Measuring Efficiency of Generating Electric Processes

Electric energy sources are the foundation for supporting for the industrialization and modernization; however, the processes of electricity generation increase CO2 emissions. This study integrates the Holt–Winters model in number cruncher statistical system (NCSS) to estimate the forecasting data and the undesirable model in data envelopment analysis (DEA) to calculate the efficiency of electricity production in 14 countries all over the world from past to future. The Holt–Winters model is utilized to estimate the future; then, the actual and forecasting data are applied into the undesirable model to compute the performance. From the principle of an undesirable model, the study determines the input and output factors as follows nonrenewable and renewable fuels (inputs), electricity generation (desirable output), and CO2 emissions (undesirable output). The empirical results exhibit efficient/inefficient terms over the period from 2011–2021 while converting these fuels into electricity energy and CO2 emissions. The efficiency reveals the environmental effect level from the electricity generation. The analysis scores recommend a direction for improving the inefficient terms via the principle of inputs and undesirable outputs excess and desirable outputs shortfalls in an undesirable model.

[1]  Peter R. Winters,et al.  Forecasting Sales by Exponentially Weighted Moving Averages , 1960 .

[2]  C. Lewis Industrial and business forecasting methods : a practical guide to exponential smoothing and curve fitting , 1982 .

[3]  R. Beverton,et al.  On the dynamics of exploited fish populations , 1993, Reviews in Fish Biology and Fisheries.

[4]  K. Axhausen,et al.  The simple exponential smoothing learning model , 1994 .

[5]  Kaoru Tone,et al.  A slacks-based measure of efficiency in data envelopment analysis , 1997, Eur. J. Oper. Res..

[6]  Lawrence M. Seiford,et al.  Modeling undesirable factors in efficiency evaluation , 2002, Eur. J. Oper. Res..

[7]  K. Tone Continuous Optimization A slacks-based measure of super-efficiency in data envelopment analysis , 2002 .

[8]  James W. Taylor Exponential smoothing with a damped multiplicative trend , 2003 .

[9]  Kaoru Tone,et al.  Dealing with Undesirable Outputs in DEA: A Slacks-based Measure (SBM) Approach , 2003 .

[10]  Godfrey Boyle,et al.  Energy Systems and Sustainability , 2003 .

[11]  J. W. Taylor,et al.  Short-term electricity demand forecasting using double seasonal exponential smoothing , 2003, J. Oper. Res. Soc..

[12]  S. Raghuvanshi,et al.  Carbon dioxide emissions from coal based power generation in India , 2006 .

[13]  Enriqueta Vercher,et al.  Bayesian forecasting with the Holt–Winters model , 2010, J. Oper. Res. Soc..

[14]  Hui Zuo,et al.  Environment, energy and sustainable economic growth , 2011 .

[15]  Tarek El-Shennawy,et al.  Reducing Carbon Dioxide Emissions from Electricity Sector Using Smart Electric Grid Applications , 2013 .

[16]  H. Kuo,et al.  Analysis of Farming Environmental Efficiency Using a DEA Model with Undesirable Outputs , 2014 .

[17]  A. S. Oluwalami,et al.  Review of Sustainable Energy and Electricity Generation from Non - Rewneable Energy Sources , 2015 .

[18]  Jiang Lin,et al.  Economic rebalancing and electricity demand in China , 2016 .

[19]  Johnny C. Ho,et al.  A computational analysis of the impact of correlation and data translation on DEA efficiency scores , 2016 .

[20]  River catchment rainfall series analysis using additive Holt–Winters method , 2016, Journal of Earth System Science.

[21]  M. Goto,et al.  Operational and Environmental Efficiencies of Japanese Electric Power Companies from 2003 to 2015: Influence of Market Reform and Fukushima Nuclear Power Accident , 2017 .

[22]  Ashraf A. Shahin Using Multiple Seasonal Holt-Winters Exponential Smoothing to Predict Cloud Resource Provisioning , 2017, ArXiv.

[23]  Air Pollution Caused by Coal-fired Power Plant in Middle Taiwan , 2017 .

[24]  Ema Utami,et al.  ANALYSIS OF MOVING AVERAGE AND HOLT-WINTERS OPTIMIZATION BY USING GOLDEN SECTION FOR RITASE FORECASTING , 2017 .

[25]  Berna Haktanirlar Ulutas,et al.  Efficiency analysis of cement manufacturing facilities in Turkey considering undesirable outputs , 2017 .

[26]  Data Envelopment Analysis with Fixed Inputs, Undesirable Outputs and Negative Data , 2017 .

[27]  R. Procter Cutting carbon emissions from electricity generation , 2017 .

[28]  Thomas A. Adams,et al.  Comparison of CO2 Capture Approaches for Fossil-Based Power Generation: Review and Meta-Study , 2017 .

[29]  Jinfeng Liu,et al.  Improving Flexibility and Energy Efficiency of Post-Combustion CO2 Capture Plants Using Economic Model Predictive Control , 2018, Processes.

[30]  T. Walmsley,et al.  Energy Ratio analysis and accounting for renewable and non-renewable electricity generation: A review , 2018, Renewable and Sustainable Energy Reviews.

[31]  Antonio Carlos de Francisco,et al.  Carbon Footprint of Electricity Generation in Brazil: An Analysis of the 2016–2026 Period , 2018, Energies.