Benchmarking Energy Efficiency Research Articles

Template-Assisted Mechanosynthesis Leading to Benchmark Energy Efficiency and Sustainability in the Production of Bifunctional Fe–N–C Electrocatalysts

Template-Assisted Mechanosynthesis Leading to Benchmark Energy Efficiency and Sustainability in the Production of Bifunctional Fe–N–C Electrocatalysts

Energy efficiency of drinking water treatment plants: A methodological approach for its ranking

Drinking water treatment systems (DWTSs) are energy intensive facilities, and are an example of the water-energy nexus. Benchmarking energy efficiency is a valuable tool for improving the economic and environmental performance of such facilities. Data envelopment analysis (DEA) is typically used to assess efficiency, allocating flexible weights (FSW) to variables that maximise energy efficiency scores for each DWTS (DEA-FSW). It means that different conditions are applied to each DWTS. Moreover, the DEA-FSW approach has finite discriminatory power which limits cross-unit comparison of energy efficiency hindering the benchmarking of DWTSs. To overcome these limitations, our study explored the effect of estimating the energy efficiency scores of DWTSs by allocating common sets of weights (CSW) within DEA (DEA-CSW). This approach was applied empirically on a sample of 146 DWTSs. Evaluated DWTSs had poor energetic performance based on both DEA-FSW and DEA-CSW estimates (low energy efficiency scores: 0.329 and 0.163, respectively). Even in the optimistic scenario, the average energy efficiency score was low (0.220), with potential electricity savings of 78 % by DWTPs when energy efficient. Unlike DEA-FSW, DEA-CSW allowed energy efficient DWTSs to be distinguished from the 146 facilities. Significant differences in the weights allocated to electricity and pollutants removed from raw water were reported for both approaches, and contributed to diverging energy efficiency scores. In conclusion, this study demonstrated the relevance of using suitable methods to generate comparable results for water companies, allowing the energy performance of DWTSs to be objectively evaluated for benchmarking purposes.

Сравнительный анализ энергоэффективности библиотек по технологии FinFET 7 нм

Сравнительный анализ энергоэффективности библиотек по технологии FinFET 7 нм

Benchmarking energy efficiency of water treatment plants: Effects of data variability

This study evaluates, for the first time, the energy efficiency of a sample of drinking water treatment plants (DWTPs) using the data envelopment analysis (DEA) tolerance method, which is based on the simulation of scenarios to integrate data variations. The integration of data uncertainty in energy efficiency estimation changes drastically results for approximately one-third of the DWTPs evaluated. The results showed that, even in the best-case scenario, most of the DWTPs evaluated are inefficient and may therefore, be able to reduce the energy used to treat raw water. From a policy perspective, the findings of this study reveal that omitting data variability in benchmarking would involve critical repercussions when efficiency scores are used by regulators to set water tariffs. Omitting the degree of data uncertainty is likely to result in biased conclusions; in the scenarios evaluated, the inclusion of this information altered the rankings of some energy-efficient DWTPs.

Revised energy efficiency design index parameters for inland cargo ships of Bangladesh

A generalized CO2 emission benchmark (energy efficiency design index) cannot be established for inland ships in a similar fashion like the one established by International Maritime Organization for seagoing vessels. Difference in environmental, geographical and economic conditions, for example, shallow and restricted water effects, different fuel quality (to reduce operational cost), reduction in speed, increase in engine power, reduction in carrying capacity and cargo availability, forbids the direct use of energy efficiency design index as formulated by International Maritime Organization. These conditions have been addressed at first in this research, and revision of some parameters is presented for calculating energy efficiency design index for inland cargo ships of Bangladesh. Moreover, the ship data stored at different organizations of Bangladesh deviate from the existing ship data. As a result, the collected data have undergone through several verification processes. Based on the verified real data, CO2 emission benchmark for inland cargo ships of Bangladesh was established. This benchmark is one of the world’s first CO2 emission benchmark for inland cargo shipping. This benchmark will force ship designers of Bangladesh to design more energy efficient ships that will consume less energy per ton-mile. It is interesting to note that the energy efficiency design index curve for inland cargo ships using revised energy efficiency design index parameters is below the energy efficiency design index curve using International Maritime Organization formulation. The prime reasons behind this fact have also been explained in this research.

Benchmarking energy efficiency in drinking water treatment plants: Quantification of potential savings

Within the urban water cycle, energy issues have been mostly studied for wastewater treatment plants, ignoring that drinking water treatment plants (DWTPs) also consume a significant quantity of energy. Knowing the real energy consumption of DWTPs is the starting point for any energy-saving initiative. This paper benchmarks the energy efficiency measures of a sample of real DWTPs using the data envelopment analysis methodology. Subsequently, whether these facilities are affected by economies of scale is investigated. This issue is essential for planning new DWTPs that minimize energy consumption. In the second stage of analysis, some structural and managerial variables affecting energy efficiency are explored. The results showed that most of the DWTPs analysed have a suitable size but can greatly improve in terms of saving energy. It was found that the age of the plants and the water company operating them significantly affects its energy efficiency. The approach applied in this paper is of great interest for water regulators and company managers since it enables them to learn from the best practices for reducing energy consumption in DWTPs and contributes to improving the sustainability and efficiency of the urban water cycle.

Benchmarking for Energy Efficiency on School Buildings Design: A Review

Increased energy demand end to the world grew by 39% between 1990 to 2008 and further increased by 40% between 2007 to 2030. Energy consumption in buildings has been identified to contribute up to 40% of the total world. Through the selection of methods and the right strategy will reduce the problem of increase energy in buildings. Based on the theory of energy efficiency developed it can be achieved through three main factors; a) building design; b) services design; and c) occupant behavior. This paper aims to discuss methods to benchmark energy efficiency in school buildings use energy-efficiency.

BEEST-EB Energy-Rating Method for Assessing the Energy Efficiency of Existing Buildings

AbstractCurrent sustainable building rating systems typically rate the energy performance of existing buildings by assessing how efficiently they consume energy, relative to similar buildings nationwide. In these rating systems, building type and external benchmark are the two notions typically employed. Alternatively, this study explores the notions of space type and internal benchmark and develops the benchmarking energy efficiency by space type for existing buildings (BEEST-EB) method to assess the energy efficiency of existing buildings. Specifically, core research concepts such as space type, standard operation settings, standard energy consumption, operational effectiveness index, and energy efficiency score are adopted in the BEEST-EB method. An existing building usually consists of different space types, each featuring similar operational characteristics and energy demands. It is therefore proposed that, for each space type, its standard operation settings should be defined and its standard energy...

Benchmarking Energy Efficiency by ′Space Type′: An Energy Management Tool for Individual Departments Within Universities

′Energy management′ is a challenging task for individual departments within universities, especially when several departments occupy the same facility. To assist individual departments in dealing with the complex energy management topics, this study aims to establish the Benchmarking Energy Efficiency by Space Type (BEEST) method. The BEEST is developed around the core concept of ′space type′. It is proposed that the spaces within a department be categorized into several ′space types′; and for each space type, its ′standard operation settings′ be defined to further estimate the ′standard energy consumption′ for the department, with energy prediction module such as eQuest, as its energy consumption benchmark. The energy efficiency index of a department and its facility can then be assessed by comparing its ′actual energy consumption′ against the estimated ′standard energy consumption. The problem areas can be identified and energy saving action plans recommended by conducting further analyses, such as energy analyses by space types, floors, and equipment types as well as sensitivity analyses of the energy reduction effects of various ′standard settings′. The Department of Architecture of the national university NTUST in Taiwan is used as a case to demonstrate how the BEEST functions as an effective energy management tool.

Benchmarking Energy Efficiency, Power Costs and Carbon Emissions on Heterogeneous Systems

With the advent of heterogeneous computing systems consisting of multi-core central processing units (CPUs) and many-core graphics processing units (GPUs), robust methods are needed to facilitate fair benchmark comparisons between different systems. In this paper, we present a benchmarking methodology for measuring a number of performance metrics for heterogeneous systems. Methods for comparing performance and energy efficiency are included. Consideration is given to further metrics, such as associated running costs and carbon emissions. We give a case study for these metrics applied to Bristol University Docking engine, a molecular mechanics-based docking application that has been ported to open computing language at the University of Bristol. Results are included for both AMD and NVIDIA GPUs, and for a highly optimized code on the latest x86 CPUs.

Benchmarking the energy efficiency of commercial buildings

Benchmarking energy-efficiency is an important tool to promote the efficient use of energy in commercial buildings. Benchmarking models are mostly constructed in a simple benchmark table (percentile table) of energy use, which is normalized with floor area and temperature. This paper describes a benchmarking process for energy efficiency by means of multiple regression analysis, where the relationship between energy-use intensities (EUIs) and the explanatory factors (e.g., operating hours) is developed. Using the resulting regression model, these EUIs are then normalized by removing the effect of deviance in the significant explanatory factors. The empirical cumulative distribution of the normalized EUI gives a benchmark table (or percentile table of EUI) for benchmarking an observed EUI. The advantage of this approach is that the benchmark table represents a normalized distribution of EUI, taking into account all the significant explanatory factors that affect energy consumption. An application to supermarkets is presented to illustrate the development and the use of the benchmarking method.

97/00723 Purchasing-power-parity (PPP) approach to energy-efficiency measurement: Implications for energy and environmental policy

Benchmarking energy-efficiency is an important tool to promote the efficient use of energy in commercial buildings. Benchmarking models are mostly constructed in a simple benchmark table (percentile table) of energy use, which is normalized with floor area and temperature. This paper describes a benchmarking process for energy efficiency by means of multiple regression analysis, where the relationship between energy-use intensities (EUIs) and the explanatory factors (e.g., operating hours) is developed. Using the resulting regression model, these EUIs are then normalized by removing the effect of deviance in the significant explanatory factors. The empirical cumulative distribution of the normalized EUI gives a benchmark table (or percentile table of EUI) for benchmarking an observed EUI. The advantage of this approach is that the benchmark table represents a normalized distribution of EUI, taking into account all the significant explanatory factors that affect energy consumption. An application to supermarkets is presented to illustrate the development and the use of the benchmarking method.