预兆前
发表于 2025-3-21 18:10:19
书目名称Applied Operations Research and Financial Modelling in Energy影响因子(影响力)<br> http://figure.impactfactor.cn/if/?ISSN=BK0160005<br><br> <br><br>书目名称Applied Operations Research and Financial Modelling in Energy影响因子(影响力)学科排名<br> http://figure.impactfactor.cn/ifr/?ISSN=BK0160005<br><br> <br><br>书目名称Applied Operations Research and Financial Modelling in Energy网络公开度<br> http://figure.impactfactor.cn/at/?ISSN=BK0160005<br><br> <br><br>书目名称Applied Operations Research and Financial Modelling in Energy网络公开度学科排名<br> http://figure.impactfactor.cn/atr/?ISSN=BK0160005<br><br> <br><br>书目名称Applied Operations Research and Financial Modelling in Energy被引频次<br> http://figure.impactfactor.cn/tc/?ISSN=BK0160005<br><br> <br><br>书目名称Applied Operations Research and Financial Modelling in Energy被引频次学科排名<br> http://figure.impactfactor.cn/tcr/?ISSN=BK0160005<br><br> <br><br>书目名称Applied Operations Research and Financial Modelling in Energy年度引用<br> http://figure.impactfactor.cn/ii/?ISSN=BK0160005<br><br> <br><br>书目名称Applied Operations Research and Financial Modelling in Energy年度引用学科排名<br> http://figure.impactfactor.cn/iir/?ISSN=BK0160005<br><br> <br><br>书目名称Applied Operations Research and Financial Modelling in Energy读者反馈<br> http://figure.impactfactor.cn/5y/?ISSN=BK0160005<br><br> <br><br>书目名称Applied Operations Research and Financial Modelling in Energy读者反馈学科排名<br> http://figure.impactfactor.cn/5yr/?ISSN=BK0160005<br><br> <br><br>
轻而薄
发表于 2025-3-21 21:16:26
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
Credence
发表于 2025-3-22 03:43:16
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
constellation
发表于 2025-3-22 06:31:25
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.
Salivary-Gland
发表于 2025-3-22 11:44:59
http://reply.papertrans.cn/17/1601/160005/160005_5.png
虚弱的神经
发表于 2025-3-22 14:46:29
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
CURT
发表于 2025-3-22 19:32:45
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
惊惶
发表于 2025-3-22 22:04:28
http://reply.papertrans.cn/17/1601/160005/160005_8.png
离开真充足
发表于 2025-3-23 04:13:11
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
gait-cycle
发表于 2025-3-23 09:30:07
,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.