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A statistical and predictive modeling study to analyze impact of seasons and covid-19 factors on household electricity consumption 分析季节和新冠肺炎因素对家庭用电量影响的统计和预测模型研究
Q3 Energy Pub Date : 2021-11-01 DOI: 10.30521/jes.933674
G. S. Ramnath, H. R.
Load is dynamic in nature and changing from aggregated load to disaggregated loads. Hence, need to analyze individual household’s energy consumption pattern. Many factors are contributing to household electricity consumption (HEC). The most influencing factor is the end user’s behavioral aspect. The calendar and seasonal factors are directly affecting user’s behavior activities. This paper consists of two aim, first aim is to validate the performance of traditional predictive models and second aim is to identify the best-fitted predictive model from five predictive models namely: Random Forest, Linear Regression, Support Vector Machine, Neural Network (NN) and Adaptive Boosting. The orange tool is used to simulate the predictive models. The JASP tool is used for statistical analysis of the dataset. From the predictive modeling study, the NN model is the most fitted model. The values of the performance matrix parameter like MSE, RMSE and MAE of the NN model is observed to be 0.558, 0.747 and 0.562 respectively. This study gives insights to researchers and utility companies about traditional predictive models that can predict the HEC under anomaly situations like Covid-19. This study also helps the researchers in using Orange and JASP tool to perform the statistical and predictive modeling. © 2021 Published by peer-reviewed open access scientific journal.
负荷是动态的,由聚集负荷向分解负荷变化。因此,有必要分析个体家庭的能源消费模式。许多因素影响着家庭用电量(HEC)。影响最大的因素是终端用户的行为方面。日历和季节因素直接影响用户的行为活动。本文包括两个目标,一是验证传统预测模型的性能,二是从随机森林、线性回归、支持向量机、神经网络和自适应增强五种预测模型中识别出最适合的预测模型。橙色工具用于模拟预测模型。JASP工具用于数据集的统计分析。从预测建模的研究来看,神经网络模型是最拟合的模型。观察到该NN模型的性能矩阵参数MSE、RMSE和MAE分别为0.558、0.747和0.562。这项研究为研究人员和公用事业公司提供了关于传统预测模型的见解,这些模型可以预测Covid-19等异常情况下的HEC。本研究还有助于研究人员使用Orange和JASP工具进行统计和预测建模。©2021由同行评审的开放获取科学期刊出版。
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引用次数: 3
Comparative analysis of dynamic pricing schemes in distributed energy management of residential users in smart grid 智能电网中住宅用户分布式能源管理动态定价方案的比较分析
Q3 Energy Pub Date : 2021-10-25 DOI: 10.30521/jes.973307
Monika Gaba, S. Chanana
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引用次数: 0
Bio-energy characteristics of black pine (Pinus nigra Arn.) hydrodistillation waste products 黑松(Pinus nigra Arn.)加氢蒸馏废弃物的生物能源特性
Q3 Energy Pub Date : 2021-10-25 DOI: 10.30521/jes.962474
H. Fidan, S. Stankov, N. Petkova, B. Bozadzhiev, M. Dimov, L. Lazarov, A. Simitchiev, A. Stoyanova
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引用次数: 1
R1234yf and R744 as alternatives to R134a at mobile air conditioners R1234yf和R744作为移动空调R134a的替代品
Q3 Energy Pub Date : 2021-10-25 DOI: 10.30521/jes.949753
C. Onan, Serkan Erdem
: R1234yf is a synthetic HFO refrigerant co-developed as a replacement refrigerant for R134a in automotive air conditioning applications. Thus, in this study, alternatives to the R134a refrigerant that were removed from the newly produced devices were examined. The performance of the cooling processes of R1234yf and R744 refrigerant gases was compared with that of R134a. A simulation model was first developed. This simulation model was validated with experimental results. Analysis was conducted for both cooling and heating modes. In the case of cooling, evaporation temperature was 5 °C–7.5 °C, condenser, or gas cooler outlet temperature was 35 °C– 50 °C and the cooling load was 10 kW. In heating mode, evaporation temperature was −4 °C–12 °C, condenser, or gas cooler outlet temperature was 45 °C–50 °C and the heating load was 13.5 kW. The results were analyzed in terms of the coefficient of performance (COP), compressor power consumption, and compressor discharge temperature. In terms of COP and compressor power consumption, R134a gave the best results in all cases. R1234yf gave the closest results to R134a. In terms of compressor discharge temperature, which affects the lifetime and lubrication quality of the compressor, R1234yf gave the lowest temperatures in all cases.
: R1234yf是一种合成HFO制冷剂,用于替代汽车空调应用中的R134a制冷剂。因此,在本研究中,研究了从新生产的设备中移除的R134a制冷剂的替代品。对R1234yf和R744制冷剂气体的冷却过程性能与R134a进行了比较。首先建立了仿真模型。通过实验验证了该仿真模型的正确性。对冷却和加热两种模式进行了分析。在冷却的情况下,蒸发温度为5°C - 7.5°C,冷凝器或气体冷却器出口温度为35°C - 50°C,冷却负荷为10 kW。加热模式下,蒸发温度为- 4℃~ 12℃,冷凝器或气体冷却器出口温度为45℃~ 50℃,热负荷为13.5 kW。从性能系数(COP)、压缩机功耗和压缩机排气温度三个方面对结果进行了分析。在COP和压缩机功耗方面,R134a在所有情况下都给出了最好的结果。R1234yf给出了最接近R134a的结果。在影响压缩机寿命和润滑质量的压缩机排气温度方面,R1234yf给出了所有情况下的最低温度。
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引用次数: 0
Predicting Cost of Farm-based Biogas Plants 预测农场沼气厂的成本
Q3 Energy Pub Date : 2021-10-18 DOI: 10.30521/jes.980467
Arash SAMİZADEH MASHHADİ, N. Saady, Carlos Bazan
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引用次数: 0
On Minimization of the Group Variability of Intermittent Renewable Generators 间歇式可再生能源发电机组群变率最小化研究
Q3 Energy Pub Date : 2021-10-15 DOI: 10.30521/jes.943813
D. Sabolic
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引用次数: 0
Techno-economic analysis of a transmission line between an island grid and a mainland grid 岛网与大陆网之间输电线路的技术经济分析
Q3 Energy Pub Date : 2021-09-30 DOI: 10.30521/jes.838784
B. Dinc, M. Fahrioglu, C. Batunlu
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引用次数: 1
Solar energy sector under the influence of Covid-19 pandemic: A critical review 新冠肺炎疫情影响下的太阳能产业述评
Q3 Energy Pub Date : 2021-09-23 DOI: 10.30521/jes.942691
H. Eroglu, Erdem Cüce
Different controversies arise when the world is dealing with the Covid-19 outbreak and fast solutions are produced in the field of health. However, the impact of Covid-19 on some critical sectors is perspicuous. One of the most important of those is the status of the solar industry, which is a favorite renewable and sustainable energy sector and the most sensitive part of global energy transformation. In this study, the solar energy sector has been examined in detail under the lens of Covid-19. The effect of the covid-19 outbreak on the sector has been tried to be measured and the steps that could be taken for a quick recovery have been proposed. In addition, the possible positive effects of the pandemic on the sector have been discussed within the perspective of the research. © Uludag Aricilik Dergisi. All rights reserved.
在世界应对新冠肺炎疫情的过程中,在卫生领域产生了快速解决方案,引发了不同的争议。然而,新冠肺炎疫情对一些关键部门的影响是显而易见的。其中最重要的是太阳能产业的地位,这是一个受欢迎的可再生和可持续能源部门,也是全球能源转型中最敏感的部分。在本研究中,在新冠肺炎的背景下对太阳能行业进行了详细研究。人们试图衡量covid-19疫情对该行业的影响,并提出了可以采取的快速恢复措施。此外,还从研究的角度讨论了大流行对该部门可能产生的积极影响。©Uludag Aricilik Dergisi。版权所有。
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引用次数: 8
Combined cycle gas turbine for combined heat and power production with energy storage by steam methane reforming 蒸汽甲烷重整储能热电联产联合循环燃气轮机
Q3 Energy Pub Date : 2021-09-04 DOI: 10.30521/jes.936064
I. Komarov, S. Osipov, O. Zlyvko, A. Vegera, V. Naumov
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引用次数: 0
Sensorless Current Prediction in Single Phase Inverter Circuits with Machine Learning Algorithms 基于机器学习算法的单相逆变电路无传感器电流预测
Q3 Energy Pub Date : 2021-09-04 DOI: 10.30521/jes.932581
Hüseyin Türe, S. Balci, K. Sabanci, Muhammet Fatih Aslan
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引用次数: 1
期刊
Journal of Energy Systems
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