Level Of Energy Efficiency Research Articles

The exposure-response of air pollution and climate change to chronic respiratory diseases: Does residential energy efficiency matter?

Promoting energy efficiency is crucial for reducing energy consumption, yet its impact on human health remains discussed. This study examines the relationship between household energy efficiency, ambient air pollution, climate change, and mortality risk from chronic respiratory diseases. The study collected observational data in six major cities in Taiwan from 2008 to 2020. The energy efficiency level was determined using the input demand function derived from the stochastic frontier analysis (SFA). Subsequently, analysis was conducted employing a dynamic panel data model and a pooled mean group estimator. The study's findings indicate that enhancing household energy efficiency decreases the mortality rate associated with chronic respiratory diseases. Specifically, at the highest level of energy efficiency (99%), the relative risk reaches its lowest value of 0.639 (95% CI: 0.58–0.70). Additionally, a positive exposure-response relationship is observed between degree days and ambient air pollution (PM2.5, PM10, and SO2), associated with an increased risk of death from chronic respiratory diseases. These results underscore the importance of initiatives to enhance energy efficiency programs in households, particularly in metropolitan areas.

ESTIMATIVA DO CUSTO PARA OBTENÇÃO DA CLASSE “A” DO RTQ-R POR UMA EDIFICAÇÃO MULTIFAMILIAR EM BELO HORIZONTE

Taking into account the growing demand for energy in our society, building energy efficiency plays an important role in this context. The households are responsible for a large portion of the Brazilian elec-tricity consumed, representing around 26% of their total. Thus, the present work aims at evaluating the additional cost to reach Class A, according to the Technical Quality Regulation for the Energy Efficiency Level of Residential Buildings (RTQ-R), of a multifamily building in Belo Horizonte, Brazil (bioclimatic zone 3), by the prescriptive method. As a result of the building’s original design analysis, the final classification “B” was obtained. On the other hand, the proposed improvements to achieve class “A” resulted in an increase of 4.71% in the overall cost of the real estate project. Hence, it was concluded that the costs of satisfying the necessary attributes to obtain an efficient building show to be feasible, mainly, due to their benefits for future users provided by labeling it.

How does economic complexity improve energy efficiency? Mechanism discussion and empirical test.

Energy efficiency represents one of the best productive strategies to lessen the effects of global warming and climate change. Similarly, the manufacturing and export of a wide variety of high-tech, knowledge-based items like chemicals and machinery have significant effects on both economic growth and the environment. The main goal of this study is to examine the long-run effects of economic complexity on energy efficiency in 93 countries over the period from 1995 to 2015. The empirical outcomes reveal that economic complexity improves energy efficiency. Specifically, countries that produce and export a wide range of sophisticated products tend to enjoy higher levels of energy efficiency compared to their counterparts who export a limited range of simple products. Moreover, empirical findings suggest that economic growth and population density increase energy efficiency, while trade impedes it. Further empirical investigations from a mediation analysis revealed that about 63% and 38% of the effects of economic complexity on energy efficiency mediate through income inequality reduction and human capitalaccumulation, respectively. Based on these results, political leaders and governments are provided with solid reasons to pay more attention to improving their productive structures in order to promote energy efficiency and a green future.

State of health estimation of lithium-ion batteries based on modified flower pollination algorithm-temporal convolutional network

Lithium-ion batteries (LIBs) need to maintain high energy efficiency and power level in several application scenario. Accurate state of health (SOH) forecast is essential for designing a safe and reliable battery management systems (BMS). Temporal convolutional network (TCN) is a prevailing deep learning method for estimating the SOH of lithium-ion batteries. However, the hyperparameters in the network are usually difficult to predefine, which poses a challenge for the SOH estimation accuracy in real-world. To solve this problem, this paper proposes a data-driven estimation approach, where the TCN is combined with the modified flower pollination algorithm (MFPA) to determine the network topology. After hyperparameter optimization, the external sensor raw data and identified ohmic resistances trajectories in the equivalent circuits model (ECM) are both input to the TCN model to estimate SOH of LIBs. In contrast to prior approaches for feature extraction, this method is not only conductive to improve SOH estimation accuracy, but also can reduce on-board estimation computing burden. We carry out experiments on the same type of cells from NASA public data resources. The experimental results systematically validate the superiority of the proposed method, which covers high estimation accuracy, great robustness to varied training set and satisfied universality to different batteries.

ANALISIS PERHITUNGAN AUDIT ENERGI PADA SISTEM KELISTRIKAN GEDUNG ASRAMA UNIVERSITAS TEKNOLOGI SUMBAWA

The development of science and technology in the world has created many new innovations, including electronic equipment. The use of electronic devices in everyday life is very easy and effective. Along with technological developments and the magnitude of developments in the above sectors in the province of West Nusa Tenggara, the demand for electrical energy always increases every year. To overcome the wasteful use of energy which will result in an increase in electricity payments, energy efficiency must be carried out. The UTS Dormitory Building, as a middle-level consumer of electrical energy, needs to be analyzed to determine the level of energy consumption efficiency. The purpose of this study was to determine the Energy Consumption Intensity (IKE) in the kWh meter of the Sumbawa University of Technology dormitory and to determine the level of energy consumption and efficiency in the kWh meter of the Sumbawa University of Technology dormitory. The method used in this study was a mixed method (Mixed Method). Research, namely by combining the types of qualitative and quantitative methods. The results of the study obtained that the energy consumption intensity value each year in the kWh of the dormitory building is 12.89 kWh/m2/year. This value is included in the efficient category according to the ASEAN-USAID IKE value standard, namely for a hotel/apartment building the maximum is 300 kWh/m2/year. The value of energy consumption intensity (IKE) every year in kWh of dormitory buildings is included in the efficient criteria.

Does green credit affect enterprises’ energy efficiency?

Whether green credit promotes heavily polluting enterprises’ energy efficiency is indeed of great practical significance for China to early achieve the “double carbon” goal. This paper uses the green credit guideline issued in 2012 as a natural experiment drawing on the relevant data of Chinese listed enterprises from 2009 to 2020. It adopts a difference-in-differences model to assess the effect of green credit guideline on energy efficiency. The research finds green credit guideline improve the energy efficiency level of pilot enterprises by 0.0141 compared to non-pilot enterprises, and green credit guideline can improve energy efficiency by encouraging enterprises to increase research and development investment. Further, trade credit promotes the positive impact of green credit guideline on the energy efficiency. Moreover, green credit guideline plays a role in stimulating energy efficiency in enterprises in regions with high marketization degree, enterprises with high liquidity ratio and state-owned enterprises.

Rising energy inequity and its driving factors to approach a just energy transition in China

China, as the world's largest emitter of greenhouse gases, faces the urgent need to transition to a sustainable and low-carbon energy system. A just energy transition is vital to ensure that this transformation is equitable, taking into account social, economic, and environmental factors. One crucial aspect of this transition is assessing provincial energy efficiency across China. This study aims to analyze the energy efficiency performance of different provinces in China and evaluate their progress towards a just energy transition. By employing a comprehensive set of indicators, including energy intensity, energy consumption patterns, renewable energy deployment, and social equity considerations, this research provides a nuanced understanding of the provincial energy landscape. In the last 12 years, the energy efficiency performance of Chinese provinces has exhibited a concerning trend, with 87.1% of the provinces experiencing a decline in energy efficiency. The analysis reveals a significant and consistent decline in energy efficiency across all three regions (eastern, middle, and western) over the course of the past 12 years. Energy efficiency inequality is a prevalent issue observed across all provinces in China, with the eastern region consistently demonstrating higher energy efficiency levels compared to the western region. Each region (eastern region [industry structure and urbanization], middle region [industry structure and financial support] and western region [industry structure and industrialization]) in China presents unique considerations and challenges that must be taken into account to improve energy efficiency effectively. Ultimately, this research seeks to provide valuable insights into shaping effective policies that can accelerate China's progress towards a sustainable and equitable energy future.

Data analysis and interpretable machine learning for HVAC predictive control: A case-study based implementation

Energy efficiency and thermal comfort levels are key attributes to be considered in the design and implementation of a Heating, Ventilation and Air Conditioning (HVAC) system. With the increased availability of Internet of Things (IoT) devices, it is now possible to continuously monitor multiple variables that influence a user’s thermal comfort and the system’s energy efficiency, thus acting preemptively to optimize these factors. To this end, this paper reports on a case study with a two-fold aim; first, to analyze the performance of a conventional HVAC system through data analytics; secondly, to explore the use of interpretable machine learning techniques for HVAC predictive control. A new Interpretable Machine Learning (IML) algorithm called Permutation Feature-based Frequency Response Analysis (PF-FRA) is also proposed. Results demonstrate that the proposed model can generate accurate forecasts of Room Temperature (RT) levels by taking into account historical RT information, as well as additional environmental and time-series features. Our proposed model achieves 0.4017 °C and 0.9417 °C of Mean Absolute Error (MAE) for 1-h and 8-h ahead RT prediction, respectively. Tools such as surrogate models and Shapley graphs are employed to interpret the model’s global and local behaviors with the aim of increasing trust in the model.

Politics of climate change and energy policy in Japan: Is green transformation likely?

While being proud of comparatively high levels of energy efficiency and having experienced the Fukushima nuclear disaster, it may be presumed that Japan would become a leader in the global energy transition. On the contrary, however, Japan has demonstrated a degree of intransigence around deep decarbonization. Why does Japan not take leadership in the energy transition and arrest climate change? What is the context for Japanese climate/energy policymaking? What is the context for the 2050 net-zero target, what are the obstacles, and which actors could assist effectively in accelerating this transition? This article considers new drivers for national policy as well as the influence of exogenous domestic and international forces. This leads to the question of whether the 2050 carbon neutrality policy goal could deliver a rapid shift away from fossil fuels. It presents an analytical framework to explain Japan’s evolving climate-energy nexus, illuminating core variables hampering and enabling the attainment of the net-zero 2050 target.

Pilot study of emerging low-energy seawater reverse osmosis desalination technologies for high-salinity, high-temperature, and high-turbidity seawater

The increasing impact of climate change has worsened drought conditions, leading to a surge in the demand for seawater desalination. However, current seawater reverse osmosis (SWRO) desalination technologies are not energy-efficient for handling the high-salinity, high-temperature, and high-turbidity seawater found in the Arabian Gulf. Therefore, a pilot-scale SWRO desalination plant was established to evaluate the new desalination technologies. The pilot plant tested high-performance SWRO membranes, meshed tube filtration (MTF) as a low-energy pretreatment for the high turbidity induced by algal blooms, and membrane capacitive deionization (MCDI) to improve permeate quality. The high-performance SWRO membrane demonstrated exceptional salt rejection and produced permeate quality in the range of 250–455 mg/L from the feed with salinities of 47,500–53,500 mg/L. MTF was effective in controlling turbidity, and the energy consumption was reduced by 77 % compared to the existing pretreatment process. Furthermore, MCDI exhibited similar levels of energy efficiency as brackish water reverse osmosis and may have the potential for future deployment by enhancing process development. Based on the pilot plant results, it is expected that a 100,000–200,000 m3/d SWRO desalination plant will have a specific energy consumption of 3.11–3.13 kWh/m3 for final product water quality at or below 200 mg/L.

The Impact of Green Finance and Resource Tax Policy on Regional Energy Efficiency Based on the Non-Desired Output Super-Efficiency SBM-Tobit Model

With the continuous growth of the global population and rapid economic development, the demand for energy is increasing, and the increasing scarcity of energy resources and severity environmental problems have become important factors limiting sustainable economic and social development. Therefore, achieving sustainable energy development has received global attention. The main purpose of this work was to measure the energy efficiency (EE) of different regions based on China’s 2008–2021 panel data using the super-efficient SBM model and to examine the roles of green finance and resource tax policies in promoting energy efficiency using the Tobit model, so as to further improve China’s EE, optimize the energy structure, and improve environmental pollution. We concluded the following: First, the average EE value is about 0.549, and there is high regional heterogeneity, which is high in the east and low in the west. Second, the development of green finance at the national level and in the eastern regions promotes EE and achieves the mutual benefits of economic development and ecological protection, while in the western region, the development of green finance significantly suppresses the EE level and is too low to have a significant effect on EE improvement in the central region. The resource tax policy can significantly improve the EE at the national level and in the eastern region, but on the contrary, it does not have a significant effect on improving the EE in other big regions. Third, the degree of openness to the outside world significantly improves the EE at the national level and in the eastern region. However, in the other two big regions, this effect will not be significant. The effect of the industrialization level on the EE at the national level and in the central and western regions is significantly negative, while in the eastern region, it is negative but not significant. The effect of the energy price level on the EE at the national level and in the central and eastern regions is positive, while it is not significant in the western region. Human capital can improve the regional EE in all regions, and the central region has the highest elasticity coefficient.

Do geopolitical risks facilitate the global energy transition? Evidence from 39 countries in the world

Geopolitical risks exert a profoundly detrimental impact on global economy, investment and resource security. Do geopolitical risks affect the worldwide energy transition remains open and controversial. To complement research related to geopolitical risks and energy transition, this research checks the influence of geopolitical risks on energy transition. Based on data collected from 39 countries from 2003 to 2019, it reveals that geopolitical risks exert a promotion influence on energy transition, including energy consumption transition and energy production transition. Moreover, the influence of geopolitical risks on energy transition can be further amplified by improving financial incentive, energy efficiency level and government governance capacity. That is, financial incentive, energy efficiency level and government governance capacity can positively moderate the influence of geopolitical risks on energy transition. Additionally, heterogeneous analysis further uncovers that the influence of geopolitical risks on energy transition varies across economic level and regions. Specifically, the influence of geopolitical risks on energy production transition is relatively high in European and American developed countries, while the influence of geopolitical risks on energy consumption transition is more obvious in Asian, African and Latin American developing countries. Current findings are valuable for governments and scholars to promote energy transition.

The Energy Saving Renovation of Regional Buildings Based on Bayesian Estimation

With China's new urbanization vision and existing urban renewal and building energy efficiency improvement project, large-scale energy conservation renovation of regional buildings has become an important content of ecological environmental protection and low-carbon and an important means to improve the regional energy efficiency level. Of energy-saving reform foundation database is presented in this paper, including construction, power consumption, equipment, operation, technology and willingness to word stock, statistics obtained the regional energy complex parameter (envelope thermal performance, elevator equipment, power and efficiency of cooling and heat sources, etc.) statistical rule and can use the benchmark, build the regional buildings energy consumption prediction model based on bayesian estimation, The probability matrix of large-scale reconstruction of regional buildings is obtained. At the same time, the global sensitive parameter analysis method and the single parameter regression model of energy saving transformation factor are established, and it is found that the internal load factor has the most significant effect on energy consumption. By adjusting energy parameters, the probability distribution curve of energy saving and the distribution of normalized value under the constraint conditions of different reconstruction scenarios were simulated, and the discrete MC simulation method was used to predict the regional overall energy saving reconstruction based on renovation intention, which could realize the trade-off judgment and gradual optimization of the strategy package of energy saving measures combination.

BAS-PSO Algorithm for Integrated Energy System Optimization of Multiple Energy Supply Facilities

Regional low carbon will play an important role in the path of achieving the “double carbon” goal, and there are still many key technologies to be broken in its planning research. In this paper, an optimal scheduling model of integrated energy system with multiple energy supply devices is established by using Beetle Antennae Search-Particle Swarm Optimization (BAS-PSO) algorithm. First, in the scheduling model, a carbon trading mechanism is introduced and a stepped carbon trading cost model is constructed to constrain the carbon emissions of the plant. Then, using drosophila algorithm, the premise of whether wind power generation and photovoltaic power generation need to be built is determined by judging the economics of wind power generation and photovoltaic power generation in the construction area, and then the target power consumption curve and new energy power supply output curve are fitted. Then use the life cycle analysis method to analyze the carbon emissions generated by electric energy storage equipment, consider the carbon trading mechanism in the system economic operation model, and solve the model by BAS-PSO algorithm to overcome the problems of local optimum and slow convergence speed. Finally, a typical park integrated energy system is simulated to analyze the economic operation conditions and energy efficiency level of the system before and after participation in demand response. The innovation of this paper lies in considering the carbon trading mechanism and solving with the optimized BAS-PSO algorithm. Considering carbon trading can effectively improve the system wind power consumption capacity, and the optimized BAS-PSO algorithm improves the defects of traditional PSO. The simulation analysis results show that the established optimal scheduling model with multiple energy supply devices can realize the optimal operation of the integrated energy system of the park in the demand response environment; the improved BAS-PSO finds the lowest energy consumption within 100 iterations in 21 out of 50 iterations; the improved BAS-PSO algorithm reduces the energy consumption by 27.1 kW on average.

Performance of an Adaptive Optimization Paradigm for Optimal Operation of a Mono-Switch Class E Induction Heating Application

The progress of technology involves the continuous improvement of current machines to attain higher levels of energy efficiency, operational dependability, and effectiveness. Induction heating is a thermal process that involves the heating of materials that possess electrical conductivity, such as metals. This technique finds diverse applications, including induction welding and induction cooking pots. The optimization of the operating point of the inverter discussed in this study necessitated the resolution of a pair of non-convex mathematical models to enhance the energy efficiency of the inverters and mitigate switching losses. In order to determine the most advantageous operational location, a sophisticated surface optimization was conducted, requiring the implementation of a sophisticated optimization methodology, such as the adaptive black widow optimization algorithm. The methodology draws inspiration from the resourceful behavior of female black widow spiders in their quest for nourishment. Its straightforward control variable design and limited computational complexity make it a feasible option for addressing multi-dimensional engineering problems within confined constraints. The primary objective of utilizing the adaptive black widow optimization algorithm in the context of induction heating is to optimize the pertinent process parameters, including power level, frequency, coil design, and material properties, with the ultimate goal of efficiently achieving the desired heating outcomes. The utilization of the adaptive black widow optimization algorithm presents a versatile and robust methodology for addressing optimization problems in the field of induction heating. This is due to its capacity to effectively manage intricate, non-linear, and multi-faceted optimization predicaments. The adaptive black widow optimization algorithm has been modified in order to enhance the optimization process and guarantee the identification of the global optimum. The empirical findings derived from an authentic inverter setup were compared with the hypothetical results.

The heterogeneous effect of administrative level of cities on energy efficiency: a panel study of China

ABSTRACT Although the nexus between urbanization and energy efficiency has been evidenced at the macro level, we have less knowledge about the effects of administrative level of cities on energy efficiency. Understanding this impact mechanism is necessary to achieve goals of energy conservation and sustainable development. We estimate the relationship between the administrative level of cities and energy efficiency in China by adopting the province-level fixed-effect models from a panel dataset. The empirical findings present that energy efficiency of capital cities is 0.46 (95% Confidence Interval: −0.713, −0.206) lower than that of general cities. Moreover, these effects of administrative level are more substantial in underdeveloped cities. These findings suggest that more political sources and autonomy in the higher-level cities might lead to low energy efficiency.

Bi-Usage Energy Efficient Techniques (BUEET) for Energizing the Vehicle and Broadcasting the Information in Highway Scenario

The proposed technique BUEET is introduced mainly for two major reasons such as (i) RSU broadcast the emergency messages and the energy status to the vehicles without any interruption. It helps to shake hands with the neighboring node for energy sharing and (ii) RSU boost up the energy efficiency level with the help of energy sharing by the adjacent vehicles. Most of the self-organizing protocols in wireless sensor networks considers only initial energy consumption phase and neglects the maintenance phase of topology. The vehicles are cooperatively interacted to form a reliable network structure. RSU’s are placed in the roadway infrastructure and On-Board Units (OBU’s) are placed in the vehicles, then the communication takes place with the help of these devices. Therefore, this provides the clear roadmap structure by receiving the information from various sources of vehicles. If the information received from the RSU is not relevant to the vehicles then it automatically transmits the information to the adjacent vehicles travelling in the highway. The adjacent vehicle checks the information and transmits to another vehicle if it is relevant. Therefore, the proposed method considers two parameters such as packet transmission and energy efficiency on NS3 simulation. Packet transmission among the vehicles plays a vital role in the proposed technique and the nodes without any route breaks and the loss of connection leads to a strong network. Energy efficiency is analyzed and compared with other existing schemes; in result it is proved that BUEET’s energy efficient is higher in all the nodes. It helps to communicate among the nodes without energy loss. When the energy among the nodes in the network is high, then the performance of the nodes also good. The maximum number of Hop’s helps to transmit the information without any delay.

ИССЛЕДОВАНИЕ ОСОБЕННОСТЕЙ ИСПОЛЬЗОВАНИЯ НОРМАЛИЗОВАННОЙ ТЕПЛОВОЙ НАГРУЗКИ ДЛЯ ОПРЕДЕЛЕНИЯ УРОВНЯ ЭНЕРГОЭФФЕТКИВНОСТИ КОТЕЛЬНОГО АГРЕГАТА В КОНКРЕТНЫХ УСЛОВИЯХ

When comparing the efficiency gross of the same units operating under different conditions, or the same unit for different periods of time, often does not take into account that these values are obtained under different conditions. It is well known that the efficiency of the boiler unit depends on the heat load of the boiler. As a rule, the value of efficiency maximum at moderate loads of the boiler unit and then with increasing heat load gradually decreases according to a law close to linear. Thus, it is possible that for a unit with a better technical condition, an efficiency value will be obtained that is lower than that of another unit operating in more favorable conditions. There is also ambiguity in the presentation of the efficiency dependence. boiler unit from heat load. The paper studies the issue of the features of using the normalized heat load to determine the level of energy efficiency of a boiler unit based on the TVG-4r boiler of a small quarterly boiler house. It is shown that a feature of using the reduced efficiency, which is determined depending on the normalized heat load, is that this indicator characterizes not only the actual technical condition of the boiler unit, but also the efficiency of its regulation, taking into account changes outside air temperature

The Statutory Model of Energy Performance Contracting as a Means of Improving Energy Efficiency in Public Sector Units as Seen in the Example of Polish Legal Policies

The intensification of the EU’s institutional action to ensure an appropriate level of energy efficiency demonstrates the key role of pro-saving measures and energy efficiency measures to achieve a climate-neutral economy by 2050. Achieving this goal is therefore also possible through energy performance contracting (EPC), which enables specialised entities called energy service companies (ESCOs) to operate in the market. The creation of an appropriate and favourable framework at the EU level for the use of EPCs does not imply an identical approach by all individual member states. For this reason, the domestic policy of one member state, Poland, was contrasted with the EU framework. Under the influence of the EU guidelines and regulations, the EPC legislations in Poland was amended. This provided a reason to analyse the importance of legal factors (conditions) in implementing the EPC model, especially from the perspective of public institutions. In light of the above, it was attempted to show whether legal regulations would be sufficient to increase the number and level of ESCO projects in public sector units and whether they would remove the barriers to EPC development that are seen in public–private partnerships and the impact of obligations under an energy efficiency improvement agreement on the level of government debt or public finance sector deficit. This paper adopts research methods that are typical to studying EPC topics; however, the research conducted here is theoretical (descriptive analysis) and of dogmatic legal in nature.

History of blast furnace blowers.

The development of blowers is inextricably linked with the stages of the history of technology and blast furnace production. The amount of blast is one of the main parameters of cast iron production, determining the size and performance of the blast furnace. Consideration of the issues of changing the design and drive of blowers starts from the natural draft and wedged leather bellows. The use of a horse-drawn drive or a water co-scaffold made it possible to increase the amount of air supplied to the furnace. The important event in the development of blowers was the advent of a steam engine, which made it possible to ensure industrial steel production in the middle of the XIX century. A fundamental change in the design of blowers occurred during the transition from reciprocating machines to centrifugal machines ‒ turboblowers powered by a steam turbine drive. Various design solutions of blowers of the transition period of the beginning of the XX century are considered, on the example of the technical equipment of the blower station of the Donetsk Metallurgical Plant (the plant of the Novorossiysk Society). Modern blower machine designs and characteristics are shown. It is noted that the cessation of the increase in the volume of created blast furnaces is associated with the achievement of the capacity limit of blowers. It is noted that in a number of cases, on the basis of technical and economic calculations, the possibility of obtaining a higher level of energy efficiency of the process of supplying blast to a blast furnace is substantiated when gas-tubine installations operating on blast-furnace gas are used to drive a turboblower. Some increase in the efficiency of the blast furnace process can be obtained, by considering the operation of a complex of devices: a turbo blower, an air heater, an air piping system.