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This paper seeks to describe a protocol to estimate annual community energy consumption baselines for single‐family detached homes in the Gainesville Regional Utility service area of Alachua County, Florida, USA. Further, it details methods using these baselines to make direct comparisons of individual households' energy consumption and evaluate the energy impacts of three prescriptive demand side management (DSM) programs.

To improve estimates of energy savings, the paper proposes using a “micro” scale multivariate regression methodology based on a census of utility and property appraiser household data.

Results suggest that traditional analysis approaches are likely to overestimate savings significantly and that the annual community baseline technique provides more consistent estimates of energy savings than most commonly used methods.

This type of analysis could provide a tool that utilities can use to more accurately and cost effectively measure DSM savings. This could result in reduced energy demand related to streamlined program setup and management.

The proposed methodology is unique in that it defines a new household‐level energy consumption baseline measure that we think is a more appropriate performance measure, uses a census of publicly available data for the population of interest, merging metered utility data with property appraiser data, and works upward to construct a simple model for evaluating household‐level energy consumption. The critical element that distinguishes our proposed energy performance measures is that they are calculated and interpreted using annual, population‐level, comparison‐group baselines that effectively normalize for community energy consumption patterns in any given year.

Energy-efficient retrofitting of existing buildings is an inexpensive way of reducing energy consumption and mitigating climate change impacts. The purpose of this study is to examine electricity savings and carbon dioxide (CO₂) emission reduction potentials of energy-efficient retrofit measures for surveyed two large shopping malls in India.

A techno-economic model was developed to estimate the electricity savings achieved due to energy-efficient retrofit measures in shopping malls that were surveyed in 2017. Alternative scenarios were constructed based on capital cost and cost of conserved energy (CCE) value for retrofit measures: cheapest replacement, best available technology and best value for money. The life-cycle electricity and CO₂ emission savings and payback period for end-use retrofit measures were evaluated.

The estimated average electricity savings were around 39–56% for various retrofit measures across all three scenarios while the average CO₂ emission reductions were around 50–125 kt-CO₂. Retrofits to light-emitting diode lights and air conditioners with inverter technology offered more life-cycle electricity savings. Paybacks for most lighting end-use measures were estimated to be within 1.5 years while for most space conditioning end-use measures were between 1 and 4 years.

The primary survey-based comprehensive research makes an exclusive contribution by estimating life-cycle electricity savings and CO₂ emission reductions for energy-efficient retrofit measures of lighting and space cooling end-use appliances for existing shopping malls. The present research methodology can also be deployed in other types of commercial buildings and in residential buildings to estimate electricity savings from energy-efficient retrofit measures.

This study aims to examine the asymmetric effects of energy efficiency on employment in India. Instead of relying on partial factor energy efficiency measures, this study uses a total factor energy efficiency (TFE) measure to estimate sector-specific energy efficiency for empirical investigation.

Multi-sectoral panel data for India from 2000 to 2014 are considered for empirical estimation. The sector-specific energy efficiency estimates (using the TFE measure) are estimated in the initial stage using the stochastic frontier approach (SFA). Then the asymmetric effect of energy efficiency on employment is investigated by using a non-linear panel autoregressive distributed lag model.

The estimates of energy efficiency display that there is not much significant change in the trend of average energy efficiency over the period. The negative and statistically significant value of the error-correction term confirms the existence of asymmetric cointegrating relationship between energy efficiency and employment in India. Moreover, in the empirical findings, the positive and negative shocks in energy efficiency provide a long-run asymmetric and short-run symmetric effect on employment in India.

Rather than depending on the absolute measure of energy efficiency (energy to output ratio), this study estimates the sector-specific energy efficiency for India using panel SFA, which provides a relative measure of energy efficiency. Moreover, to the best of the authors’ knowledge, it is the first empirical study investigating the asymmetric impact of energy efficiency on employment at an aggregate level in developing countries like India. By contrast, previous studies have either concentrated on the symmetric effect of energy efficiency on employment or primarily restricted to developed countries.

The purpose of the study is to estimate the potential of end‐user effect on energy conservation in office buildings. The study quantifies the energy conservation potential and estimates the current level of energy management in four banking organisations in the Nordic countries.

The multiple case study employs quantitative scenario analysis for estimating the energy conservation potential of office equipment and lighting, and uses a qualitative model for estimating the current level of end‐user energy management in the organisations.

The study found that noticeable potential for energy conservation exists in the end‐user energy consumption. The tested scenarios significantly decreased the yearly electricity consumption of office equipment, for some 70 per cent, from 21 kWh per m² to 6 kWh per m². The electricity consumption of lighting was also noticeably improved, for some 30 per cent, from 38 kWh per m² to 27 kWh per m², equalling a 29 per cent reduction there. Altogether, the identified savings equal an improvement of roughly 20 per cent in the overall electricity consumption of the organisations. At the same time, the evaluation of current energy management showed that the management practices implemented represent only a rather modest level of end‐user energy management.

The multiple case study focused on four Nordic countries and banking organisations only. As the banking sector tends to be more conservative than some other industries, the results are not necessary valid in all industries. The simulation tools used, especially for lighting, only give estimation of the “best case” type of situation for tested scenarios, in which it is assumed that all end‐users would start to behave according to the scenario.

This study implies clearly that end‐user‐energy‐management services are needed in FM. Although the user behaviour is more challenging to manage than pure technology, it can be quite profitable. For example, in the studied organisations, the savings found in end‐user energy would equal yearly profits of roughly 1.7 million €, i.e. some 20 per cent of the overall electricity budgets of the companies.

Most of the quantitative energy conservation studies in the offices have concentrated so far on new constructions and building system improvements. This study quantifies the potential influence of good facilities management with the end‐user activation on the energy efficiency of office buildings.

This study is concerned with dynamic effects when we use explicit FEM for sheet metal forming analysis. Since sheet metal forming is a quasi‐static process, time scaling and mass scaling techniques have been widely used in order to save computation time. However, to define suitable scale factor still requires several trials. In this study, an internal energy error estimate which indicates accumulated incremental errors due to dynamic effects is suggested. This estimate is applied to each subdomain to consider the locality of deformation. Draw bending problem is analyzed to show the effectiveness of the suggested method. An analytic solution is derived for simplified draw bending to compare with the computed results. The cause of stress error in this problem is investigated and the method to reduce the stress error is suggested. Finally, the suggested method is applied to S‐rail benchmark test.

Water heating is one of the major energy‐consuming operations in the lodging sector. The purpose of the current study is to estimate the energy consumed and emission associated with hot water usage, to predict the energy cost required under different hot water systems in hotels; and to create a model for the decision‐making criteria in selecting hot water systems.

A total of 24 hotels, which use heat pumps as their main water heating systems, were investigated. A tailor‐made model for estimating the energy requirement of the water heating system was employed. Comparative studies on the energy consumption and energy costs of various types of water heating systems, including heat pumps, diesel boilers, gas boilers, and electric boilers, were conducted. Moreover, an analytic hierarchy process was used to analyze hoteliers' and lenders' selection criteria for water heating facilities.

The energy output for water heating by heat pumps was estimated at 15 GW in the lodging sector. The use of heat pumps can achieve substantial energy savings and reduction of air pollutants when compared with the energy requirements under conventional boilers. The latter accounts for 13 percent of the hoteliers' total decision weight on choosing water‐heating systems. Whereas the air pollutants generated by gas‐fired boilers are remarkably lower than those emitted in the power plants due to the use of heat pumps. Both bankers and hoteliers consider seriously the energy saving potential of hot water supply by trucks.

Due to the small number of decision‐makers in hotels participating in the analytic hierarchy process, the result can only provide an indication of the overall picture of the selection criteria adopted by hoteliers.

The analysis provides hotel owners and managers with an objective and scientific investigation of the emission prediction and energy cost estimation based on the use of different hot water systems. Hotel operators and owners can use the analytical results as reference for making green purchasing decisions.

The current study, which is based on the operational experiences of existing hotels, is a collaborative work between hospitality industry practitioners and educators. It is also the first of its kind to indicate the emission impact of various types of hotel water heating systems and the perspectives of hoteliers and bankers on these systems.

The surface energy per unit area of material is known to be proportional to the thermal energy at the melting point of the material. The purpose of this paper is to employ the values of the melting points of metals to develop a model that estimates the average surface energies of metals. Average surface energy estimator (ASEE) was developed with the aid of computational intelligence technique on the platform of support vector regression (SVR) using the values of the melting point of the materials as the descriptor.

The development of ASEE which involves 12 data set was conducted by training and testing SVR model using test-set-cross-validation technique. The developed model (ASEE) was used to estimate average surface energies of 3d, 4d, 5d and other selected metals in the periodic table. The average surface energies obtained from ASEE are in good agreement with the experimental values and with the values from other theoretical models.

The accuracy of this developed model coupled with its adoption of descriptor that can be easily obtained makes it a viable alternative in circumventing the difficulty experienced in experimental determination of average surface energies of materials.

Modeling of ASEE has never been reported in the literature. Meanwhile, the use of ASEE will help circumvent the difficulties involved in the experimental determination of average surface energies of materials.

The purpose of this paper is to benchmark the energy use of Indian iron and steel industry. For this purpose, the authors have estimated a production frontier to know the best performing states. Further, the energy-saving targets are estimated to lie below the benchmark level for those states. Panel data for this purpose are extracted from the Annual Survey of Industry (an official database from the government of India) for 19 major steel-producing states over the period from 2004–2005 to 2013–2014.

The authors employed a radial and non-radial (slack-based measure) variant of the data envelopment analysis (DEA) to estimate the production frontier. Particularly, slack-based measures (SBMs) developed by Tone (2001) are used to get a more comprehensive measure of energy efficiency along with technical efficiency. Variable returns to scale technology is specified to accommodate market imperfection and heterogeneity across states. Four inputs (capital, labour, energy and material) and a single output are conceptualised for the production process to accommodate input substitution. The relative position of each state in terms of the level of energy efficiency is then identified.

The authors started by examining energy-output ratio. The average level of energy intensity shows declining trends over the period of time. States like Bihar, Jharkhand, Gujarat and Uttarakhand remain stagnant in the energy intensity level. SBM of energy efficiency shows an overall average energy saving potential of 8 per cent without reducing average output level. Considerable heterogeneity exists among states in terms of the energy efficiency scores. Further, the authors calculated scale efficiency (SE) which shows the overall average level of SE is 0.91; hence, the scale of operation is not optimal and needs to adjusted to enhance energy efficiency.

The authors demonstrate the empirical application of DEA with SBM to energy use performance. This is the first study that benchmarks Indian states in terms of the consumption of energy input to produce iron and steel by applying DEA.

The purpose of this paper is to examine significant steps taken by the Pennsylvania State University (Penn State) to account for both energy cost savings and greenhouse gas (GHG) emissions reduction goals through strategic investments in energy conservation measures (ECMs) in campus buildings. Through an analysis of multiple years of investment in facility upgrades across the university, the impacts of ECMs of various types are characterized by building type. The standards and criteria for ECMs investments are also evaluated with the goal to develop a predictive tool to support decision making pertaining to an annual investment in a portfolio of ECMs that will maintain a trajectory to achieve both financial return on investment as well as GHG reduction goals.

This study is comprised of three main parts: analyzing the energy costs saving and GHG emissions reduction contribution of various building types in which ECMs were conducted, analyzing costs saving and GHG emissions reduction contribution of each ECM while considering the average annual investments made in them and estimating the impact of upgrading Penn State’s steam plants from firing a mixture of coal and natural gas to natural gas only on the GHG emissions.

These analyses help identify which types of buildings and ECMs would have larger savings and emissions reduction contributions. A calculator is also created to enable forecasting of costs saving and GHG emissions reduction of investment distribution strategy among ECMs. This study demonstrates that the calculator based on data from previous years will benefit decision makers in more wisely configuring the investment portfolio.

This paper fulfills an identical need to couple energy efficiency strategies coupled with the environmental impacts associated with different fossil fuel energy sources.

This study aims to develop a prototype for measuring the carbon emissions arising from the utilization of electrical appliances in a typical Hong Kong family in Kowloon District.

To estimate carbon emission coefficients of energy consumption, the US building energy database books are referred to, which include home appliances’ energy end-use expenditure splits and energy end-use carbon splits. Due to differences in climate, geography and culture, the estimation equations are refined with assumptions and constraints based on the context of Hong Kong.

By calculating the amount of carbon emissions from different electrical appliances, including space cooling, water heating, lighting, refrigeration, wet cleaning and cooking, it is estimated that the carbon emissions from major home electrical appliances in Hong Kong’s residential building sector is 1,805,397 metric tons. According to the findings of this study, by adopting the energy-saving guidelines for space coolers, refrigerators, water heaters and washing machines, carbon emissions in Hong Kong’s residential building sector can be reduced by 155,443 metric tons, or by 8.6 per cent of the current emission.

Due to limited resources, the case study does not cover residential units in other districts of Hong Kong, including the Hong Kong Island and the New Territories. In addition, relevant institutional and financing costs of implementing the proposed practices such as increasing the air-conditioning temperature and upgrading the lighting system should be further considered. Future research will be extended to the wider territory of Hong Kong and to obtain more cases for analysis.

This paper provides a microscopic perspective on investigating the carbon emissions associated with energy consumption from major home electrical appliances in Hong Kong’s residential buildings. By unveiling the interaction between energy consumption and carbon emission, we formulate measures and strategies for implementing a cost reduction and carbon saving scheme of Hong Kong’s residential buildings, which is in line with government’s guidelines on green buildings.