Effective Energy Conservation Research Articles

Analysis of home thermal energy conservation and green environment design based on intelligent devices and optical image monitoring

Global climate change and energy crisis are becoming increasingly serious, and household energy consumption occupies a certain proportion in the overall energy consumption. The purpose of this study is to analyze thermal energy use in home environment through intelligent devices and optical image monitoring technology, so as to achieve effective energy conservation and environmental optimization design. In this paper, intelligent sensors and optical image monitoring equipment are used to monitor and collect the thermal energy consumption of different households in real time. At the same time, data analysis technology is used to deeply analyze the heat flow, heat loss and equipment use efficiency in the home. The study compares the effect of traditional energy-saving methods and intelligent monitoring technology. After implementing targeted optimization design through intelligent monitoring equipment, the overall thermal energy saving rate is higher, and the comfort level and green index of the home environment are significantly improved. The results show that intelligent equipment and optical image monitoring technology show a good application prospect in home thermal energy saving. Through accurate monitoring and data analysis, heat energy waste can be effectively identified, so as to take corresponding measures to achieve energy conservation and emission reduction, and promote the landing of green environmental design.

Energy conservation strategies on academic buildings using interpretive structural modeling to develop energy sustainability

Effective energy conservation strategies are required to be implemented in academic buildings as they consume significant energy while considering the convenience and function of the buildings. However, the influencing factors are interrelated and complex, requiring an appropriate approach to unravel the complexity. Therefore, this study aims to identify, analyse, and map the interaction relationship between factors affecting energy conservation in academic buildings. This study used Interpretive Structural Modeling (ISM) procedure starting from developing the Structural Self Interaction Matrix (SSIM), converting SSIM into a Reachability Matrix, revising the matrix, and categorizing the factors by using MICMAC analysis. This study involved 9 factors, including architectural design, illumination technology, education and awareness, energy monitoring and management, renewable energy use, efficient HVAC system, energy-saving equipment, institutional policies, and campus community participation. The study found that renewable energy use at level 3 was the factor that was not influenced by and did not interact with any other factors. Meanwhile, the illumination technology was a factor that interacted with the efficient HVAC System factor which was at level 1 where these two factors were influenced by other seven factors. This study aligns with current developments in energy conservation, including an increased focus on renewable energy and energy efficiency in academic buildings, supported by global and national policies aimed at achieving sustainability goals. It provides a comprehensive understanding on developing sustainable energy conservation strategies in academic buildings.

(Invited) In Situ x-Ray Spectroscopy for Energy Materials

In response to the worldwide surge in energy demand, the materials scientists devote themselves to the search for clean and sustainable energy. Without the development of cutting-edge renewable energy materials, going green has never been simple. The zero-emission future requires a multifaceted approach. Advanced functional materials for more effective energy conversion, storage, and conservation are the universal focus of energy research. Although the ideas for increasing the efficiency of current energy materials in terms of energy conversion, storage, or conservation are clear and straightforward, they are technically challenging. It is difficult to better engineer materials in an efficient manner for a practical use without knowing the fundamental atomic and electronic structures, particularly how they respond under operating conditions. Synchrotron x-ray spectroscopies, including x-ray absorption and emission spectroscopies are powerful tools to study the local unoccupied and occupied electronic states. In addition, employing the in situ technique permits us to monitor the atomic and electronic structure modulations of the energy material at work. An emerging x-ray spectro-microscopic technique, scanning transmission x-ray microscopy, which provides spatially resolved x-ray spectroscopy. The significance of utilizing x-ray spectroscopy for the atomic and electronic structure characterization of several significant energy material systems, such as advanced nanocatalysts, artificial photosynthesis materials, and smart materials, will be discussed in this presentation. Recent developments of in situ technique, a number of recent studies, and energy science-related Tamkang University (TKU) end-stations at the BL45A and BL27A of TPS will also be presented.

Rapid Fabrication of Tungsten Oxide-Based Electrochromic Devices through Femtosecond Laser Processing

The sol-gel method is a widely adopted technique for the preparation of tungsten trioxide (WO3) materials, favored for its cost-effectiveness and straightforward production procedures. However, this method encounters challenges such as prolonged annealing periods and limited flexibility in fabricating patterned WO3 films. This study introduces a novel approach that integrates femtosecond laser processing with the sol-gel method to enhance the fabrication of WO3 films. By adjusting polyvinylpyrrolidone (PVP) concentrations during sol-gel synthesis, precise control over film thickness and optimized film properties were achieved. The innovative technique significantly reduced the annealing time required to achieve an 80% transmittance rate from 90 min to 40 min, marking a 56% decrease. Laser processing increased the surface roughness of the films from Sa = 0.032 to Sa = 0.119, facilitating enhanced volatilization of organics during heat treatment. Additionally, this method improved the transmittance modulation of the films by 22% at 550 nm compared to unprocessed counterparts. This approach not only simplifies the manufacturing process but also enhances the optical efficiency of electrochromic devices, potentially leading to broader applications and more effective energy conservation strategies.

Closing the Efficiency Gap: Insights into curbing the direct rebound effect of residential electricity consumption in Saudi Arabia

The rebound effect refers to the phenomenon where energy efficiency improvements lead to increased energy consumption, offsetting some of the expected energy savings. This article aims to estimate the direct rebound effect of residential electricity consumption in Saudi Arabia and explores potential solutions to mitigate its impact. Understanding the magnitude of the rebound effect is crucial for policymakers to develop effective energy conservation strategies. This study employs a time-series modeling approach, using a dataset covering 31 years, 1990–2021, and four regions within Saudi Arabia, incorporating various socioeconomic and energy-related variables. The magnitude of the rebound effect in residential electricity consumption in the Kingdom, which varies across regions, ranges from 41% to 71%. Since the inception of the first wave of the energy price reform in 2016, the Kingdom has witnessed a power consumption rebound in the residential sector totaling 39.02 TWh (6.5 Twh per annum), and a total of 28.13 Mt. in CO2 equivalent emissions (4.69 Mt. in CO2 equivalent per annum) during the period of 2016–2021. These findings add to the existing literature on the rebound effect, further enriching our understanding of this complex phenomenon and its implications. They also provide valuable insights for policymakers and energy planners in Saudi Arabia, helping them develop effective strategies to minimize the rebound effect and promote energy conservation.

Residential building energy consumption estimation: A novel ensemble and hybrid machine learning approach

In recent decades, there has been a substantial rise in both worldwide energy consumption and the accompanying increase in Carbon Dioxide (CO2) emissions, primarily propelled by population growth and the escalating demand for personal comfort. Operational energy consumption in buildings constitutes about 30% of the world's total final energy usage, underscoring the significance of predicting building energy usage for effective energy planning, management, and conservation. This study has enhanced the prediction of heating load (HL) and cooling loads (CL) for residential buildings using novel and dependable machine learning (ML) techniques. The study utilized two base models: Adaptive Boosting (ADA) and Extreme Gradient Boosting (XGBoost). An ensemble consisting of ADA and XGB was constructed to improve the model's performance, aligning with the principles of the Dempster-Shafer theory. To optimize the efficiency of ADA and XGB, five innovative optimizers, namely Victoria Amazonica Optimization (VAO), Giant Trevally Optimizer (GTO), Covariance Matrix Adaptation Evolution Strategy (CMAES), Coyote Optimization Algorithm (COA), and Mountain Gazelle Optimizer (MGO), were integrated. Statistical analysis has been employed to evaluate the performance of the proposed models. The results highlight the effectiveness of CMAES in optimizing the XGB and ADA models for predicting HL and CL. The most accurate result was achieved by the XGCM hybrid model, as evidenced by the impressive total R2 value of 0.9934 in HL and 0.9911 in CL prediction. The experimental findings illustrate that the suggested approach exhibits superior predictive performance across various scenarios of building energy consumption.

A Review of Metaheuristic Optimization Techniques for Effective Energy Conservation in Buildings

The built environment is a significant contributor to global energy consumption and greenhouse gas emissions. Advancements in the adoption of environmentally friendly building technology have become crucial in promoting sustainable development. These advancements play a crucial role in conserving energy. The aim is to achieve an optimal design by balancing various interrelated factors. The emergence of innovative techniques to address energy conservation have been witnessed in the built environment. This review examines existing research articles that explore different metaheuristic optimization techniques (MOTs) for energy conservation in buildings. The focus is on evaluating the simplicity and stochastic nature of these optimization techniques. The findings of the review present theoretical and mathematical models for each algorithm and assess their effectiveness in problem solving. A systematic analysis of selected algorithms using MOT is conducted, considering factors that influence wellbeing, occupant health, and indoor environmental quality. The study examines the variations among swarm intelligence MOTs based on complexity, advantages, and disadvantages. The algorithms’ performances are based on the concept of uncertainty in consistently providing optimal solutions. The paper highlights the application of each technique in achieving energy conservation in buildings.

Electricity Load Demand Prediction for Microgrid Energy Management System Using Hybrid Adaptive Barnacle‐Mating Optimizer with Artificial Neural Network Algorithm

In recent years, one of the primary causes of high electricity consumption is the growing global population and the availability of power‐demanding smart devices. As a result, accurate load forecasting tools are required for effective energy conservation in microgrids. Various simulation tools and artificial intelligence (AI)‐based methods have been used to make the best forecasts of electricity demand. Furthermore, conventional systems are static and based solely on historical data. To address this practical need, this work aims to create a machine learning (ML) model for short‐term load forecasting based on feature selection and parameter optimization. This research proposes a hybrid short‐term load demand prediction approach that combines an adaptive barnacle‐mating optimizer (ABMO) and an artificial neural network (ANN). When compared to the regression tree (RT), support vector machine (SVM), ANN, and particle swarm optimization‐based ANN (PSO‐ANN) algorithms, the proposed ABMO‐ANN algorithm reduces mean absolute percentage errors by 67.69%, 64.58%, 59.18%, and 42.02%, respectively. Compared to conventional RT, SVM, ANN, and PSO‐ANN algorithm, the hybrid ABMO with ANN outperforms them based on mean absolute percentage error, root mean square error, correlation coefficient , symmetric mean absolute percentage error, and agreement index (d).

Decomposition of the site-level energy consumption and carbon dioxide emissions of the iron and steel industry.

Identifying the key factors influencing energy consumption and CO2 emissions is necessary for developing effective energy conservation and emission mitigation policies. Previous studies have focused mainly on decomposing changes in energy consumption and CO2 emissions at the national, regional, or sectoral levels, while the perspective of site-level decomposition has been neglected. To narrow this gap in research, a site-level decomposition of energy- and carbon-intensive iron and steel sites is discussed. In this work, the logarithmic mean Divisia index (LMDI) method is used to decompose the changes in the energy consumption and CO2 emissions of iron and steel sites. The results show that the production scale significantly contributes to the increase in both energy consumption and CO2 emissions, with cumulative contributions of 229.63 and 255.36%, respectively. Energy recovery and credit emissions are two key factors decreasing site-level energy consumption and CO2 emissions, with cumulative contributions to the changes in energy consumption and CO2 emissions of -158.30 and -160.45%, respectively. A decrease in energy, flux, and carbon-containing material consumption per ton of steel promotes direct emission reduction, and purchased electricity savings greatly contribute to indirect emission reduction. In addition, site products and byproducts promote an increase in credit emissions and ultimately inhibit an increase in the total CO2 emissions of iron and steel sites.

Performance analysis of paraffin microcapsules and phase change concrete based on microporous cenospheres

Combining concrete and phase change materials has created a novel method for building energy conservation. However, introducing phase change materials into the concrete will cause various impacts on its performance. This study explores a novel approach to enhance thermal energy storage in building materials by incorporating phase change materials (PCMs) into concrete. Microporous cenospheres were extracted from fly ash through a three-step process and used to produce composite PCM microcapsules containing paraffin as the core material. The produced PCM microcapsules were characterized by scanning electron microscopy (SEM), differential scanning calorimetry (DSC), thermogravimetric analysis (TGA), and phase change cycle analysis. Phase change concrete was then formulated by substituting 0–25 % sand with these microcapsules. Effects of the addition of the PCM microcapsule on both the mechanical and thermal properties of concrete were experimentally examined. While the incorporation of microcapsules significantly improved concrete's thermal properties, it also led to a reduction in compressive strength (up to 19.65 % at 28 days), though still meeting specifications. The introduction of microcapsules notably enhanced the temperature control capabilities of concrete. In spring, the model room made with the phase change concrete exhibited a 2.2 °C lower indoor peak temperature 2 h 40 min later than the one room with the ordinary concrete. During active heating in winter, the phase change concrete demonstrated significantly lower indoor temperature fluctuations, with a maximum reduction of 45.39 %. Furthermore, the study found that the particle size of microcapsules had varying effects on concrete performance, with larger particles improving thermal properties and smaller particles enhancing mechanical properties. Overall, this research highlights the potential of composite phase change microcapsules and phase change concrete based on microporous cenospheres for effective temperature control and energy conservation in building applications.

Cluster analysis-based energy performance assessment for office building stock

To achieve carbon neutrality at the national or city level, the energy performance and conservation measures of large buildings should be evaluated. However, the assessment of the energy performance of existing building stock is often based on annual energy use intensity calculated from energy bill data due to data acquisition limitations. This approach has limitations in analyzing seasonal effects and establishing effective energy conservation strategies. In this paper, we propose a novel energy performance assessment method for existing office building stock. Our method classifies monthly electricity, gas, and heat energy use patterns using clustering algorithms without requiring additional database construction beyond the National Building Energy Database in Korea. We discuss the clustering results and provide an application method to assist policymakers through a case study.

Lightweight extensive green roof for building renovation: Summer performance analysis and application in a living laboratory

Extensive green roofs are considered an effective energy conservation measure for increasing buildings’ energy efficiency and reducing the heat wave effect in dense build environments. In this context the present work has a two-fold objective: the first is to test and analyse a commercial extensive lightweight green roof sample through an experimental monitoring campaign carried out in a hot climate during the summer time; the second is to provide a practical case study application showing the architectural integration of the extensive green roof technology for existing buildings. The experimental monitoring campaign has been set for analyzing the temperature levels of an extensive green roof compared with a traditional horizontal roof finished with cement tile. The temperature levels have been analysed through a set of sensors positioned at different levels to characterise the green roof response to the climatic forces during summer. The results show that the air temperature in proximity to the green surface (15 cm above the greenery) is warmer than the undisturbed ambient air temperature during the day and lower during the night by 2–2.5 °C. The soil substrate and the vegetative layer contribute to increase ambient air humidity levels. As expected, the evapotranspiration of the green layer increases during a typical sunny day resulting in more water content in the air above the vegetative level of about 4–8 %. The surface temperature of the ground below the vegetation layer and the temperature of the ground layer (8 cm deep) shows beneficial attenuation and time shift properties with respectively 12–15 °C and 3–4 h. Compared to the traditional cement tiles the green roof shows lower intralayer temperature with differences ranging from 6 to 8 °C. Moreover, the renovation case study represents a practical example of green roof technology integration in a real environment. The study has high replicability, and it is meant to be an interesting example for researchers and professionals to boost the green roof technology application for higher-quality built environments.

IOT SYSTEM FOR CAR PARKING, STREET LIGHTS, AND AUTOMATIC IRRIGATION WITH REAL-TIME UPDATES ON MOBILE APP

In today's energy-conscious world, our foremost duty is to efficiently conserve energy. With advancing technology, automation systems have become the preferred choice over traditional manual methods. We've developed a user-friendly IoT application encompassing smart street lights, a smart parking system, and an automatic irrigation system to ensure effective energy conservation and water reduction. Our IoT-based smart street lights utilize external light sensing to automatically adjust illumination levels, offering 50% and 100% intensity as needed. This minimizes electricity waste and reduces the need for manual control, thus conserving energy. The saved energy can be redirected for residential, commercial, or other essential purposes, contributing to sustainable energy practices. The smart parking system offers convenience to users by allowing them to view available parking slots through the application. Users can make secure online payments, streamlining the parking experience. Additionally, the system aids in locating parked vehicles, enhancing efficiency. Automatic irrigation system employs a distributed wireless network of soil moisture sensors. A gateway unit manages sensor data, triggers, and actuators, transmitting real-time information to a web application. This innovation optimizes water usage in agriculture and landscaping, reducing water wastage and labor. A realtime data, conserving water and time spent on irrigation. This system's ability to adjust water distribution based on soil moisture content ensures efficient water management. Users receive soil moisture information on their mobile devices, promoting responsible water use. By automating processes, reducing waste, and enhancing user experience, we contribute to a more sustainable and energy-efficient future.

Retrofit Analysis of City-Scale Residential Buildings in the Hot Summer and Cold Winter Climate Zone

The rising energy consumption in residential buildings within the hot summer and cold winter (HSCW) climate zone, driven by occupants’ pursuit of improved thermal comfort, necessitates effective energy conservation measures. This study established urban building energy models for 32,145 residential buildings in Changsha City, China, and conducted a comprehensive retrofit analysis of seven energy conservation measures (ECMs). Additionally, the study assessed the impact of residents’ conscious energy-saving behaviors concerning air conditioner (AC) control. The research commenced by creating six baseline models representative of the diverse building stock. Identifying seven commonly used ECMs, the study examined the potential of each measure for enhancing energy efficiency. To facilitate the analysis, a dedicated toolkit, AutoBPS-Retrofit, was developed to efficiently modify the baseline model for each ECM. Furthermore, the investigation delved into the investment cost of implementing the ECMs and evaluated their simple payback year (PBP) and net present value (NPV). The results demonstrate that tailored retrofit plans are essential when addressing envelope improvements, varying according to building types and ages. Retrofits targeting lighting systems offer both promising energy savings and favorable economic viability, albeit subject to residents’ preferences. Alternatively, upgrading the AC systems emerges as the most energy-efficient approach, yet the economic assessment raises concerns. The study’s findings offer practical insights for governments seeking to establish effective carbon reduction goals and policies. Moreover, the research can assist energy-saving institutions, real-estate companies, and stakeholders involved in renovation projects by offering guidance in making informed decisions to enhance energy efficiency in city-scale residential buildings.

Retransmission Reduction using Checkpoint based Sub-Path Routing for Wireless IoT

Wireless IoT has been one of the major breakthroughs of the current decade. It has improved the quality of life and has also aided in several improvements in domains like healthcare. Effective routing and energy conservation has been the major challenges in creating and maintaining a successful IoT network. This work presents a checkpoint based routing model, CSPR, to improve the transmission efficiency by reducing retransmission. This work selects checkpoints in the network prior to transmission. The checkpoints are used to build the final path. This process ensures that the routes created are dynamic and reactive, leading to improved security and increased path reliability. Comparison with existing routing model shows improved network lifetime and reduced selection overhead levels, exhibiting the high efficiency of CSPR.

Thermal Energy Storage Technology in solar Energy Utilization: A Review

Thermal energy storage (TES) is a promising technique that conserves accumulated thermal energy from heat and cold mediums, making it available for future use. This method allows energy to be stored under various conditions, presenting an attractive solution for harnessing solar radiation efficiently and in large quantities. TES is becoming increasingly important as renewable electricity integration grows and the demand for low-carbon energy rises. Concentrating solar power plants benefit from TES, enabling them to store excess solar energy during peak times and utilize it during periods of lower solar radiation, ensuring a continuous power supply. Additionally, standalone TES systems for grid applications are gaining popularity, especially with the declining costs of renewable energy. These systems facilitate energy integration and help meet the increasing energy demands sustainably. Phase change materials (PCMs) play a vital role in thermal energy storage systems, contributing to effective energy conservation. Their high thermal storage density and moderate temperature volatility make them ideal for storing and releasing significant amounts of thermal energy. As a result, PCMs have gained popularity in this field. This study examines various aspects of thermal energy storage systems, with a particular focus on research articles related to storage materials and methods. It explores sensible heat storage, which involves altering material temperatures to store energy, latent heat storage that capitalizes on phase change properties like those of PCMs, chemical storage utilizing chemical reactions for energy storage, and cascaded thermal storage systems that combine different methods for optimized energy storage. By exploring these areas, this research aims to advance the understanding of thermal energy storage and contribute to the ongoing efforts in achieving sustainable and low-carbon energy solutions for the future.

Design and optimization of a zero carbon emission system integrated with the utilization of marine engine waste heat and LNG cold energy for LNG-powered ships

Aiming at promoting LNG-powered ships to achieve zero carbon emission, this paper presents an efficient energy utilization system, which can fully recover the marine engine waste heat and LNG cold energy while realizing zero carbon emission. A combination of transcritical CO2 and organic Rankine cycles is adopted to replace the original ship's waste heat generation system. Based on balancing the ship's application constraints and energy efficiency indicators, the existing land air separation system is modified to recycle the products' cold energy and the oxygen-enriched combustion of the main engine is constructed. Then, through a sensible energy gradient design and adjusting the circulating flue gas parameters, the design goals are achieved while meeting the host power. The feasibility of the system's application is assessed through thermodynamic and economic analysis, and the optimal technical parameters are determined after working fluids matching and optimization of sensitive parameters based on the genetic algorithm. The results indicate that the system's CO2 capturing purity, exergy efficiency, net output power and annual net profit are 97.09%, 51.78%, 318.36 kW, US$485,165, respectively, demonstrating significant features such as high effective energy conservation and zero carbon emission.

Fire Resistant Building Radiative Cooler with Synergistically Optical Selective Based on 2D/3D Cellulose/LDH Nanosheets Film for Effective Energy Conservation

Fire Resistant Building Radiative Cooler with Synergistically Optical Selective Based on 2D/3D Cellulose/LDH Nanosheets Film for Effective Energy Conservation

Identifying the Most Efficient Natural Fibre for Common Commercial Building Insulation Materials with an Integrated PSI, MEREC, LOPCOW and MCRAT Model.

Building insulation is the most respected among the compatible and effective energy conservation technologies available today, as it also reduces yearly energy costs and negative environmental effects. A building envelope is made up of various insulation materials that are important in determining a building's thermal performance. Proper insulation material selection concludes in less energy requisition for operation. The purpose of this research is to supply information about natural fibre insulating materials used in construction insulation to maintain energy efficiency, as well as to recommend the most efficient natural fibre insulation material. As in most decision-making problems, several criteria and alternatives are involved in insulation material selection, too. Therefore, we used a novel integrated multi criteria decision making (MCDM) model including the preference selection index (PSI), method based on the removal effects of criteria (MEREC), logarithmic percentage change-driven objective weighting (LOPCOW), and multiple criteria ranking by alternative trace (MCRAT) methods to deal with the complexity of numerous criteria and alternatives. The contribution of this study is that a new hybrid MCDM method is developed. Additionally, the number of studies using the MCRAT method is very limited in the literature; therefore, this study will provide more insights into and results of this method to the literature.

An optimal selection of slot/pole combination and its influence on energy efficient PMSM for submersible water pumping applications

In India, some states have a depth of ground water reaching beyond 300–450 m from the ground level. Traditionally, submersible induction motor (SIM)-based pump sets are considered for irrigation. However, SIMs are no longer a good choice for deep bore wells, since there exists a reduction in its operating point, resulting in lower discharge. Based on these facts, submersible brushless permanent magnet motor (SBLPMM) is considered. The proposed SBLPMM is designed for 15 kW with an objective of high torque and power density within the given volumetric and diametric constraints. The proposed SBLPMM will reduce the number of impeller stages when compared with SIMs and hence it will be a viable alternative to the SIMs and hence paying the way for effective energy conservation. The study concentrates on the optimal selection of the geometrical poles and the finite element mesh-based results confirm that the operating efficiency is enhanced with the SBLPMM.