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Titlebook: Applied Operations Research and Financial Modelling in Energy; Practical Applicatio André B. Dorsman,Kazim Baris Atici,Mehmet Baha Kar Book

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发表于 2025-3-21 18:10:19 | 显示全部楼层 |阅读模式
期刊全称Applied Operations Research and Financial Modelling in Energy
期刊简称Practical Applicatio
影响因子2023André B. Dorsman,Kazim Baris Atici,Mehmet Baha Kar
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发行地址Presents a collection of the latest work on operations research and financial modelling in energy.Sheds new light on the energy shift toward renewable energy.Presents case studies from several countri
图书封面Titlebook: Applied Operations Research and Financial Modelling in Energy; Practical Applicatio André B. Dorsman,Kazim Baris Atici,Mehmet Baha Kar Book
影响因子.This book on Applied Operations Research and Financial Modelling in Energy (AORFME) presents several applications of operations research (OR) and financial modelling. The contributions by a group of OR and Finance researchers focus on a variety of energy decisions, presenting a quantitative perspective, and providing policy implications of the proposed or applied methodologies. .The content is divided into three main parts: Applied OR I: Optimization Approaches, Applied OR II: Forecasting Approaches and Financial Modelling: Impacts of Energy Policies and Developments in Energy Markets. The book appeals to scholars in economics, finance and operations research, and to practitioners working in the energy sector.. This is the eighth volume in a series of books on energy organized by the Centre for Energy and Value Issues (CEVI). For this volume, CEVI collaborated with Hacettepe University’s Energy Markets Research and Application Center. The previous volumes in the series are: Financial Aspects in Energy (2011), Energy Economics and Financial Markets (2012), Perspectives on Energy Risk (2014), Energy Technology and Valuation Issues (2015), Energy and Finance (2016), Energy Economy, F
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发表于 2025-3-21 21:16:26 | 显示全部楼层
Book 2021s Energy Markets Research and Application Center. The previous volumes in the series are: Financial Aspects in Energy (2011), Energy Economics and Financial Markets (2012), Perspectives on Energy Risk (2014), Energy Technology and Valuation Issues (2015), Energy and Finance (2016), Energy Economy, F
发表于 2025-3-22 03:43:16 | 显示全部楼层
Optimization Methods on Electricity Generation and Transmission Expansion Planning Problem,en used to handle reliability and uncertainty of renewable resources and demand. Integration of power system with natural gas network leads to integration to complex energy planning problem due to non-linearity of natural gas network. In order to provide a practical pathway to carry out an energy pl
发表于 2025-3-22 06:31:25 | 显示全部楼层
Demand-Driven Electricity Supply Options of Electric Vehicles: Modelling, Simulation, and Managemenation varying the number and type of chargers. We also report the energy consumption and utilization of chargers and introduce different charging policies (with/without valet service). We also discuss different pricing models and remaining open problems on how to best design public CSs.
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发表于 2025-3-22 14:46:29 | 显示全部楼层
Measuring Efficiency and Productivity Change in the Turkish Electricity Distribution Sector, decreased slightly in the analysis period. While reaching their efficiency scores, the EDCs assigned the majority of weights to transformer capacity as input and number of employees as output. Additionally, our findings assert that the factors of energy loss and commercial and industrial electricit
发表于 2025-3-22 19:32:45 | 显示全部楼层
Price and Volatility Forecasting in Electricity with Support Vector Regression and Random Forest,ormance metrics of the SVR model are compared with those of naive and RF estimations. Furthermore, the sensitivity of the proposed model to feature reduction is also discussed. Overall, the results reveal SVR as an effective tool for electricity price forecasting in the Turkish electricity market, w
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发表于 2025-3-23 04:13:11 | 显示全部楼层
Comparing the Renewable Energy Technologies via Forecasting Approaches,d polynomial regression. This study evaluates deep learning models and statistical models. It is quite important to compare and evaluate renewable energy prediction models. We evaluate the forecasting models using evaluation metrics. The models are compared with Mean Absolute Error (MAE) and Mean Sq
发表于 2025-3-23 09:30:07 | 显示全部楼层
,Business Cycles and  Energy Real Options Valuation,(GBM) suggests that there are significant differences across these models. The results indicate that ABM investors are more likely to invest within a specified period. Numerical analysis also points that macroeconomic risks are important for investors.
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