Achieve Energy Savings Research Articles

Innovative passive techniques and PCM integration in building envelopes for enhanced energy efficiency in desert regions

Rising energy demands in buildings for heating, cooling and operating appliances underscore the importance of implementing energy-efficient solutions. Passive cooling techniques provide a sustainable alternative to conventional, energy-intensive cooling systems, promising reduced energy consumption in buildings. Using a dynamic heat transfer model validated with ANSYS Fluent, this study examined three configurations: integrating phase change materials at the brick's centerline with cavities filled with air, EPS insulation, and emissivity coating. Additionally, the benefits of combining all three passive methods were evaluated. Results indicated that among the evaluated PCMs, capric acid demonstrated superior performance. While each passive method studied offers distinct advantages in enhancing thermal performance and achieving energy efficiency, assessing their combined effects revealed significant insights. Specifically, integrating capric acid at the brick's centerline, while simultaneously configuring EPS insulation in the upper cavities and emissivity coating in the lower cavities, yielded superior performance. This configuration significantly reduced inner surface temperature by approximately 6.6 °C and minimized heat flux by about 58.4 % compared to traditional bricks. Achieving energy savings of 56.24 %, this configuration shows potential to enhance both thermal comfort and energy efficiency in building applications.

Revolutionizing Textile Manufacturing: Sustainable and Profitable Production by Integrating Industry 4.0, Activity-Based Costing, and the Theory of Constraints

The textile industry, a cornerstone of daily life and a highly globalized sector, faces significant environmental challenges due to its high water and energy consumption and extensive chemical usage. This study proposes a comprehensive green production planning and control model integrating Industry 4.0 concepts, activity-based costing (ABC), and the theory of constraints (TOC). The model utilizes mathematical programming to optimize product mix, maximize profitability, and minimize environmental impact. It leverages real-time sensing technologies and ERP systems to facilitate waste recovery, reduce carbon emissions, and achieve energy savings. Various carbon emission cost models, including continuous and discontinuous tax functions, are explored to balance corporate profitability with environmental sustainability. The findings demonstrate the model’s potential in optimizing resource utilization, reducing the environmental footprint, and enhancing profitability.

Neuron-Inspired Flexible Phase Change Materials for Ambient Energy Harvesting and Respiration Monitoring.

The global energy crisis and climate change pose unprecedented challenges. Wearable devices with personal thermoregulation and energy harvesting hold great promise for achieving energy savings and human thermal comfort. Here, inspired by neurons, a novel phase change material (PCM) is reported for efficient energy harvesting and respiratory monitoring via a self-assembly strategy. The use of gum arabic (GA) enabled the encapsulation of polyethylene glycol (PEG) and the targeted distribution of carboxylated multi-walled carbon nanotubes (cMWCNTs) simultaneously in poly (ethylene vinyl acetate) (EVA) matrix. The material exhibits an outstanding toughness value of 14.88MJ m-3 and high elongation at a break of 565.67%, exhibiting remarkable flexibility. The material with sufficient melting enthalpy (71.11 J g-1) demonstrates high photothermal conversion efficiency (95.27%) under 808nm laser irradiation (105mW cm-2). In addition, due to the synergistic effect of GA and PEG, especially the formation of microdome structures on the surface, the material demonstrates ultrasensitive humidity responsiveness for respiratory monitoring with high precision, excellent repeatability, and fast response/recovery time (50.4/50.5ms). Notably, it shows great potential for moisture-electric generators (MEGs) with the function of non-contact sensing. This material opens the path toward next-generation wearable devices in energy conversion and health monitoring.

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.

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.

Hollow fibers with encapsulated ionic liquid for gas dehydration

Membrane technology is a viable alternative for achieving energy savings and sustainability, particularly in dehydration applications. The selective removal of water vapor from gas mixtures by membranes enhances the process efficiency furthermore the industry increasingly prioritizes resource conservation and environmental impact, advancements in membrane technology are crucial for meeting these goals and driving progress toward a sustainable future. Our approach consists of the incorporation of a proline amino acid-based ionic liquid encapsulated in carbon capsules, dispersed in a Pebax®1657 matrix, and coated on polyetherimide hollow fibers. The filled capsules have high sorption capacity for both water vapor and carbon dioxide (CO2) contributing for successful exploration for air dehumidification, as well as flue gas, natural gas and biogas dehydration. The membranes had exceptional water vapor permeance, reaching values up to approximately 10,000 GPU while maintaining an H2O/N2 selectivity of around 124,000. CO2/N2 selectivity as high as 100 and promising results for CH4 purification were demonstrated. Long-term studies using alternative modules to prevent saturation of the encapsulated capsules with water vapor and effective regeneration have been proven.

U-Net architecture for high-resolution thermal image segmentation: Enhancing CFD models in smart HVAC design

This paper presents an enhanced U-Net architecture for accurately segmenting high-resolution thermal images to facilitate the design of intelligent Heating, Ventilation, and Air Conditioning (HVAC) systems using Computational Fluid Dynamics (CFD) models. Indoor thermal comfort for occupants will improve under the same energy load by imposing comfort demands. Segmented data is fed into CFD simulations using a U-Net variant for thermal imagery. Integrating the segmented thermal data into the CFD simulations increases the model’s accuracy by 15 % for segmentation and 20% for prediction when compared to a traditional U-Net architecture. Experimental findings demonstrate the potential to achieve energy savings of up to 25 % in the simulated scenario by using the trained and validated model on a dataset of 1,500 high-resolution thermal images of various building styles. Compared to existing methods, the proposed method saves 30% in total simulation time for complex building models. Deep learning enables building the next generation of HVAC systems by pushing the boundaries of energy-efficient and environmentally friendly buildings. This study shows that advanced computer vision can be leveraged to create more innovative and sustainable built environments, leading to drastic improvements in energy efficiency.

Enhanced temperature regulation in compound heating systems: Leveraging guided policy search and model predictive control

With the widespread application of solar-energy-air-source heat pump composite heating, optimizing the regulation of air temperature in heating zones, enhancing system thermal comfort, and reducing energy consumption have become crucial. To ensure residential comfort while minimizing HVAC system energy costs, a control method combining guided policy search (GPS) with model predictive control (MPC) is proposed for composite heating systems. This method replaces state estimation in MPC with policy search and substitutes the offline trajectory optimization used in guided policy search with MPC, thus integrating the strengths of both approaches. This strategy not only improves control precision but also reduces energy consumption and enhances system robustness. The GPS-MPC control algorithm was validated against reinforcement learning and MPC. A simulation model was developed and validated on a real physical platform. Simulations compared the control effects of on-off control and MPC on pump frequency, flow rate, stratified thermal storage tank temperature, component, and system energy consumption. The data results indicate that the GPS-MPC algorithm offers superior predictive accuracy, efficiency, and robustness in composite heating control systems compared to conventional methods. Under the GPS-MPC strategy, indoor temperature fluctuations, pump frequency response speed, and energy consumption were significantly improved, with temperature fluctuation limited to only 0.8 °C, and the system achieving energy savings of over 12 %.

CNN-LSTM is all you Need for Efficient Resource Allocation in Cloud Computing

Many organizations have embraced cloud computing in recent years to provide new services, easily expand their IT resources, and reduce the cost of their IT infrastructure. This has been made possible through the implementation of resource allocation strategies by cloud service providers. One of the major challenges during resource allocation is to minimize power consumption while ensuring the required Service Level Agreement (SLA). To solve this problem, a new approach to efficiently allocate resources in cloud computing while optimizing energy consumption and guaranteeing the required service level agreement has been proposed. The main idea of this proposal is to leverage the CNN-LSTM architecture to accurately predict resource utilization in order to make the appropriate resource allocation decision. The proposed solution was validated in two steps: step 1) a comprehensive set of statistical performance analysis and step 2) an intensive simulation of the solution for resource allocation using cloudSim Plus tool. The results of the experimentation demonstrated that the proposed solution can help cloud service providers achieve energy savings while guaranteeing the required SLA.

Research on Adaptive Control Systems for Building Shading Based on Decision Tree and Random Forest Classification Algorithms

Dynamic shading systems for buildings, due to their variable mechanisms and high adaptability, have the potential to reduce energy consumption in buildings significantly. However, the performance of dynamic shading is largely influenced by the control system employed. This study aims to explore an adaptive control method for building shading based on decision tree and random forest classification algorithms (machine learning), with the objective of minimizing energy demands related to lighting and cooling as much as possible. The adaptive control system model may independently modify the location of building shading in response to input environmental conditions, thereby achieving energy savings for both lighting and cooling. According to the findings, the automated control system model may reduce building energy consumption by 38% overall, which is close to the ideal level of energy conservation.

Social equity provisions in energy efficiency obligations: An ex-post analysis of the French program

We evaluate the distributional effects of the French energy efficiency obligations, focusing on a reform implemented from 2016 to 2018. This reform introduced two social equity measures: a sub-obligation requiring energy suppliers to achieve energy savings in residences occupied by fuel-poor households, and a bonus system granting additional certificates to suppliers supporting investments in such dwellings. Our analysis, based on aggregated data from 2019, reveals that the program initially succeeded in channeling financial benefits towards low-income households. However, the primary driver of this positive outcome – generous bonuses – went to pose a risk of reversing the intended effect. We provide a detailed explanation of this shift and offer policy recommendations for improving social equity provisions. Our findings indicate that there may be no discernible advantage in combining both measures to address social equity concerns, advocating instead for a program based solely on the sub-obligation.

The direct energy rebound effects for manufacturing and service sectors in China: Evidence from firm-level estimations

China has implemented a variety of energy efficiency policies to achieve energy saving and carbon abatement targets. While the national carbon market has been launched and China has opted to give market-oriented policies a greater role in achieving net zero, energy efficiency policies still constitute a significant portion of the policy mix. However, the substantial drawback of energy efficiency policies compared to carbon trading, the rebound effect, has made energy efficiency policies debatable. It is crucial to precisely understand the magnitude of the rebound effect to design and implement effective energy policies. Our study estimates and compares the direct energy rebound and output expansion effects in China's manufacturing and service sectors using firm-level data from 2013 to 2016 and employing panel regression analysis. We also analyze the impact of firm characteristics such as ownership status, geographical location, and industry type on enterprises' rebound effects. We found a 33.5% direct rebound effect and a 19.3% output expansion effect in the manufacturing sector. In the service sector, the direct energy rebound effect was 57.9%, a larger one than the manufacturing sector. We also estimate direct energy rebound effects for key manufacturing and service enterprises. Our findings reveal that key manufacturing enterprises exhibit a larger rebound effect compared to the overall manufacturing sector, while key service enterprises show a smaller rebound effect than the overall service sector. Public, foreign, and heavy service enterprises show a smaller energy rebound effect, while northeastern manufacturing and service enterprises exhibit a larger rebound effect. We suggest that China's carbon market should also cover key service enterprises. For those below the threshold, enhancing energy management is essential to mitigate the rebound effect. Aligning with carbon emission cap targets, rather than relying solely on energy efficiency policies, could more effectively achieve energy savings and carbon abatement.

Effect of promoters on the performance of CuNi catalysts for hydrogen production from methanol decomposition

Abstract Utilizing the exhaust heat from engines to decompose methanol for hydrogen production, and subsequently introducing this hydrogen into the combustion chamber, is one of the crucial approaches for achieving energy savings and emission reductions. The role of an efficient and stable catalyst for methanol decomposition is paramount in this application. Therefore, CuNi-based catalysts modified with promoters such as Zr, La, Mn, and Mg were prepared using a stepwise impregnation method. The developed catalysts were tested using various analytical methods and characterization techniques. The results indicate that the addition of the Zr enhances the dispersion of active components, improves the catalyst’s reducibility. This, in turn, enhances the catalyst’s activity and hydrogen selectivity. The hydrogen yield of the Zr modified catalyst increased by an average of 12% compared to the original catalyst. Furthermore, the Zr modified catalyst exhibits exceptional stability after prolonged use. La can enhance the low-temperature activity of the catalyst but performs poorly at high temperatures. The promoter Mn has a minimal impact on the overall performance of the catalyst. Conversely, the addition of Mg as a promoter inhibits the dispersion of active components, resulting in adverse effects on the catalyst.

Research on energy consumption optimization of predictive cruise control considering the state of the leading vehicle

To comprehensively assess the economic performance of electric vehicles (EVs) cruise planning and mitigate conflicts with leading vehicles, a dynamic programming-based predictive cruise speed optimization control system was developed. This system comprises two layers: an upper V2X-based LSTM-DP cruise speed planning system and a lower adaptive cruise control system based on sliding mode control (SMC), and the mode switching strategy of the speed and distance tracking controller is designed. Initially, a segmented uniform variable motion algorithm processed the historical driving data of the leading vehicle, significantly enhancing the LSTM neural network's acceleration prediction capability, particularly on inclines, with an improvement in accuracy of 37.8 %. Subsequently, an adaptive cruise tracking controller was implemented to track the planned vehicle speed. Simulation tests on actual roads demonstrated that the LSTM-DP approach not only ensures driving safety but also markedly improves fuel efficiency over conventional constant speed cruising methods when planning is effective. Moreover, even in scenarios where planning fails, the LSTM-DP strategy can still achieve energy savings of up to 37 % without compromising the safety distance between vehicles. Finally, hardware-in-the-loop (HIL) testing confirmed the system's efficacy under real-time operational conditions.

Analysis of proper ink management impact on overall environmental equipment efficiency for sustainability

Printing as a process itself generates many environmental concerns. The paper addresses ink management in terms of environmental issues in the label printing industry, focusing on its environmental implications. The goal is to demonstrate how a proper ink management system impacts overall printing process efficiency and environmental sustainability for printing companies. The paper introduces an empirical approach to managing components for label and packaging production, utilizing automatic ink dispensing systems. The results demonstrate that the proper management of ink dispensing to minimize waste in packaging printing is crucial for optimizing operating print costs, potentially reducing the amount of ink needed to prepare colors by 52% and achieving energy savings of 37%. This approach fulfills the goal of sustainability by addressing environmental, economic, and social concerns. By optimizing ink usage and energy consumption, companies can significantly reduce operating costs and enhance economic performance. Simultaneously, these practices improve product quality, meet consumer demands for sustainable packaging, and create better working conditions for employees. Future directions and practical implications for supporting operational excellence in production are also discussed.

Design of a dynamically switchable metamaterial solar heating and daytime radiative cooling device based on reversible metal electrodeposition

Abstract The combination of daytime radiative cooling and solar heating can regulate the temperature of buildings in all seasons, which is vital to achieving energy savings. However, achieving a dynamic and efficient switch between radiative cooling and solar heating states is still challenging. The reversible metal electrodeposition is a promising electrochromic (EC) technology that can dynamically manipulate the visible and infrared spectrum by depositing a nanoscale metal layer. By combining it with a delicate nanostructure design, reconfigurable daytime radiative cooling and solar heating states can be achieved. In this work, a metamaterial EC device capable of efficiently switching between high solar reflectance (91.73%) and high solar absorptance (95.41%) via reversible silver layer electrodeposition has been developed. This device consists of a top layer, which is a visible transparent electrode of indium tin oxide covered with an ultrathin platinum film, an electrolyte in the middle, and a metamaterial absorber based on a metal/dielectric multilayered structure at the bottom. Afterward, we analyze the spectrum of each part of the device and carry out a parametric study on the practical performance with different ambient temperatures and convective heat transfer coefficients. The average solar reflectance of the device in the radiative cooling state is 93.41% within the range of 0.3–2.5 μm. In the solar heating mode, the average solar absorptance of the device reaches 95.46%. Numerical simulations show that the device in the cooling mode achieves a temperature drop of 5 °C–9.8 °C and an average cooling power of 140.8 W m − 2 . The device in heating mode achieves a maximum temperature rise of 45 °C and a maximum heating power of 700 W m − 2 . This work provides a feasible design for solar heating and daytime radiative cooling switching devices, which is promising in applications like energy-saving buildings, wearable devices, electric vehicles and other outdoors facilities.

A comparative study of energy performance of Green, White, and gravel roofs in a temperate climate through In-Situ measurement and dynamic simulation

Roofs have been recognized as a potential area for intervention to achieve energy savings in buildings and enhance indoor comfort. In this context, green and cool roofs emerged as promising and attractive solutions for sustainable construction, offering various advantages, particularly in terms of the building’s energy efficiency. However, their energy performance is closely tied to the local climatic conditions. This study presents a comparative analysis of the thermal and energy performance of four types of roofs − a green roof with sedum vegetation, a green roof with grass vegetation, a white painted roof, and a gravel-covered roof − under the temperate climate conditions of Poitiers, France. This comparative investigation helps to recommend optimal construction solutions tailored for temperate climates. Initially, in-situ measurements are conducted on two residential buildings equipped with these roof types. The experimental campaign involves monitoring weather conditions, roof surface temperatures, heat flux, and moisture content within the substrate layers of the green roofs. Subsequently, the TRNSYS software is employed to evaluate the heating, cooling, and total energy consumption of the investigated roof scenarios. The experiment results demonstrate that, in comparison to non-vegetated roofs, green roofs could maintain moderated outer surface temperatures. In summer, they effectively limit the entry of heat waves into the building, reducing heat flow by 52% for sedum and 71% for grass compared to the gravel roof. Furthermore, simulation results revealed that the grass green roof is the most effective solution for passive cooling, achieving energy reductions of 81 to 93% compared to other investigated roof types. Meanwhile, sedum vegetation emerges as the optimal choice for reducing total energy consumption, with reductions of up to 12% compared to the other roof type.

A novel intermediate heat exchange intensified extractive pressure-swing distillation process for efficiently separating n-hexane-tetrahydrofuran-ethanol

The new intensified strategy can be used in special distillation processes to achieve energy savings. Through analyzing the influence of different extractants and pressure on the separation performance of n-hexane-tetrahydrofuran-ethanol. system, extractive pressure-swing distillation process (EPSD) using dimethyl sulfoxide as extractant was proposed. Multi-objective optimization was performed to definite the best operating parameters of EPSD. Based on the phenomenon that the temperature of high-temperature column top is lower than low-temperature column bottom, making it impossible to perform traditional thermal integration, an intermediate heat exchange intensified process (IHE-EPSD) was developed. The performance influencing factors and heat exchange network of IHE-EPSD were analyzed to obtained the optimal IHE-EPSD process. Three heat integration schemes of the IHE-EPSD were designed to further save energy consumption. Finally, the processes were assessed from economy, energy, environment and exergy. The results indicate that the IHE-EPSD process exhibits excellent separation performance, achieving the efficient separation of n-hexane-tetrahydrofuran-ethanol.

Energy savings by adapting consumer behavior in grid-connected photovoltaic systems with battery storage

Today, the world faces many energy challenges that make the use of clean energy an obligation and an emergency. These challenges require changes in a wide range of sectors, including transport, industry and residential areas. At present, energy production is responsible for a large amount of greenhouse gas emissions, the main cause of climate change with its various dangerous effects on human life. Therefore, a change towards non-carbon energy is becoming a necessity. Certainly, this evolution has been underway for years and the renewable energy market has developed in a surprisingly efficient way. However, the current situation has reached an alarming state and requires the active participation of all stakeholders, especially consumers. Therefore, the main objective of this paper is to illustrate how consumer behavior can significantly influence and contribute to the optimization of renewable energy systems, especially photovoltaic systems. The paper emphasizes the beneficial integration of batteries and storage systems to achieve energy savings. The results show that with some adjustments in daily behavior, an overall energy saving of 62% can be achieved compared to the normal consumption scenario: the energy obtained from the grid and then the electricity bill is reduced.

Integrating insulated concrete form (ICF) with solar-driven reverse osmosis desalination for building integrated energy storage in cold climates

This research addresses the pressing global challenges of clean energy and water supplies, emphasizing the need for sustainable solutions for the building sector. The study integrates Reverse Osmosis (RO) systems with building energy systems, incorporating Solar Thermal Collectors (STC)/Photovoltaic Thermal (PVT), water-to-water heat pumps, and an Insulated Concrete Form (ICF) based building foundation wall thermal energy storage. It explores the effectiveness of four configurations—STC-ICF, STC-ICF-RO, PVT-ICF, and PVT-ICF-RO—using sensitivity analysis on collector area (25 % and 50 % increase) and weather data from four Canadian cities: Winnipeg, Toronto, Halifax, and Vancouver and two non-Canadian cities: Helsinki, Finland and Bergen, Norway. Key outcomes highlight the benefits of integrated RO scenarios, such as reduced ICF wall temperature, diminished unwanted heat in the cooling season, reduced RO pump consumption, and enhanced solar energy production. The STC-ICF-RO and PVT-ICF-RO systems achieved energy savings of 653 kWh and 131 kWh, respectively, compared to their non-integrated RO counterparts. Both systems also contributed to lowering CO2 production levels. The STC-ICF-RO system's payback period is 2 years, affirming its economic viability. Compared to the base system without ICF and RO integration, the STC-ICF-RO and PVT-ICF-RO configurations reduced energy consumption by 20 % and 32 %, respectively. The sensitivity analysis suggests potential system improvements under specific conditions, primarily when implemented in communities of buildings.