Energy Star Research Articles

Universe and Multiverse Transportation Possibilities Using Consciousness and Thought Travelling

Universe and Multiverse Transportation Possibilities Using Consciousness and Thought Travelling

Facile synthesis of N/O dual-doped carbon spheres with excellent electrochemical performance

Sodium ion battery is a new energy star to meet carbon neutrality idea due to abundant resources and low price. The exploration of proper anode materials to accommodate Na+ is a significant research subject. Here, N/O dual-doped carbon spheres (NOCS) derived from polyaniline have been easily prepared via pyrolysis treatment of polyaniline spheres (PS). When applied as an anode material for SIBs, NOCS shows the superior capacity (314 mAh g−1 at 50 mA g−1), good rate performance (139 mAh g−1 at 2 A g−1) and excellent structural stability. The great electrochemical capacity of NOCS can be attributed to the unique spherical structure, large carbon interlayer distance (0.375 nm), proper specific surface area (211 m2 g−1) and abundant heteroatom content (6.27 at% of nitrogen and 14.88 at% of oxygen). NOCS also displays excellent capacitance (104.4 F g−1 at 20 A g−1) and long cycling life (145.4 F g−1 at 1 A g−1 after 5000 cycles) as an electrode material for supercapacitors. These results illustrate that designing structure and composition will effectively promote the electrochemical properties of carbon materials.

Energy use and environmental degradation in Europe: evidence from panel nonlinear ARDL

This study examines the nonlinear effect of energy use including coal, gas, oil, and electricity on CO2 emissions in European Union (EU) countries. Annual panel data of twenty-one European Union countries i.e. Austria, Azerbaijan, Belarus, Bulgaria, Belgium, Denmark, Finland, France, Greece, Germany, Hungary, Ireland, Italy, Kazakhstan, Lithuania, Netherland, Norway, Spain, Slovakia, Switzerland, and Ukraine from 1990 to 2018 is used, while panel nonlinear ARDL methodology is applied for examining the long and short run association. Findings showed that an increase in the coal, electricity, and oil usage increase carbon dioxide emissions, while a reduction in coal, electricity, gas, and oil usage reduce carbon dioxide emissions in the long run. Results of panel nonlinear ARDL (NARDL) model prove the existence of asymmetric impact of energy usage including coal, electricity, and oil on CO2 emissions in European countries. Bidirectional causality exits among positive partial sum of gas usage and CO2 emissions, as well as among partial positive component of oil usage and CO2 emissions in the short run. Pollution needs to be controlled by using new and efficient technologies by making research and development centers more efficient and productive, while the usage of renewable energy resources should be promoted by replacing the traditional energy resources to reduce emissions. ENERGY STAR strategy can be used to improve the energy performance of commercial buildings and industrial plants as well as government has to formulate policies of production which are environment friendly.

Large-Scale Modeling and DR Control of Electric Water Heaters With Energy Star and CTA-2045 Control Types in Distribution Power Systems

This article proposes a generalized energy storage model for battery energy storage systems, electric water heaters (EWH), and heating, ventilation, and air-conditioning (HVAC) systems to enable demand response control complying to Energy Star and CTA-2045 standards. The demand response control has been implemented in the DER integration testbed, which was originally developed by Electric Power Research Institute, to demonstrate that the “energy content” and “energy take” for BESS and EWH with mixing valve technology are comparable for typical residential ratings. A distribution power system was modeled using the modified IEEE 123-bus feeder system, measured residential loads, and EWH power simulated based on realistic hot water draws from CBECC-Res software. The demand response control, which complies to CTA-2045 standards was implemented to the EWHs considering the energy take values. Results demonstrate that the EWHs can be controlled to postpone the peak power at the distribution system level and provide a large amount of energy storage, while maintaining system robustness. The impact on occupant comfort was also analyzed.

Analysis of Energy Efficiency Characteristics of Domestic and Foreign EXIT Lights

In this study, the energy efficiency of EXIT lights, a firefighting product, was analyzed from the perspective of general lighting equipment by examining domestic and foreign certification systems, such as KS, IEC, and the American ‘Energy Star Program’. Experiments were performed on EXIT lights from the United States, Japan, Austria, and domestic EXIT lights. The energy efficiency test factors derived from domestic and foreign standards include power, power factor, current total harmonic distortion, third harmonic current component, and fifth harmonic current component. The results of the test indicated that the US EXIT light consumed the least energy at 2.8 W, satisfying the US EXIT light Energy Star Program regulations, while other products consumed about 5-6 W. Additionally, the US product exhibited the lowest power factor of 0.1, while other products showed a value between 0.5 and 0.62; so there was no product that satisfied the KS (0.9 or more) and Energy Star Program (0.7 or more) standards. The total harmonic distortion (THD) of current in the US product was the lowest at 23.1%, while the Austrian product showed the highest THD at 132%. None of the products satisfied the KS standard of 20% or less. However, all products satisfied the KS standard of 86% or less and 61% or less for the 3rd and 5th harmonic components, respectively. Although the power consumption of the EXIT lights is as low as several watts, if the demand for high-power EXIT lights (several tens of watts) equals the level of lighting equipment in the future, it will be necessary to increase energy efficiency by employing power-factor-limiting regulations for EXIT lights.

Nuclear Symmetry Energy and Hyperonic Stars in the QMC Model

The nuclear symmetry energy, together with the other saturation properties of symmetric nuclear matter, plays an important role in low energy nuclear structure of terrestrial systems, as well as astrophysical objects. In particular, its density dependence, both in sub- and supra-saturation regions in high density matter in neutron stars, is of utmost significance and has been a subject of active research for decades, usually within a mean-field framework. We report results obtained using the latest version of Quark-Meson-Coupling Model (QMC-A) with just three variable parameters, the baryon-meson coupling constants in free space. It is shown that these parameters can be determined directly using nuclear matter (NM) properties at saturation; two parameters of symmetric nuclear matter (SNM), the baryon number density and the energy per particle, and the symmetry energy coefficient of asymmetric nuclear matter (ANM). The effects of uncertainties in the these parameters and propagation of these uncertainties through the calculation of properties of dense hyperonic matter and cold neutron stars are demonstrated. This approach leads to new limits on both the NM parameters and the QMC coupling constants. The results, which exploit the unique features of the QMC model, are discussed and future prospects are outlined.

Consumer Preference for Energy Label in the Purchase Decision of Refrigerator: A Discrete Choice Experiment Approach in the East Coast, Malaysia

Energy label is a widely used policy instrument to increase consumer awareness of energy-efficient home appliances. It helps consumers make better informed purchasing decision intending to save on the electricity bill. The increase in energy efficiency for a household can generate significant energy savings and emissions reduction which can reduce environmental impact. The energy label targets to fight climate change, protect the environment and is significant to support Sustainable Development Goals (SDGs). This study presents the results of a discrete choice experiment (DCE) on the East Coast, Malaysia to investigate the consumer preferences for energy label in the purchasing decision of a refrigerator. Multinomial logit (MNL) and mixed logit (ML) models are specified to measure the attributes that consumers assess when choosing refrigerators. This study focuses on four attributes, namely energy star, energy consumption, energy saving and refrigerator price. Findings show that consumers have responded positively to the labels, in which about 88.11% of respondents are willing to pay to get better quality appliances that promote a safe environment while 11.89% of respondents are not willing to pay. The findings are useful in improving the effectiveness of existing energy efficiency and labelling programs to accelerate the adoption of energy-efficient technology in Malaysia. Hence, the implementation of energy label promotes energy-efficient appliances, which is in line with SDG Goal 7: Ensuring access to affordable, reliable, sustainable, and modern energy for all.

Does Energy Star Certification Reduce Energy Use in Commercial Buildings?

Does Energy Star Certification Reduce Energy Use in Commercial Buildings?

Can analytics software measure end user computing electricity consumption?

The purpose of this research is to answer the question, ‘can analytics software measure end user computing electricity consumption?’ The rationale being that the success of traditional methodologies, such as watt metres, is limited by newly evolved barriers such as mobility and scale (Greenblatt et al., in Field data collection of miscellaneous electrical loads in Northern California: initial results. Ernest Orlando Lawrence Berkeley National Laboratory research paper, pp 4–5, 2013). Such limitations significantly reduce the availability of end user computing use phase energy consumption field data (Karpagam and Yung, in J Clean Prod 156:828, 2017). This causes computer manufacturers to instead rely upon no-user present energy efficiency benchmarks (Energy Star, in Product finder, product, certified computers, results. Washington, D.C.: United States Department of Energy. https://www.energystar.gov/productfinder/product/certified-computers/results, 2021) to act as baseline data for product carbon footprint reports. As the benchmark approach is previously tested to cause scope 2 greenhouse gas emissions quantification to be inaccurate by − 48% to + 107% (Sutton-Parker, in Determining end user computing device Scope 2 GHG emissions with accurate use phase energy consumption measurement, 1877-0509. Amsterdam: Science Direct, Elsevier B.V., 2020), testing a new methodology that includes the impact of human–computer interaction is arguably of value. As such, the proposed method is tested using a distributed node based analytics software to capture both computer asset and human use profile data sets from one hundred and eleven computer users operating in a subject organisation for 30-days. The simple rationale is that the node, unlike a watt metre, is not restricted by location, can be deployed and monitored globally from a centralised location and can move with the computer to ensure constant measurement. The resulting data sets are used to populate a current use phase electricity consumption calculation data flow (Kawamoto et al., in Energy 27:255, 2001; Roth et al., in Energy consumption by office and telecommunications equipment in commercial buildings: energy consumption baseline, 2002) in order to examine for omissions. Additionally, to test for data accuracy, one computer user acts as a control subject, measuring electricity consumption with both a watt-metre and the analytics software. The rationale being that the watt-metre data is extensively proven to be accurate (Energy Star, in Energy star computers final version 8.0 Specification, Washington D.C., United States Department of Energy. https://www.energystar.gov/products/spec/computers_version_8_0_pd, 2020) and will therefore expose errors produced by the software in relation to power draw, on-time and resulting kilo-watt hours (kWh) values. Further to the data capture period, the findings are mixed. Positively, the new method overcomes the barriers of numerous, assorted devices (scale) operating in ever changing locations (mobility). This is achieved by the node reporting in real-time make and model asset data together with device specific electricity consumption and location data via internet technologies. Negatively, the control subject identifies that the electricity consumption values produced by the software are inaccurate by a relatively constant 48%. Furthermore, data omissions are experienced including the exclusion of computer displays caused by the node requiring an operating system to collect data. This latter point would exclude the energy consumption measurement and therefore concomitant greenhouse gas emissions of any displays connected to desktop or mobile computers. Consequently, whilst the research question is answered, the identification of the software exaggerating use phase energy consumption by 48% and excluding peripheral devices, determines the analytics methodology to be in need of further development. The rationale being that use phase consumption quantification is key to lifecycle assessment and greenhouse gas accounting protocol and both require high levels of accuracy (WBCSD and WRI, in The greenhouse gas protocol. A corporate accounting and reporting standard, Geneva, Switzerland and New York, USA. https://ghgprotocol.org/corporate-standard, 2004). It is therefore recommended that further research be undertaken to specifically address omissions and to reduce the over reporting aspect identified as caused by algorithms in the software used to calculate hardware power draw.Graphical abstract

Energy performance certificates in the USA and in France-a case study of multifamily housing.

In the USA and the European Union, buildings account for more than 40% of total energy use and a large proportion of buildings are energy inefficient. Countries address these inefficiency challenges with various initiatives and strategies. One of them relies on rating buildings with energy performance certificates, with the goal that awareness on energy consumption would lead to an efficient retrofit. In this article, we analyze the different methods chosen by the USA and France to rate multifamily buildings, i.e., the Energy Star score and the Diagnostic de Performance Energétique. We conduct a case study of a multifamily housing using a Design of Experiments to determine what inputs are the most influent on the output. In the French certificate, the results show that the climate, ventilation system, and building envelope are the most influent inputs on the energy consumption. In the USA certificate, the actual energy consumption and the climate are the most influent factors on the building score. We then discuss the significant differences in the two approaches, and the consequences in terms of accuracy, as well as how the DPE and ES scores are used as a tool in public energy policy to propose energy conservation measures and reduce energy consumption.

Customer behavior in purchasing energy-saving products: Big data analytics from online reviews of e-commerce

With the acceleration of China's urbanization and the increasing number of household appliances, the promotion of energy-saving products has attracted widespread attention. However, the influence of online reviews on energy-saving products, especially the influence of energy-efficiency-related textual reviews, has not been elucidated. Drawing upon product information and consumer comments from one of China's largest e-commerce platforms, we covered 1188 products and over 600,000 corresponding online textual reviews. After categorizing the textual reviews, we constructed a dictionary of energy-saving attitude. Based on an elaboration likelihood model (ELM), this study analyzes the influence of energy-saving related e-WOM on product sales by a linear regression model. With an intermediary role in consideration, this study also demonstrates the influence mechanism of online reviews on purchase decision through the central route (the effects of gross sentiment, e-WOM and review tag on product sales) and peripheral route (the effects of star rating and review numbers on product sales) in ELM. The results show that both the online reviews and the rating scores of energy star have a strong influence on product sales in a short period of time. The gross sentiment of an online review can influence product sales, which is wholly affected by the intermediary role of the star rating. The central route can also influence product sales indirectly through the peripheral route.

Design of energy-saving driver circuit for 80 Watt LED using Flyback Converter

Due to numerous features such as compact size, longevity, mercury-free start, and higher color rendering index, the introduction of light-emitting diode (LED) lights has changed residential and commercial lighting systems worldwide. There are two main types of LED driver configurations: single-stage (SS) and double-stage (DS). Low-power systems employ SS configuration converters, while high-power commercial systems use DS configuration converters. This study presented a discontinuous conduction mode (DCM) flyback converter with a PI controller for LED lighting, replacing double-stage converter systems. The proposed DCM-based flyback AC-DC converter includes fewer components, lower total harmonic distortions (THD), improved power factor, and high efficiency. The hardware prototype was created and tested at various AC main voltages to ensure the feasibility of the proposed model. At rated voltage 230 V, system THD is 5.2 %, power factor is 0.995, and efficiency is 92.1 %. The whole system efficiency meets the Energy Star standards.

Spatio-temporal heterogeneity analysis of energy use in residential buildings

Conventional building energy dynamic analysis relies on climatic and geographical statistics of buildings. A large number of studies focus on investigating the major influencing factors and the energy use with engineering methods and statistical methods to predict buildings' energy use. However, due to the lack of temporal statistics when analyzing buildings' energy use intensity (EUI), very few studies concerned the energy consumption from spatial and temporal heterogeneity simultaneously. This study extends the traditional regression model by introducing the temporal variations into the geographically weighted regression (GWR) model and developed a geographically and temporally weighted regression (GTWR) model. A case study, which is based on four years’ urban building energy consumption (from 2015 to 2018) in the city of Seattle, is used to validate the performance of the proposed regression model. Meanwhile, the ordinary least square (OLS), GWR, and temporally weighted regression (TWR) models are developed for making cross-comparison of the goodness-of-fit. The results show the GTWR and TWR models reduce the mean absolute error by 13% and 7%, respectively, relative to the GWR model. Moreover, the GTWR model has a substantial improvement of goodness-of-fit, which can be referred from the value of R2 increasing by 49% in OLS, 16% in GWR, and 6% in TWR. Two driving forces, the number of floors (NF) and energy stars (ES), are identified to have the most significant coefficients in evaluating the site EUI. The novelty of this research lies in achieving better reliability and accuracy in building energy modeling by GTWR, which provides an assisting tool for government and local authorities to make the master plan and sustainability strategies for the new buildings in the city.

FEASIBILITY STUDY OF NET-ZERO ENERGY RESIDENTIAL BUILDINGS IN HOT AND HUMID CLIMATES: A CASE STUDY OF IRAN

The use of solar energy in buildings is a good alternative to fossil fuels considering the geographical location of Iran. The present research evaluated the feasibility of net-zero energy residential buildings in southern Iran using a distinct framework. To this end, a low-energy and simulated model of a multi-family residential building as the dominant typology in Bushehr city equipped with combined (active and passive) solutions were used in Design Builder software for optimizing energy consumption. Annual electricity consumption of this simulated model was reduced by 48% using very low power density appliances and an Air Source Heat Pump (ASHP) water heater with an Energy Star label to supply hot water. Then, a grid-connected PV system with a capacity of 29 kW was used to provide the annual electricity demand. The results indicate that this system annually generates 52 MWh of electricity that not only covers the annual electricity demand but also sends 5.8 MWh of additional electricity to the grid. The Economic analysis indicates that the payback period in this project will be very long. Therefore, achieving NZEBs in southern Iran is technically feasible but, from the economic point of view, it depends on the provision of the economic infrastructures.

Bunching with the Stars: How Firms Respond to Environmental Certification

Voluntary environmental certification programs have been a popular tool used by governments, industry groups, and nonprofit organizations alike. A central question in the design of such programs is who should pay for them. In a context where firms respond strategically to a certification, the answer to this question is a priori ambiguous and, ultimately, empirical. This paper provides important insights on this question using ENERGY STAR, a voluntary certification program for energy-efficient products, as a case study. I show that firms are highly strategic with respect to this certification and extract consumer surplus associated with certified products via three mechanisms. They offer products that bunch at the certification requirement, differentiate certified products in the energy and nonenergy dimensions, and charge a price premium on certified products. I use these findings to motivate a structural econometric model with firms’ strategic behaviors with respect to product line and pricing decisions and to investigate the incidence of a certification licensing fee to fund the certification program. This paper was accepted by Juanjuan Zhang, marketing.

Slowly rotating dark energy stars

We study isotropic and slowly-rotating stars made of dark energy adopting the extended Chaplygin equation-of-state. We compute the moment of inertia as a function of the mass of the stars, both for rotating and non-rotating objects. The solution for the non-diagonal metric component as a function of the radial coordinate for three different star masses is shown as well. We find that (i) the moment of inertia increases with the mass of the star, (ii) in the case of non-rotating objects the moment of inertia grows faster, and (iii) the curve corresponding to rotation lies below the one corresponding to non-rotating stars.

Dark energy stars in Tolman–Kuchowicz spacetime in the context of Einstein gravity

Dark energy is the component in the present Universe with the greatest abundance, and it is responsible for the accelerating expansion of the Universe. As a result, dark energy is likely to interact with any compact astrophysical object [Muhammad F.A.R. Sakti and Anto Sulaksono, {\it Phys. Rev. D} {\bf 103}, 084042 (2021)]. In present paper, we propose a model for a dark energy star made up of dark and ordinary matter in which the density of dark energy is proportional to the density of isotropic perfect fluid matter. In the context of general relativity, the model is derived in the curved Tolman-Kuchowicz spacetime geometry [Tolman, Phys Rev 55:364, (1939); Kuchowicz, Acta Phys Pol 33:541, (1968)]. Here, we look at how dark energy affects stellar mass, compactness, and equilibrium etc. The physical parameters of the model e.g., pressure, density, mass function, surface redshift etc. are investigated, and the stability of stellar configuration is studied in detail. The model has interesting properties because it meets all energy criteria and is free from central singularities. The maximum allowable mass has been obtained from our model with the help of $M-R$ diagram. We analyse many physical properties of the model and checked that it meets all regularity constraints, is stable, and therefore physically realistic.

Impacts of green certification programs on energy consumption and GHG emissions in buildings: A spatial regression approach

For better energy assessment and management in the buildings industry, a spatial regression method is presented to quantitively measure the impacts of green certification programs on energy consumption and Greenhouse Gas (GHG) emissions in buildings. Spatial autocorrelation analysis is developed to determine the spatial pattern of energy consumption and GHG emissions. A Gaussian kernel function method is employed to calculate the spatial weighting matrix, and an Akaike Information Criterion method is implemented to choose the function bandwidth in the spatial weighting matrix. Subsequently, a geographically weighted regression (GWR) model based on the spatial weighting matrix is constructed to estimate energy consumption and GHG emissions in buildings. Several error criteria are employed to verify the development of the GWR model compared with the ordinary least square (OLS) model. Regression coefficient, Pearson correlation coefficient, and Spearman correlation coefficient analyses are implemented to investigate the effects of green certification programs on building energy consumption and GHG emissions. A case study in Seattle city in 2015 is used to implement the GWR model and validate the improved goodness-of-fit of this regression model. The results imply that: (1) Energy stars programs as the driving force would save energy consumption by 13.2% on average and reduce GHG emissions by 3.24% on average; (2) The fitted GWR models can significantly improve the model’s goodness-of-fit and accuracy with an adjusted R2 value of 0.572 in modeling energy consumption and that of 0.626 in modeling GHG emissions compared with the OLS models (with an R2 of 0.538 and 0.594, respectively); (3) Older buildings, taller buildings, and larger buildings with energy star labels have been demonstrated to have a big potential to significantly save more energy and reduce more GHG emissions. This spatial regression approach can be used as an assessment tool for professionals and authorities to detect the influence factors on energy consumption and GHG emissions in buildings and make environmentally sustainable plans for buildings in cities.

A Relativistic Compact Stellar Model of Anisotropic Quark Matter Mixed with Dark Energy

The possibility of strange stars mixed with dark energy to be one of the candidates for dark energy stars is the main issue of the present study. Our investigation shows that quark matter atcs as dark energy after a certain yet unknown critical condition inside the quark stars. Our proposed model reveals that strange stars mixed with dark energy feature a physically acceptable stable model and mimic characteristics of dark energy stars. The plausible connections are shown through the mass-radius relation as well as the entropy and temperature. We particularly note that a two-fluid distribution is a major reason for the anisotropic nature of the spherical stellar system.

Energy Star based benchmarking model for Malaysian Government hospitals - A qualitative and quantitative approach to assess energy performances

The hospital building's operation can be symbolised as high energy usage with different services provision and facilities. The energy performances for hospital buildings in Malaysia are often quantified using a simple Building Energy Intensity (BEI) as the indicator. However, this method has less accuracy on the building's performance due to complex energy systems and different sizes for hospital-built environments. Hereof, this study aims to establish an energy benchmarking indicator for government hospitals in Malaysia using the Energy Star score method. The actual large dataset of hospitals samples has been quantified, scrutinised and optimised using the multiple linear regression (MLR) analysis to identify specific parameters affecting the energy use. The proposed model found that the final output result had shown a good fit for all hospital datasets. Further classifications were conducted on hospitals according to their energy score and air-conditioned area (ACA) for ranking comparison and assessment. Subsequently, from the same benchmarking model, an energy benchmarking calculator was developed in order to assist hospitals in automatically assessing their energy performance and labelling. Therefore, better energy management practices and decisions may be suggested for the Malaysian hospital buildings' continuous improvement. The novelties for this study are the formulated benchmarking models by optimising significant parameters from the actual large dataset of Malaysian hospitals and the developed benchmarking calculator for the regulator and hospital use.