scholarly journals Selection Methodology of Energy Consumption Model Based on Data Envelopment Analysis

2016 ◽  
Vol 11 (1) ◽  
pp. 5-12 ◽  
Author(s):  
Vladimir Nakhodov ◽  
Algirdas Baskys ◽  
Nils-Olav Skeie ◽  
Carlos F. Pfeiffer ◽  
Ivanko Dmytro
Keyword(s):  
Mathematical Model ◽  
Energy Efficiency ◽  
Energy Consumption ◽  
Data Envelopment Analysis ◽  
Data Envelopment ◽  
Monitoring Methods ◽  
Energy Consumption Model ◽  
Selection Methodology ◽  
The Mathematical Model ◽  
Selection Of

Abstract The energy efficiency monitoring methods in industry are based on statistical modeling of energy consumption. In the present paper, the widely used method of energy efficiency monitoring “Monitoring and Targeting systems” has been considered, highlighting one of the most important issues — selection of the proper mathematical model of energy consumption. The paper gives a list of different models that can be applied in the corresponding systems. The numbers of criteria that estimate certain characteristics of the mathematical model are represented. The traditional criteria of model adequacy and the “additional” criteria, which allow estimating the model characteristics more precisely, are proposed for choosing the mathematical model of energy consumption in “Monitoring and Targeting systems”. In order to provide the comparison of different models by several criteria simultaneously, an approach based on Data Envelopment Analysis is proposed. Such approach allows providing a more accurate and reliable energy efficiency monitoring.

Analisa Pengukuran Efisiensi Dengan Metode Data Envelopment Analysis (Dea) Di Heaven Store Surabaya Barat

2017 ◽  
Vol 1 (1) ◽  
pp. 44
Author(s):  
Satya Swesty Widiyana ◽  
Rus Indiyanto
Keyword(s):  
Mathematical Model ◽  
Data Envelopment Analysis ◽  
Customer Satisfaction ◽  
Customer Service ◽  
Data Envelopment ◽  
Input And Output ◽  
Using Data ◽  
Measurement Efficiency ◽  
The Mathematical Model

ABSTRACTThis study was taken from the problems in Heaven Store ranging from turnover does not reach the target, the different display products for each branch, and a just few reference customer visiting from problems in customer satisfaction. because the values of input and output obtained from each branch has a different values so demanding customers Heaven Store to correct weaknesses in the efficiency of customer service and satisfaction, then we tried to respond to the challenges of these improvements to the study "Analysis of Measurement Efficiency Services Methods Data envelopment analysis (DEA) In Heaven Store in West Surabaya "So in this study, researchers will assist the managementHeaven Store for measuring the level of efficiency that Heaven store along 5th branches can improve the quality of service by using data envelopment analysis (DEA), which is a methods that determine the level of efficiency similar organization where efficiency is not determined by the organization concerned. It is hoped this analysis will help the management to withdraw the customer so that the customer can buy the products that are sold in Heaven Store. After calculation of the mathematical model by referring to the calculation of the mathematical model DEA CRS, obtained the efficiency 0.8479688 on the fifth branch Heaven Store, then after an improvement in input and output according to the reference fixes the target model of DEA CRS, then the value of the relative efficiency DMU 5 can be increased from 0.8479688 (inefficient) to 1.000000 (efficient). Keywords: Data Envelopment Analysis, customer satisfaction, efficiency

scholarly journals Improving Energy Efficiency of Barley Production Using Joint Data Envelopment Analysis (DEA) and Life Cycle Assessment (LCA): Evaluation of Greenhouse Gas Emissions and Optimization Approach

2021 ◽  
Vol 13 (11) ◽  
pp. 6082
Author(s):  
Zahra Payandeh ◽  
Ahmad Jahanbakhshi ◽  
Tarahom Mesri-Gundoshmian ◽  
Sean Clark
Keyword(s):  
Energy Efficiency ◽  
Life Cycle Assessment ◽  
Life Cycle ◽  
Energy Consumption ◽  
Data Envelopment Analysis ◽  
Environmental Indicators ◽  
Optimization Approach ◽  
Data Envelopment ◽  
Aquatic Ecotoxicity ◽  
Environmental Emissions

Eco-efficiency has become a cornerstone in improving the environmental and economic performance of farms. The joint use of life cycle assessment (LCA) and data envelopment analysis (DEA), known as LCA + DEA methodology, is an expanding area of research in this quest. LCA estimates the environmental impacts of the products or services, while DEA evaluates their efficiency, providing targets and benchmarks for the inefficient ones. Because energy consumption and environmental quality are highly interdependent, we carried out a study to examine energy efficiency and environmental emissions associated with rain-fed barley farms in Kermanshah Province, Iran. Fifty-four rain-fed barley farms were randomly selected, and production data were collected using questionnaires and interviews. DEA and LCA were used to quantify and compare environmental indicators before and after efficiency improvements were applied to the farms. To accomplish this, efficient and inefficient farms were identified using DEA. Then environmental emissions were measured again after inefficient farms reached the efficiency limit through management improvements. The results showed that by managing resource use, both energy consumption and environmental emissions can be reduced without yield loss. The initial amount of energy consumed averaged 13,443 MJ/ha while that consumed in the optimal state was determined to be 12,509 MJ/h, resulting in a savings of 934 MJ/ha. Based on the results of DEA, reductions in nitrogen fertilizer, diesel fuel, and phosphate fertilizer offered the greatest possibilities for energy savings. Combining DEA and LCA showed that efficient resource management could reduce emissions important to abiotic depletion (fossil fuels), human toxicity, marine aquatic ecotoxicity, global warming (GWP100a), freshwater aquatic ecotoxicity, and terrestrial ecotoxicity. This study contributes toward systematically building knowledge about crop production with the joint use of LCA + DEA for eco-efficiency assessment.

Scheme Selection of Product Energy-Saving Design Based on Data Envelopment Analysis

2010 ◽  
Vol 44-47 ◽  
pp. 2080-2084
Author(s):  
Run Liang Dou ◽  
Ting Li
Keyword(s):  
Life Cycle ◽  
Energy Consumption ◽  
Data Envelopment Analysis ◽  
Energy Saving ◽  
Selection Method ◽  
Point Of View ◽  
Data Envelopment ◽  
Super Efficiency ◽  
Scheme Selection ◽  
Selection Of

A scheme selection method on the base of super efficiency DEA (DEA, data envelopment analysis) is put forward in order to design an energy-saving product. Firstly, a fishbone figure from the point of view of life cycle is established for the sake of analyzing energy-consumption factors systematically. Secondly, as the restriction of basic DEA model, super efficiency model of DEA is referred to choose the potentially energy-saving project. In view of the high difficulty in acquiring energy-consumption factors, this paper takes all the factors into account to select the scheme of energy-saving design. Finally an example of a blade at the stage of manufacture is introduced to illustrating this model. The achievements above solve the problem of the selection of energy-saving scheme, which provide significant references for product energy-saving design.

scholarly journals Energy Efficiency Outlook of New Zealand Dairy Farming Systems: An Application of Data Envelopment Analysis (DEA) Approach

Energies ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 251 ◽  
Author(s):  
Hafiz Muhammad Abrar Ilyas ◽  
Majeed Safa ◽  
Alison Bailey ◽  
Sara Rauf ◽  
Azeem Khan
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
New Zealand ◽  
Data Envelopment Analysis ◽  
Energy Saving ◽  
Farming Systems ◽  
Dairy Farms ◽  
Data Envelopment ◽  
Energy Saving Potential ◽  
Dairy Systems

This study evaluates energy efficiency of pastoral (PDFs) and barn (BDFs) dairy farming systems in New Zealand through application of data envelopment analysis (DEA) approach. Two models constant return to scale (CCR) and variable return to scale (BCC) of DEA were employed for determining the technical (TE), pure technical (PTE) and scale (SE) efficiencies of New Zealand pastoral and barn dairy systems. Further, benchmarking was also performed to separate efficient and inefficient dairy farms and energy saving potential was identified for both dairy systems based upon their optimal energy consumption. For this study, the energy inputs data were taken from 50 dairy farms (including PDFs and BDFs) across Canterbury, New Zealand. The results indicated that the average technical, pure technical and scale efficiencies of pastoral (PDFs) dairy systems were 0.84, 0.90, 0.93 and for barn (BDFs) systems were 0.78, 0.84, 0.92, respectively, showing that energy efficiency is slightly better in PDFs system than the BDFs. From the total number of dairy farms 40% and 48% were efficient based on the constant return to scale and variable return to scale models, respectively. Further, the energy saving potential for PDFs and BDFs dairy systems through optimal energy consumption were identified as 23% and 35%, respectively. Thus, energy auditing, use of renewable energy and precision agricultural technology were recommended for energy efficiency improvement in both dairy systems.

A data envelopment analysis (DEA) model for building energy benchmarking

2019 ◽  
Vol 17 (4) ◽  
pp. 747-768 ◽  
Author(s):  
Baabak Ashuri ◽  
Jun Wang ◽  
Mohsen Shahandashti ◽  
Minsoo Baek
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Data Envelopment Analysis ◽  
Building Energy ◽  
Scale Efficiency ◽  
Data Envelopment ◽  
Data Set ◽  
Content Type ◽  
Dea Model ◽  
Energy Benchmarking

Purpose Building energy benchmarking is required for adopting an energy certification scheme, promoting energy efficiency and reducing energy consumption. It demonstrates the current level of energy consumption, the value of potential energy improvement and the prospects for additional savings. This paper aims to create a new data envelopment analysis (DEA) model that overcomes the limitations of existing models for building energy benchmarking. Design/methodology/approach Data preparation: the findings of the literature search and subject matter experts’ inputs are used to construct the DEA model. Particularly, it is ensured that the included variables would not violate the fundamental assumption of DEA modeling, DEA convexity axiom. New DEA formulation: controllable and non-controllable variables, e.g. weather conditions, are differentiated in the new formulation. A new approach is used to identify outliers to avoid skewing the efficiency scores for the rest of the buildings under consideration. Efficiency analysis: three distinct efficiencies are computed and analyzed in benchmarking building energy: overall, pure technical, and scale efficiency. Findings The proposed DEA approach is successfully applied to a data set provided by a utility management and energy services company that is active in the multifamily housing industry. Building characteristics and energy consumption of 124 multifamily properties in 15 different states in the USA are found in the data set. Buildings in this data set are benchmarked using the new DEA energy benchmarking formulation. Building energy benchmarking is also conducted in a time series manner showing how a particular building performs across the period of 12 months compared with its peers. Originality/value The proposed research contributes to the body of knowledge in building energy benchmarking through developing a new outlier detection method to mitigate the impact of super-efficient and super-inefficient buildings on skewing the efficiency scores of the other buildings; avoiding ratio variables in the DEA formulation to adhere to the convexity assumption that existing DEA methods do not follow; and distinguishing between controllable and non-controllable variables in the DEA formulation. This research contributes to the state of practice through providing a new energy benchmarking tool for facility managers and building owners that strive to relatively rank the energy-efficiency of their properties and identify low-performing properties as investment targets to enhance energy efficiency.

Research on Jobshop Scheduling Problem with Energy Consumption

2011 ◽  
Vol 314-316 ◽  
pp. 2071-2075
Author(s):  
Jia Hai Wang ◽  
Wen Tao Gong
Keyword(s):  
Mathematical Model ◽  
Energy Consumption ◽  
Processing Time ◽  
Electric Energy ◽  
Low Carbon ◽  
Load Shifting ◽  
Electric Energy Consumption ◽  
Jobshop Scheduling ◽  
Time Division ◽  
The Mathematical Model

Discrete machine manufacture enterprises have to induce new low-carbon manufacturing model in order to solve a dilemma of mutual restraint between development and electric energy consumption. The paper presents an approach to solve JSP with the objective of minimizing the energy consumption by shortening the distance between electricity peak and valley according to theory of load shifting in electricity. The mathematical model is proposed for JSP with objective of minimizing the energy consumption and processing time of entire batch, then the idea of time division is introduced, and a solving method based on GA built-in eM-Plant is employed to verify the model and get satisfactory scheduling results.Discrete machine manufacture enterprises have to induce new low-carbon manufacturing model in order to solve a dilemma of mutual restraint between development and electric energy consumption. The paper presents an approach to solve JSP with the objective of minimizing the energy consumption by shortening the distance between electricity peak and valley according to theory of load shifting in electricity. The mathematical model is proposed for JSP with objective of minimizing the energy consumption and processing time of entire batch, then the idea of time division is introduced, and a solving method based on GA built-in eM-Plant is employed to verify the model and get satisfactory scheduling results.

scholarly journals Measuring the Productivity of Energy Consumption of Major Industries in China: A DEA-Based Method

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Xishuang Han ◽  
Xiaolong Xue ◽  
Jiaoju Ge ◽  
Hengqin Wu ◽  
Chang Su
Keyword(s):  
Decision Making ◽  
Energy Consumption ◽  
Data Envelopment Analysis ◽  
Malmquist Productivity Index ◽  
Productivity Index ◽  
Productivity Change ◽  
Data Envelopment ◽  
Policy Makers ◽  
Time Periods ◽  
Decision Making Units

Data envelopment analysis can be applied to measure the productivity of multiple input and output decision-making units. In addition, the data envelopment analysis-based Malmquist productivity index can be used as a tool for measuring the productivity change during different time periods. In this paper, we use an input-oriented model to measure the energy consumption productivity change from 1999 to 2008 of fourteen industry sectors in China as decision-making units. The results show that there are only four sectors that experienced effective energy consumption throughout the whole reference period. It also shows that these sectors always lie on the efficiency frontier of energy consumption as benchmarks. The other ten sectors experienced inefficiency in some two-year time periods and the productivity changes were not steady. The data envelopment analysis-based Malmquist productivity index provides a good way to measure the energy consumption and can give China's policy makers the information to promote their strategy of sustainable development.

scholarly journals Modelling Carbon Emissions Efficiency from UK Higher Education Institutions Using Data Envelopment Analysis

2019 ◽  
pp. 1-18
Author(s):  
Adefarati Oloruntoba ◽  
Japhet Tomiwa Oladipo
Keyword(s):  
Higher Education ◽  
Energy Efficiency ◽  
Data Envelopment Analysis ◽  
Carbon Emission ◽  
Scale Effects ◽  
Higher Education Institutions ◽  
High Energy ◽  
Data Envelopment ◽  
Emission Efficiency ◽  
The Uk

Aims: To correlate the energy and carbon emission efficiency relative to research income, gross internal area, and population for all the Higher Education Institutions (HEIs) in the UK and to assess the comparative carbon emission efficiency of HEIs relative to economic metrics. Study Design:  Analytical panel data study. Place and Duration of Study: This paper evaluates the energy efficiency of 131 HEIs in the UK subdivided into Russell and non-Russell groups from 2008 to 2015. Methodology: Data Envelopment Analysis (DEA) and Malmquist productivity indexes (MPI) are used for the efficiency calculations. Results: The empirical results indicate that UK HEIs have relatively high energy efficiency scores of 96.9% and 77.6% (CRS) and 98.5%, 86.3% (VRS) for Russell and non-Russell groups respectively. Conclusion: The evidence from this study reveals that HEIs are not significantly suffering from scale effects, hence, an increase in energy efficiency of these institutions is feasible with the present operating scale but would need to work on their technical improvements in energy use. Malmquist index analysis confirms the lack of substantial technological innovation, which impedes their energy efficiency and productivity gain. Findings show that pure technical efficiency accounts for the annual efficiency obtained in the DEA model, the technological progress in contrast is the source of their energy inefficiency.

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