirical evidence on the effectiveness of China's green fiscal policies in achieving green development, improving quality and efficiency.

A novel design optimization framework to sustain remanufacturability

The ever-increasing global carbon emissions have urged the need for environmentally conscious/sustainable product design, for which the design for remanufacturing (DfRem) is one potential approach. DfRem targets at designing products that have multiple life cycles, thus significantly reducing raw material usage, energy consumption, and carbon emissions. In this paper, we develop a three-stage framework that consists of (1) systematic design space exploration and a multi-objective optimization formulation to minimize the likelihood of failure causes (such as fatigue and wear) and environmental footprint, (2) topology optimization to further reduce material usage without significantly affecting the load-carrying capability of the product, and (3) post-topology optimization design verification to ensure the proposed design satisfies all design constraints. The environmental impact can be assessed at varying comprehensiveness levels (e.g., design and manufacturing phase, use phase) and in terms of carbon or GHG emission, energy use, and waste generation. Because the novel design framework predominantly adjusted the geometry, we focused on mass-based change and energy savings due to sustained remanufacturability. The multi-objective optimization formulation in the first step results in a Pareto optimal set of possible design solutions that the designer can use for the second step. We demonstrate the utility of this framework through a case study of an engine cylinder head subjected to thermo-mechanical loads, where we find that about 5% of the product mass can be conserved with only about a 3% increase in surface area that has a fatigue life less than 10,000 cycles.

Investigation of perovskite materials for solar cells using scanning tunneling microscopy.

The issue of energy scarcity has become more prominent due to the recent scientific and technological advancements. Consequently, there is an urgent need for research on sustainable and renewable resources. Solar energy, in particular, has emerged as a highly promising option because of its pollution-free and environment-friendly characteristics. Among the various solar energy technologies, perovskite solar cells have attracted much attention due to their lower cost and higher photoelectric conversion efficiency (PCE). However, the inherent instability of perovskite materials hinders the commercialization of such devices. The utilization of scanning tunneling microscopy/spectroscopy (STM/STS) can provide valuable insights into the fundamental properties of different perovskite materials at the atomic scale, which is crucial for addressing this challenge. In this review, we present the recent research progress of STM/STS analysis applied to various perovskites for solar cells, including halide perovskites, two-dimensional Ruddlesden-Popper perovskites, and oxide perovskites. This comprehensive overview aims to inspire new ideas and strategies for optimizing solar cells.