Achieve Energy Savings Research Articles

Analyzing the Time-Varying Thermal Perception of Students in Classrooms and Its Influencing Factors from a Case Study in Xi’an, China

Owing to movement in the spatial environment and changes in activity levels, students’ thermal perception is time varying in classrooms throughout different periods of the day. However, previous studies have rarely considered the time-varying thermal perception in different periods of the day, which may cause discomfort for students and lead to energy wastage. Therefore, a study was conducted to investigate the time-varying thermal perception of students and its influencing factors in different classes of the day. In addition, the differences in students’ adaptive behaviors in different periods were also explored. A total of 578 university students were surveyed using questionnaire surveys during the heating season in Xi’an, China. The following results can be obtained: (1) The thermal sensation vote and thermal preference vote values in the afternoon were significantly higher than those in the morning. At the start of the first class in the morning/afternoon, the thermal sensation of the students had the highest sensitivity to outdoor temperature changes. (2) The students’ thermal perception was greatly affected by the preclass activity state at the start of the first class in the morning/afternoon. However, in other periods, the above phenomenon was not obvious. (3) In the afternoon, the frequency of clothing adjustment was greater than that in the morning, and this behavior would significantly affect the students’ thermal sensation. (4) Compared with the current classroom heating strategy, the heating strategy of dynamically adjusting the indoor set temperature according to the time-varying characteristics of the students can theoretically achieve energy savings of 25.6%.

Easily splittable hollow segmented-pie microfiber nonwoven material with excellent filtration and thermal-wet comfort for energy savings

The segmented-pie composite fibers can be separated by high-pressure hydroentanglement to form a high-strength, lightweight microfiber nonwovens. Nowadays, it is an urgent problem to develop easily split composite fibers and reduce the energy consumption of hydroentangled in bicomponent spunbond hydroentangled technology. Herein, molecular dynamics simulations were first performed for polyester and polyamide 6. The results reveal that the surface adsorption energy between polymers decreases with the decrease of the polymer molecular chain regularity, and the cohesion energy density and solubility parameters reduce with the reduction of the polymer crystallinity. Then polyamide 6 compound spinning with polyesters of different crystallinity, and the segmented pie microfiber nonwoven was prepared through different hydroentangled pressures. The results show that the splitting rate of low crystallinity polyester/polyamide 6 fibers is much higher than that of polyester/polyamide 6 fibers, with a maximum increase of 94.80% at the same hydroentanglement pressure, and the hydroentangled energy consumption can be reduced up to 77.12% for a certain splitting rate. This strategy promotes the complete splitting of composite fibers at a low hydroentanglement pressure, which saves energy and effectively improves the filtration and thermal-wet comfort of nonwovens, providing a viable method for achieving energy savings in bicomponent spunbond hydroentangled technology.

On-Demand Charging Management Model and Its Optimization for Wireless Renewable Sensor Networks.

Nowadays, wireless energy transfer (WET) is a new strategy that has the potential to essentially resolve energy and lifespan issues in a wireless sensor network (WSN). We investigate the process of a wireless energy transfer-based wireless sensor network via a wireless mobile charging device (WMCD) and develop a periodic charging scheme to keep the network operative. This paper aims to reduce the overall system energy consumption and total distance traveled, and increase the ratio of charging device vacation time. We propose an energy renewable management system based on particle swarm optimization (ERMS-PSO) to achieve energy savings based on an investigation of the total energy consumption. In this new strategy, we introduce two sets of energies called emin (minimum energy level) and ethresh (threshold energy level). When the first node reaches the emin, it will inform the base station, which will calculate all nodes that fall under ethresh and send a WMCD to charge them in one cycle. These settled energy levels help to manage when a sensor node needs to be charged before reaching the general minimum energy in the node and will help the network to operate for a long time without failing. In contrast to previous schemes in which the wireless mobile charging device visited and charged all nodes for each cycle, in our strategy, the charging device should visit only a few nodes that use more energy than others. Mathematical outcomes demonstrate that our proposed strategy can considerably reduce the total energy consumption and distance traveled by the charging device and increase its vacation time ratio while retaining performance, and ERMS-PSO is more practical for real-world networks because it can keep the network operational with less complexity than other schemes.

Analysis of Highway Asphalt Modified with Recycled Rubber and Waste Plastics

In this study, it is shown how recycled rubber and waste plastics can modify the softening point and penetration of asphalt traditionally used for highways. It is shown that the modified asphalt can meet the performance index requirements when the components are present with a certain proportion or relative ratio (1:3.5). The dispersion process of the masterbatch in base asphalt can effectively be implemented, with good results and a smaller mixing time. The proposed approach may be regarded as a good strategy to achieve energy savings and protection of the environment.

Modelling the effect of slide table mass on machine tool energy consumption: The role of lightweighting

Much of the environmental footprint of a machine tool is due to the energy consumed during its use phase. A promising strategy for reducing this energy consumption is to minimize the mass of components through changes in the design of the machine tool. This paper explores the lightweighting of the machine slides (or tables) to achieve energy savings. An energy model is developed which considers the table and workpiece mass, the cutting force, and the motor that drives the table. A case study is carried out on a three-axis vertical milling machine to explore the energy savings that can be realized via lightweighting a slide table. The model is used to compare the energy consumption of a solid table and a lightweight table. Results show a potential energy savings of up to 38% in terms of the energy required to move the table. The model is verified using experimental data already found in the literature. It was observed that the model has a lower prediction capability when the table speed and power demand are very low but as the speed and power increase the prediction capability increases significantly.

Analysis of Food Factory Energy Consumption Pattern for Application of Factory Energy Management System(FEMS)

Recently, the Korean government is actively promoting support and investment for climate technology innovation with the goal of becoming carbon neutral by 2050. For this reason, there is a growing interest and necessity for energy saving and efficiency improvement. As an alternative, various solutions have been proposed that introduce a Factory Energy Management System(FEMS) to achieve energy savings through partial system improvement. In the food industry, the scale of energy consumption is gradually increasing due to the rapid improvement of dietary standards. Therefore, energy saving in the food factory is important, but there is no application of the FEMS. In the case of some small factories, it is difficult to ascertain even the amount of energy consumed for actual production. In this study, the energy consumption pattern was analyzed to estimate the amount of energy saved when the FEMS is applied to a food factory. The optimal level of energy consumption for the food processing was derived by predicting the energy demand and supply required in food factories and analyzing the basic unit per product output. In addition, the energy consumption pattern was predicted through data mining to calculate the amount of energy savings, and the accuracy of the prediction model was evaluated.

Optimization of daylight in atrium in underground commercial spaces: A case study in Chongqing, China

Facing urban problems such as rapid urbanization, air pollution, heatwaves, and urban flooding, the underground space, as a significant urban composition, has demonstrated the unique potential. However, the properties of the lighting environment in underground spaces require the consumption of large amounts of energy. An optimal design strategy for natural daylighting in underground commercial spaces with high density, high usage, and high illumination demand can effectively achieve energy savings. In this study, the most common natural daylighting structure, the atrium, was selected. And the orthogonal analysis was used to simulate the lighting environment generated by four influencing factors (shape, number, profile inclination, and skylight aspect ratio) of underground commercial spaces. For an atrium space of two floors, the results show that the case of 1–3 square-shaped atriums, a profile inclination of 5°-10°, and a skylight aspect ratio of 1:6 comprised a suitable combination that can significantly reduce the electricity consumption. Different combinations of the four influencing factors resulted in different energy savings rates, and the highest energy savings rate was 1.2–1.3 times higher than the lowest. This study can be a valuable insight into the energy-saving design of the light environment in underground space.

COMPATIBILITY OF EU AND SERBIAN ENERGY POLICIES WITH SPECIAL REFERENCE TO BUILDINGS

<jats:p>The sustainable development of Serbia and the process of accession to the European Union cannot be viewed as separate or parallel processes, but represent a mechanism of holistic transformation of Serbian society into a sustainable community. It is a matter of normative regulation of the relationship between the new necessary values of true development. The process of harmonization of EU and Serbian energy policies in the construction sector is important, in terms of achieving energy efficiency of buildings. This paper deals with the issues of verification of national construction regulations on energy efficiency of buildings in order to achieve energy savings and strengthen market surveillance of energy efficient products implemented in EU member states.</jats:p>

Enabling Optimal Energy Management with Minimal IoT Requirements: A Legacy A/C Case Study

The existing literature on energy saving focuses on large-scale buildings, wherein the energy-saving potential is substantially larger than smaller-scale buildings. However, the research intensity is significantly less for small-scale deployments and their capacities to regulate energy use individually, directly and without depreciating users’ comfort and needs. The current research effort focused on energy saving and user satisfaction, concerning a low-cost—yet technically sophisticated—methodology for controlling conventional residential HVAC units through cheap yet reliable actuation and sensing and auxiliary IoT equipment. The basic ingredients of the proposed experimental methodology involve a conventional A/C unit, an Arduino microcontroller, typical wireless IoT sensors and actuators, a configured graphical environment and a sophisticated, model-free, optimization-and-control algorithm (PCAO) that portrays the ground basis for achieving improved performance results in comparison with conventional methods. The main goal of this study was to produce a system that would adequately and expeditiously achieve energy savings by utilizing minimal hardware/equipment (affordability). The system was designed to be easily expandable in terms of new units or thermal equipment (expandability) and also to be autonomous, requiring zero user interventions at the experimental site (automation). The real-life measurements were collected over two different seasonal periods of the year (winter, summer) and concerned a conventional apartment in the city of Xanthi, Northern Greece, where summers and winters exhibit quite diverse climate characteristics. The final results revealed the increased efficiency of PCAO’s optimization in comparison with a conventional rule-based control strategy (RBC), as concerns energy savings and user satisfaction.

TECHNICAL FEATURES OF USING A COMPLEX WIND-SOLAR POWER SUPPLY SYSTEM OF AN ENERGY-EFFICIENT HOUSE


 
 
 The wish to achieve energy savings of organic fuel while reducing the negative man-made impact on the environment, especially in the context of global warming, encourages for the search for new approaches in the construction of energy efficient houses and buildings. Analysis of the accumulated meteorological data on wind and solar potentials in Kyiv shows that in an urban city it is possible to create a fully autonomous house, which will be supplied with electricity using solar and wind energy. Year-round monitoring of atmospheric parameters such as current values of wind speed and direction, relative humidity and barometric air pressure was carried out using a portable meteorological station TFA Nexus using appropriate Primus software, which allowed to collect and analyze measurement data. Data on insolation for the location of the energy-efficient house were obtained experimentally in 2019 using measurements with a pyranometer SR-U1. The developed system consists of a wind generator, solar modules, charge controllers, inverters and batteries. The calculation of electricity production by the solar station was performed in the PVSyst program according to meteorological data Meteonorm. At the same time, it should be noted that the energy contribution from photomodules is crucial for the power supply of an energy-efficient house. The proposed instrumental methods for determining the parameters of insolation provide an opportunity to assess its technically available potential for a given area, as well as to use the obtained data for the design of complex power supply systems using energy of wind and solar. The research results prove that in the conditions of the city of Kyiv there are potential opportunities for the creation of residential buildings with zero energy balance, in which, for the purpose of power supply, wind generators and solar modules can be used quite successfully.
 
 

Lignin rebirth enables sustainable afterglow emission

Converting low-quality waste materials into available high-quality products in a simple way is one of the effective ways to achieve energy savings and emissions reductions. Here, Yuan and colleagues took full advantage of abundant lignin to prepare a series of sustainable and environment-friendly afterglow materials and successfully applied them to flexible fabrics.

A low-energy storage container for food and agriculture products

In 2018, the food, beverages, and tobacco sectors within the EU-27 consumed approximately 27,500 ktoe of energy. The food facilities and the food production plants are responsible for a large part of this energy consumption. Current global strategies focus on energy conservation and natural environmental protection, ascribing a lot of importance to building-related analyses. Areas for food storage are essential within the food production chain, as the indoor thermal parameters determine the characteristics of the final products. In this paper, a low-energy storage container is proposed. The envelope of the container is made from sandwich panels with a polyurethane layer paired with two phase change material (PCM) layers. The container is designed to store perishable materials, such as extra virgin olive oil. A storage container prototype, equipped with a mini-split heating, ventilation, and air conditioning electric system, was built to analyse and assess the energy spent during its use. Moreover, the achievable yearly energy savings with respect to a container without the PCM layers was calculated. The results showed that the PCM layers improve the energy performance of the container at an indoor temperature of 20°C with an energy saving of about 27%, and at an indoor temperature of 17°C with an energy saving of over 22%.

Urban Overheating Impact: A Case Study on Building Energy Performance

It is well known that the construction sector is one of the main sectors responsible for energy consumption in the current global energy scenario. Thus, buildings’ energy software become essential tools for achieving energy savings. Climate and its implications for building energy performance are a critical threat. Hence, the aim of this study is to evaluate the climatic conditions in urban and suburban areas of Rome, estimating the incidence of the Urban Heat Island (UHI) phenomenon. To this end, meteorological data obtained from three different areas (two airports and one inside the city) were examined and compared. Then, TRNSYS software was used to create a simple building, in order to assess the impacts of various climatic situations on building energy performance. The study revealed significant percentage differences both in terms of energy needs for heating, from −20.1% to −24.9% when the reference stations are, respectively, Fiumicino and Ciampino, and for cooling, with a wider range, from +48.7% to +87.5% when the reference stations are Ciampino and Fiumicino. Therefore, the study showed the importance of more accurately selecting sets of climate values to be included in energy simulations.

Base Station Power Optimization for Green Networks Using Reinforcement Learning

The next generation mobile networks have to provide high data rates, extremely low latency, and support high connection density. To meet these requirements, the number of base stations will have to increase and this increase will lead to an energy consumption issue. Therefore “green” approaches to the network operation will gain importance. Reducing the energy consumption of base stations is essential for going green and also it helps service providers to reduce operational expenses. However, achieving energy savings without degrading the quality of service is a huge challenge. In order to address this issue, we propose a machine learning based intelligent solution that also incorporates a network simulator. We develop a reinforcement-based learning model by using deep deterministic policy gradient algorithm. Our model update frequently the policy of network switches in a way that, packet be forwarded to base stations with an optimized power level. The policies taken by the network controller are evaluated with a network simulator to ensure the energy consumption reduction and quality of service balance. The reinforcement learning model allows us to constantly learn and adapt to the changing situations in the dynamic network environment, hence having a more robust and realistic intelligent network management policy set. Our results demonstrate that energy efficiency can be enhanced by 32% and 67% in dense and sparse scenarios, respectively.

Measuring aggregate electricity savings from the diffusion of more efficient lighting technologies

Increasing concerns about sustainability and energy conservation, coupled with the proliferation of incentives in the EU to achieve energy savings, suggest that significant improvements in energy efficiency should be realized. A policy measure that should have a direct impact on energy savings is the replacement of incandescent and halogen light bulbs by more efficient lighting technologies, which was implemented in 2009. Due to the lack of detailed data, it is not feasible to measure the effect of energy-efficient improvements on electricity consumption at the aggregate level using a bottom-up approach. To overcome this limitation, this paper analyzes hourly electricity demand in a very specific period of the day: the transition from day to night. In this short period, it is plausible that lighting is the main driver of changes in electricity demand, thus making it possible to estimate the increase in electricity consumption when lights are switched on and to analyze the effects of higher energy efficiency in lighting, if any. The results of the analysis for Spain show that during the periods 2009–2011 and 2015–2016, an estimated energy savings of 251 GWh can be attributed to a reduction in the magnitude of the lighting effect, which accounts for 20.3% of the observed decrease in electricity consumption during these two periods.

Stereoscopic particle image velocimetry study of the single side entrainment behaviour of multiple air jets with different nozzle cross-sections

Achieving energy savings in the building sector while maintaining high air quality of indoor environments requires efficient ventilation systems. Active chilled beams (ACBs) are a promising solution for the ventilation in non-residential buildings. Their functionality is often based on the single side entrainment of multiple air jets. In order to enhance this entrainment and thus increase the efficiency of ACBs, this study investigates the specific flow situation of a single side entrainment experimentally. Additionally to the reference circular type, three different cross-sections (elliptic, cross-like, lobed) of an orifice nozzle are investigated with different experimental methods. The unique flow situation in ACBs with multiple interacting jets entraining air from one side is extracted to a test bench. First, the general nozzle efficiency and total entrainment is investigated. Then, the detailed understanding of the entrainment process is achieved with stereoscopic particle image velocimetry (stereo-PIV). The results show that a minimum distance between the jets is necessary for the development of individual flow behaviour affecting the entrainment. For a larger nozzle distance we measured an increased overall entrainment for the non-circular nozzle geometries, with the lobed geometry showing the greatest improvement. The PIV results show how the jets from the different nozzle geometries pass through different stages of entrainment behaviour and profile transformation.

A data-driven methodology for enhanced measurement and verification of energy efficiency savings in commercial buildings

Methods to obtain accurate estimations of the savings generated by building energy efficiency interventions are a topic of great importance, and considered to be one of the keys to increase capital investments in energy conservation strategies worldwide. In this study, a novel data-driven methodology is proposed for the measurement and verification of energy efficiency savings, with special focus on commercial buildings and facilities. The presented approach involves building use characterization by means of a clustering technique that allows to extract typical consumption profile patterns. These are then used, in combination with an innovative technique to evaluate the building’s weather dependency, to design a model able to provide accurate dynamic estimations of the achieved energy savings. The method was tested on synthetic datasets generated using the building energy simulation software EnergyPlus, as well as on monitoring data from real-world buildings. The results obtained with the proposed methodology were compared with the ones provided by applying the time-of-week-and-temperature (TOWT) model, showing up to 10% CV(RMSE) improvement, depending on the case in analysis. Furthermore, a comparison with the deterministic results provided by EnergyPlus showed that the median estimated savings error was always lower than 3% of the total reporting period consumption, with similar accuracy retained even when reducing the total training data available.

Concentrated solar power: technology, economy analysis, and policy implications in China.

Renewable energy plays a significant role in achieving energy savings and emission reduction. As a sustainable and environmental friendly renewable energy power technology, concentrated solar power (CSP) integrates power generation and energy storage to ensure the smooth operation of the power system. However, the cost of CSP is an obstacle hampering the commercialization of this emerging industry, so the paper studies the technical characteristics, economic analysis, and policy implications of CSP. This paper sorts out the relevant policies of CSP and uses the levelized cost of electricity (LCOE) model by considering financial parameters, investment parameters, operation and maintenance parameters, tax parameters, capacity parameters, etc., to analyze the economics of CSP. The model parameters are set by the actual situation of CSP projects. The economic evaluation of different technology types of parabolic trough (PT), solar tower (ST), secondary reflection ST, and linear Fresnel reflector (LFR) is carried out. The LCOE of PT project is 1.11 RMB/kWh (0.17 US$/kWh), the ST project is 0.93 RMB/kWh (0.14 US$/kWh), the secondary reflection ST project is 0.97 RMB/kWh (0.15 US$/kWh), and the LFR project is 0.92 RMB/kWh (0.14 US$/kWh). The results show that the grid parity era of CSP in China is within reach, and ST is the most potential technology type. Based on the results of economic analysis and the problems faced by CSP in China, this paper puts forward policy implications by preferential loans, tax incentives, and R&D fund support to promote the development of CSP.

Multi-Objective Particle Swarm Optimization-Based Decision Support Model for Integrating Renewable Energy Systems in a Korean Campus Building

Renewable energy systems are an alternative to existing systems to achieve energy savings and carbon dioxide emission reduction. Subsequently, preventing the reckless installation of renewable energy systems and formulating appropriate energy policies, including sales strategies, is critical. Thus, this study aimed to achieve energy reduction through optimal selection of the capacity and lifetime of solar thermal (ST) and ground source heat pump (GSHP) systems that can reduce the thermal energy of buildings including the most widely used photovoltaic (PV) systems. Additionally, this study explored decision-making for optimal PV, ST, and GSHP installation considering economic and environmental factors such as energy sales strategy and electricity price according to energy policies. Therefore, an optimization model based on multi-objective particle swarm optimization was proposed to maximize lifecycle cost and energy savings based on the target energy savings according to PV capacity. Furthermore, the proposed model was verified through a case study on campus buildings in Korea: PV 60 kW and ST 32 m2 GSHP10 kW with a lifetime of 50 years were found to be the optimal combination and capacity. The proposed model guarantees economic optimization, is scalable, and can be used as a decision-making model to install renewable energy systems in buildings worldwide.

Base Station Power Optimization for Green Networks Using Reinforcement Learning

The next generation mobile networks have to provide high data rates, extremely low latency, and support high connection density. To meet these requirements, the number of base stations will have to increase and this increase will lead to an energy consumption issue. Therefore "green" approaches to the network operation will gain importance. Reducing the energy consumption of base stations is essential for going green and also it helps service providers to reduce operational expenses. However, achieving energy savings without degrading the quality of service is a huge challenge. In order to address this issue, we propose a machine learning based intelligent solution that also incorporates a network simulator. We develop a reinforcement based learning model by using deep deterministic policy gradient algorithm. Our model update frequently the policy of network switches in a way that, packet be forwarded to base stations with an optimized power level. The policies taken by the network controller are evaluated with a network simulator to ensure the energy consumption reduction and quality of service balance. The reinforcement learning model allows us to constantly learn and adapt to the changing situations in the dynamic network environment, hence having a more robust and realistic intelligent network management policy set. Our results demonstrate that energy efficiency can be enhanced by 32% and 67% in dense and sparse scenarios, respectively.