Pub Date : 2024-02-06DOI: 10.1177/01445987231221877
K. Purna Prakash, Y. V. P. Kumar, Kongara Ravindranath, G. Pradeep Reddy, Mohammad Amir, Baseem Khan
Smart homes are at the forefront of sustainable living, utilizing advanced monitoring systems to optimize energy consumption. However, these systems frequently encounter issues with anomalous data such as missing data, redundant data, and outliers data which can undermine their effectiveness. In this paper, an artificial neural network (ANN)-based approach for data imputation is specifically designed to deal with the anomalies in smart home energy consumption datasets. Our research harnesses the power of ANNs to model intricate patterns within energy consumption data, enabling the accurate imputation of missing values while detecting and rectifying anomalous data. This approach not only enhances the completeness of the data but also augments its overall quality, ensuring more reliable results. To evaluate the effectiveness of our ANN-based imputation method, comprehensive experiments were conducted using real-world smart home energy consumption datasets. Our findings demonstrate that this approach outperforms traditional imputation techniques like mean imputation and median imputation in terms of accuracy. Furthermore, it showcases adaptability to diverse smart home scenarios and datasets, making it a versatile solution for improving data quality. In conclusion, this study introduces an advanced data imputation technique based on ANNs, tailor-made for addressing anomalies in smart home energy consumption data. Beyond merely filling data gaps, this approach elevates the dataset's reliability and completeness, thereby facilitating a more precise analysis of energy consumption and supporting informed decision-making in the context of smart homes and sustainable energy management. Ultimately, the proposed method has the potential to contribute considerably to the ongoing evolution of smart home technologies and energy conservation efforts.
智能家居走在可持续生活的前沿，利用先进的监控系统优化能源消耗。然而，这些系统经常会遇到异常数据问题，如缺失数据、冗余数据和异常值数据，这些都会影响系统的有效性。本文专门设计了一种基于人工神经网络（ANN）的数据估算方法，用于处理智能家居能耗数据集中的异常数据。我们的研究利用人工神经网络的强大功能，对能源消耗数据中错综复杂的模式进行建模，从而在检测和纠正异常数据的同时，准确估算缺失值。这种方法不仅增强了数据的完整性，还提高了数据的整体质量，确保得出更可靠的结果。为了评估我们基于 ANN 的估算方法的有效性，我们使用真实世界的智能家居能耗数据集进行了综合实验。我们的研究结果表明，这种方法在准确性方面优于传统的估算技术，如均值估算和中值估算。此外，它还展示了对各种智能家居场景和数据集的适应性，使其成为提高数据质量的通用解决方案。总之，本研究介绍了一种基于 ANN 的高级数据归因技术，该技术专为解决智能家居能耗数据中的异常情况而量身定制。这种方法不仅能填补数据空白，还能提高数据集的可靠性和完整性，从而促进对能耗进行更精确的分析，为智能家居和可持续能源管理方面的知情决策提供支持。最终，所提出的方法有望为智能家居技术和节能工作的不断发展做出巨大贡献。
{"title":"Artificial neural network-based data imputation for handling anomalous energy consumption readings in smart homes","authors":"K. Purna Prakash, Y. V. P. Kumar, Kongara Ravindranath, G. Pradeep Reddy, Mohammad Amir, Baseem Khan","doi":"10.1177/01445987231221877","DOIUrl":"https://doi.org/10.1177/01445987231221877","url":null,"abstract":"Smart homes are at the forefront of sustainable living, utilizing advanced monitoring systems to optimize energy consumption. However, these systems frequently encounter issues with anomalous data such as missing data, redundant data, and outliers data which can undermine their effectiveness. In this paper, an artificial neural network (ANN)-based approach for data imputation is specifically designed to deal with the anomalies in smart home energy consumption datasets. Our research harnesses the power of ANNs to model intricate patterns within energy consumption data, enabling the accurate imputation of missing values while detecting and rectifying anomalous data. This approach not only enhances the completeness of the data but also augments its overall quality, ensuring more reliable results. To evaluate the effectiveness of our ANN-based imputation method, comprehensive experiments were conducted using real-world smart home energy consumption datasets. Our findings demonstrate that this approach outperforms traditional imputation techniques like mean imputation and median imputation in terms of accuracy. Furthermore, it showcases adaptability to diverse smart home scenarios and datasets, making it a versatile solution for improving data quality. In conclusion, this study introduces an advanced data imputation technique based on ANNs, tailor-made for addressing anomalies in smart home energy consumption data. Beyond merely filling data gaps, this approach elevates the dataset's reliability and completeness, thereby facilitating a more precise analysis of energy consumption and supporting informed decision-making in the context of smart homes and sustainable energy management. Ultimately, the proposed method has the potential to contribute considerably to the ongoing evolution of smart home technologies and energy conservation efforts.","PeriodicalId":507696,"journal":{"name":"Energy Exploration & Exploitation","volume":"391 1-3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139860421","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-06DOI: 10.1177/01445987231221877
K. Purna Prakash, Y. V. P. Kumar, Kongara Ravindranath, G. Pradeep Reddy, Mohammad Amir, Baseem Khan
Smart homes are at the forefront of sustainable living, utilizing advanced monitoring systems to optimize energy consumption. However, these systems frequently encounter issues with anomalous data such as missing data, redundant data, and outliers data which can undermine their effectiveness. In this paper, an artificial neural network (ANN)-based approach for data imputation is specifically designed to deal with the anomalies in smart home energy consumption datasets. Our research harnesses the power of ANNs to model intricate patterns within energy consumption data, enabling the accurate imputation of missing values while detecting and rectifying anomalous data. This approach not only enhances the completeness of the data but also augments its overall quality, ensuring more reliable results. To evaluate the effectiveness of our ANN-based imputation method, comprehensive experiments were conducted using real-world smart home energy consumption datasets. Our findings demonstrate that this approach outperforms traditional imputation techniques like mean imputation and median imputation in terms of accuracy. Furthermore, it showcases adaptability to diverse smart home scenarios and datasets, making it a versatile solution for improving data quality. In conclusion, this study introduces an advanced data imputation technique based on ANNs, tailor-made for addressing anomalies in smart home energy consumption data. Beyond merely filling data gaps, this approach elevates the dataset's reliability and completeness, thereby facilitating a more precise analysis of energy consumption and supporting informed decision-making in the context of smart homes and sustainable energy management. Ultimately, the proposed method has the potential to contribute considerably to the ongoing evolution of smart home technologies and energy conservation efforts.
智能家居走在可持续生活的前沿，利用先进的监控系统优化能源消耗。然而，这些系统经常会遇到异常数据问题，如缺失数据、冗余数据和异常值数据，这些都会影响系统的有效性。本文专门设计了一种基于人工神经网络（ANN）的数据估算方法，用于处理智能家居能耗数据集中的异常数据。我们的研究利用人工神经网络的强大功能，对能源消耗数据中错综复杂的模式进行建模，从而在检测和纠正异常数据的同时，准确估算缺失值。这种方法不仅增强了数据的完整性，还提高了数据的整体质量，确保得出更可靠的结果。为了评估我们基于 ANN 的估算方法的有效性，我们使用真实世界的智能家居能耗数据集进行了综合实验。我们的研究结果表明，这种方法在准确性方面优于传统的估算技术，如均值估算和中值估算。此外，它还展示了对各种智能家居场景和数据集的适应性，使其成为提高数据质量的通用解决方案。总之，本研究介绍了一种基于 ANNs 的高级数据归因技术，该技术专为解决智能家居能耗数据中的异常情况而量身定制。这种方法不仅能填补数据空白，还能提高数据集的可靠性和完整性，从而促进对能耗进行更精确的分析，为智能家居和可持续能源管理方面的知情决策提供支持。最终，所提出的方法有望为智能家居技术和节能工作的不断发展做出巨大贡献。
{"title":"Artificial neural network-based data imputation for handling anomalous energy consumption readings in smart homes","authors":"K. Purna Prakash, Y. V. P. Kumar, Kongara Ravindranath, G. Pradeep Reddy, Mohammad Amir, Baseem Khan","doi":"10.1177/01445987231221877","DOIUrl":"https://doi.org/10.1177/01445987231221877","url":null,"abstract":"Smart homes are at the forefront of sustainable living, utilizing advanced monitoring systems to optimize energy consumption. However, these systems frequently encounter issues with anomalous data such as missing data, redundant data, and outliers data which can undermine their effectiveness. In this paper, an artificial neural network (ANN)-based approach for data imputation is specifically designed to deal with the anomalies in smart home energy consumption datasets. Our research harnesses the power of ANNs to model intricate patterns within energy consumption data, enabling the accurate imputation of missing values while detecting and rectifying anomalous data. This approach not only enhances the completeness of the data but also augments its overall quality, ensuring more reliable results. To evaluate the effectiveness of our ANN-based imputation method, comprehensive experiments were conducted using real-world smart home energy consumption datasets. Our findings demonstrate that this approach outperforms traditional imputation techniques like mean imputation and median imputation in terms of accuracy. Furthermore, it showcases adaptability to diverse smart home scenarios and datasets, making it a versatile solution for improving data quality. In conclusion, this study introduces an advanced data imputation technique based on ANNs, tailor-made for addressing anomalies in smart home energy consumption data. Beyond merely filling data gaps, this approach elevates the dataset's reliability and completeness, thereby facilitating a more precise analysis of energy consumption and supporting informed decision-making in the context of smart homes and sustainable energy management. Ultimately, the proposed method has the potential to contribute considerably to the ongoing evolution of smart home technologies and energy conservation efforts.","PeriodicalId":507696,"journal":{"name":"Energy Exploration & Exploitation","volume":"15 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139800679","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-06DOI: 10.1177/01445987241230593
Li Li, Jian Liu, Zhenzhu Xi, Ling Zhang
To clarify the activity patterns and source characteristics of coal mining–induced microseismicity, this study analyzed the spatial distribution characteristics of microseismic events in the Datong coal mining area based on records from the regional digital seismic network. We conducted a detailed characterization of the depth distribution characteristics of microseismic events using the double-difference localization method. Additionally, the source parameters, including corner frequency ( fc), source rupture radius ( r), seismic moment ( M0), source radiated energy ( Es), and stress drop (Δσ), were calculated for 136 mine-induced earthquakes with magnitudes ranging from ML1.3 to ML3.2. The results show that ML ≥ 2.0 mining-induced seismic events occur mainly within numerous microfractures in the Datong mining area. The depth of the seismic sources in the mining area is concentrated at 200∼500 m, with significant north–south differences and a close correlation with the mining depth. The displacement spectra of microseismic sources show agreement with the Brune source model [Formula: see text] attenuation pattern. As M0 gradually increases, r, Δσ, and Es show an increasing trend, while fc gradually decreases, exhibiting characteristics similar to those of tectonic earthquakes. Compared to tectonic earthquakes, coal mining-induced earthquakes have lower corner frequencies and stress drop levels mainly because mining activities alter the originally stable geological structure and stress state, leading to weakened rock strength, decreased elastic modulus, and shallower source depths. These factors contribute to the reduction in corner frequencies. As mining operations continue, microfracturing occurs in the coal and surrounding rock mass, intensifying the dynamic instability of the rock mass that was already under high stress conditions. This situation triggers larger-magnitude, mining-induced seismic events under lower stress conditions.
{"title":"Induced seismic activity and source parameter characteristics in the Datong coal mine, China","authors":"Li Li, Jian Liu, Zhenzhu Xi, Ling Zhang","doi":"10.1177/01445987241230593","DOIUrl":"https://doi.org/10.1177/01445987241230593","url":null,"abstract":"To clarify the activity patterns and source characteristics of coal mining–induced microseismicity, this study analyzed the spatial distribution characteristics of microseismic events in the Datong coal mining area based on records from the regional digital seismic network. We conducted a detailed characterization of the depth distribution characteristics of microseismic events using the double-difference localization method. Additionally, the source parameters, including corner frequency ( fc), source rupture radius ( r), seismic moment ( M0), source radiated energy ( Es), and stress drop (Δσ), were calculated for 136 mine-induced earthquakes with magnitudes ranging from ML1.3 to ML3.2. The results show that ML ≥ 2.0 mining-induced seismic events occur mainly within numerous microfractures in the Datong mining area. The depth of the seismic sources in the mining area is concentrated at 200∼500 m, with significant north–south differences and a close correlation with the mining depth. The displacement spectra of microseismic sources show agreement with the Brune source model [Formula: see text] attenuation pattern. As M0 gradually increases, r, Δσ, and Es show an increasing trend, while fc gradually decreases, exhibiting characteristics similar to those of tectonic earthquakes. Compared to tectonic earthquakes, coal mining-induced earthquakes have lower corner frequencies and stress drop levels mainly because mining activities alter the originally stable geological structure and stress state, leading to weakened rock strength, decreased elastic modulus, and shallower source depths. These factors contribute to the reduction in corner frequencies. As mining operations continue, microfracturing occurs in the coal and surrounding rock mass, intensifying the dynamic instability of the rock mass that was already under high stress conditions. This situation triggers larger-magnitude, mining-induced seismic events under lower stress conditions.","PeriodicalId":507696,"journal":{"name":"Energy Exploration & Exploitation","volume":"24 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139801069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-06DOI: 10.1177/01445987241230593
Li Li, Jian Liu, Zhenzhu Xi, Ling Zhang
To clarify the activity patterns and source characteristics of coal mining–induced microseismicity, this study analyzed the spatial distribution characteristics of microseismic events in the Datong coal mining area based on records from the regional digital seismic network. We conducted a detailed characterization of the depth distribution characteristics of microseismic events using the double-difference localization method. Additionally, the source parameters, including corner frequency ( fc), source rupture radius ( r), seismic moment ( M0), source radiated energy ( Es), and stress drop (Δσ), were calculated for 136 mine-induced earthquakes with magnitudes ranging from ML1.3 to ML3.2. The results show that ML ≥ 2.0 mining-induced seismic events occur mainly within numerous microfractures in the Datong mining area. The depth of the seismic sources in the mining area is concentrated at 200∼500 m, with significant north–south differences and a close correlation with the mining depth. The displacement spectra of microseismic sources show agreement with the Brune source model [Formula: see text] attenuation pattern. As M0 gradually increases, r, Δσ, and Es show an increasing trend, while fc gradually decreases, exhibiting characteristics similar to those of tectonic earthquakes. Compared to tectonic earthquakes, coal mining-induced earthquakes have lower corner frequencies and stress drop levels mainly because mining activities alter the originally stable geological structure and stress state, leading to weakened rock strength, decreased elastic modulus, and shallower source depths. These factors contribute to the reduction in corner frequencies. As mining operations continue, microfracturing occurs in the coal and surrounding rock mass, intensifying the dynamic instability of the rock mass that was already under high stress conditions. This situation triggers larger-magnitude, mining-induced seismic events under lower stress conditions.
{"title":"Induced seismic activity and source parameter characteristics in the Datong coal mine, China","authors":"Li Li, Jian Liu, Zhenzhu Xi, Ling Zhang","doi":"10.1177/01445987241230593","DOIUrl":"https://doi.org/10.1177/01445987241230593","url":null,"abstract":"To clarify the activity patterns and source characteristics of coal mining–induced microseismicity, this study analyzed the spatial distribution characteristics of microseismic events in the Datong coal mining area based on records from the regional digital seismic network. We conducted a detailed characterization of the depth distribution characteristics of microseismic events using the double-difference localization method. Additionally, the source parameters, including corner frequency ( fc), source rupture radius ( r), seismic moment ( M0), source radiated energy ( Es), and stress drop (Δσ), were calculated for 136 mine-induced earthquakes with magnitudes ranging from ML1.3 to ML3.2. The results show that ML ≥ 2.0 mining-induced seismic events occur mainly within numerous microfractures in the Datong mining area. The depth of the seismic sources in the mining area is concentrated at 200∼500 m, with significant north–south differences and a close correlation with the mining depth. The displacement spectra of microseismic sources show agreement with the Brune source model [Formula: see text] attenuation pattern. As M0 gradually increases, r, Δσ, and Es show an increasing trend, while fc gradually decreases, exhibiting characteristics similar to those of tectonic earthquakes. Compared to tectonic earthquakes, coal mining-induced earthquakes have lower corner frequencies and stress drop levels mainly because mining activities alter the originally stable geological structure and stress state, leading to weakened rock strength, decreased elastic modulus, and shallower source depths. These factors contribute to the reduction in corner frequencies. As mining operations continue, microfracturing occurs in the coal and surrounding rock mass, intensifying the dynamic instability of the rock mass that was already under high stress conditions. This situation triggers larger-magnitude, mining-induced seismic events under lower stress conditions.","PeriodicalId":507696,"journal":{"name":"Energy Exploration & Exploitation","volume":"3 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139861054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
As a result of the coal combustion process, high lithium enrichment in fly ash has been observed in some areas of China, which is considered to be a potential unconventional lithium resource. At present, most of the studies on lithium in fly ash focus on the leaching process, while there are fewer studies on the activation mechanism of roasting. In view of the above problems, the roasting activation mechanism of fly ash in the Pingshuo mining area (northern China) is investigated, and the leaching process of lithium is optimized. The activation pretreatment that destroys an inert composition of fly ash is necessary. In this study, fly ash and a mixed roasting agent (Na2CO3 and K2CO3 mass ratio of 3:1) were mixed at a mass ratio of 2:1 under 950 °C for 2 h. The results showed that the leaching rate of lithium increased by 70%. Direct acid leaching experiments show that 90% lithium in fly ash is related to insoluble aluminosilicate minerals. The mineralogical analysis of the calcined product shows that the stable aluminosilicate minerals in fly ash disappear and form nepheline KNa3(AlSiO4)3 which is soluble in acid, and the percentage of nepheline in the roasted product controls the leaching rate of lithium. The kinetic analysis of the acid leaching process of the roasting product shows that the lithium leaching process is mainly controlled by chemical reactions. Under the optimal leaching conditions, the leaching rate of lithium is 87.41%.
{"title":"Lithium activation pretreatment mechanism and leaching process from coal fly ash","authors":"Yanheng Li, Jianqi Man, Liyuan Cheng, Balaji Panchal","doi":"10.1177/01445987231219816","DOIUrl":"https://doi.org/10.1177/01445987231219816","url":null,"abstract":"As a result of the coal combustion process, high lithium enrichment in fly ash has been observed in some areas of China, which is considered to be a potential unconventional lithium resource. At present, most of the studies on lithium in fly ash focus on the leaching process, while there are fewer studies on the activation mechanism of roasting. In view of the above problems, the roasting activation mechanism of fly ash in the Pingshuo mining area (northern China) is investigated, and the leaching process of lithium is optimized. The activation pretreatment that destroys an inert composition of fly ash is necessary. In this study, fly ash and a mixed roasting agent (Na2CO3 and K2CO3 mass ratio of 3:1) were mixed at a mass ratio of 2:1 under 950 °C for 2 h. The results showed that the leaching rate of lithium increased by 70%. Direct acid leaching experiments show that 90% lithium in fly ash is related to insoluble aluminosilicate minerals. The mineralogical analysis of the calcined product shows that the stable aluminosilicate minerals in fly ash disappear and form nepheline KNa3(AlSiO4)3 which is soluble in acid, and the percentage of nepheline in the roasted product controls the leaching rate of lithium. The kinetic analysis of the acid leaching process of the roasting product shows that the lithium leaching process is mainly controlled by chemical reactions. Under the optimal leaching conditions, the leaching rate of lithium is 87.41%.","PeriodicalId":507696,"journal":{"name":"Energy Exploration & Exploitation","volume":"39 21","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139446161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-02DOI: 10.1177/01445987231224636
Yingguang Wang
In the current study, a new adaptive binned kernel density estimation method has been introduced. In the proposed new method, Fourier transforms have been utilized to accomplish the convolution rather than performing the convolution by hand. By utilizing the fast Fourier transform, direct and inverse Fourier transforms have been found in a relatively short amount of time when implementing the new method. Upon analyzing the computed results, it has been observed that the newly proposed adaptive binned kernel density estimation distribution curve exhibits a high level of smoothness in the tail region. Furthermore, it demonstrates a strong alignment with the histogram derived from the recorded ocean wave dataset obtained at the NDBC station 46053. These are the major advantages of the proposed new method comparing with other existing methods such as the parametric method, the ordinary KDE method, and Abramson's adaptive KDE method. The specific research gap identified in the field is that none of the existing methods can predict the sea state parameter probability distribution tails both accurately and efficiently, and the proposed new method has successfully addressed this research gap. Upon careful examination of the calculation results, it becomes evident that the projected 50-year extreme power-take-off heaving force value, derived using the newly proposed method, is 1989300N. This value significantly surpasses (by more than 9.5%) the forecasted value of 1816200N obtained through the application of the Rosenblatt-I-SORM contour method. The findings of this study suggest that the newly proposed adaptive binned kernel density estimation method exhibits robustness and demonstrates accurate forecasting capabilities for the 50-year extreme dynamic responses of wave energy converters.
本研究引入了一种新的自适应二进制核密度估计方法。在提出的新方法中，利用傅立叶变换完成卷积，而不是手工执行卷积。通过利用快速傅立叶变换，在实施新方法时，可以在相对较短的时间内找到正傅立叶变换和反傅立叶变换。对计算结果进行分析后发现，新提出的自适应二进制核密度估计分布曲线在尾部区域表现出较高的平滑度。此外，它与从北大西洋波浪中心（NDBC）46053 站获取的海洋波浪数据集中得到的直方图非常吻合。与参数法、普通 KDE 法和 Abramson 的自适应 KDE 法等其他现有方法相比，这些都是所提出的新方法的主要优势。该领域的具体研究空白在于，现有方法都无法既准确又高效地预测海况参数概率分布尾部，而所提出的新方法成功地解决了这一研究空白。在仔细研究计算结果后可以发现，使用新提出的方法得出的 50 年极端取力翻腾力预测值为 1989300N。这一数值大大超过（超过 9.5%）采用 Rosenblatt-I-SORM 等值线方法得出的 1816200N 预测值。这项研究结果表明，新提出的自适应分档核密度估计方法具有鲁棒性，对波浪能转换器的 50 年极端动态响应具有准确的预测能力。
{"title":"Robust adaptive analysis of extreme dynamic responses of wave energy converters","authors":"Yingguang Wang","doi":"10.1177/01445987231224636","DOIUrl":"https://doi.org/10.1177/01445987231224636","url":null,"abstract":"In the current study, a new adaptive binned kernel density estimation method has been introduced. In the proposed new method, Fourier transforms have been utilized to accomplish the convolution rather than performing the convolution by hand. By utilizing the fast Fourier transform, direct and inverse Fourier transforms have been found in a relatively short amount of time when implementing the new method. Upon analyzing the computed results, it has been observed that the newly proposed adaptive binned kernel density estimation distribution curve exhibits a high level of smoothness in the tail region. Furthermore, it demonstrates a strong alignment with the histogram derived from the recorded ocean wave dataset obtained at the NDBC station 46053. These are the major advantages of the proposed new method comparing with other existing methods such as the parametric method, the ordinary KDE method, and Abramson's adaptive KDE method. The specific research gap identified in the field is that none of the existing methods can predict the sea state parameter probability distribution tails both accurately and efficiently, and the proposed new method has successfully addressed this research gap. Upon careful examination of the calculation results, it becomes evident that the projected 50-year extreme power-take-off heaving force value, derived using the newly proposed method, is 1989300N. This value significantly surpasses (by more than 9.5%) the forecasted value of 1816200N obtained through the application of the Rosenblatt-I-SORM contour method. The findings of this study suggest that the newly proposed adaptive binned kernel density estimation method exhibits robustness and demonstrates accurate forecasting capabilities for the 50-year extreme dynamic responses of wave energy converters.","PeriodicalId":507696,"journal":{"name":"Energy Exploration & Exploitation","volume":"129 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139453389","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-25DOI: 10.1177/01445987231219765
Azra Senturk, Mustafa Ozcan
In this study, the energy performance analysis of a representative residential building located in Izmir, Turkey, was carried out, utilizing the DesignBuilder energy simulation program. Heating, ventilation, and air conditioning systems are modeled as detailed in Design Builder in order to consider parameters such as thermal properties of materials, duct layout and airflow dynamics of the system in detail in the analysis. The technical and economic analysis of transforming the building into a nearly zero energy building (NZEB) was performed for eight different retrofit scenarios, including passive and active energy efficiency measures. The most effective scenario, Scenario 8 (S8), reduced the building's annual net primary energy consumption by 96.08% to 7.45 kWh/m²/year. S8's annual CO2 emissions decreased by 100.07% compared with the reference scenario, resulting in −0.042 kg CO2/m²/year. The overall energy performance class of the building was determined using Turkey's national calculation program for the preparation of the energy identity certificate. The energy performance class of the reference building was determined as D, and the class of the building designed according to S8 as A. Investment evaluations were carried out for the retrofit scenarios, revealing that the investment cost for S8, having the lowest net primary energy consumption, amounted to USD 17,565.87. This finding establishes S8 as a more financially viable option in the short term. This study demonstrates the potential of NZEBs in reducing greenhouse gas emissions and achieving sustainable development goals.
{"title":"Nearly zero energy building design and optimization: A residential building transformation in Türkiye","authors":"Azra Senturk, Mustafa Ozcan","doi":"10.1177/01445987231219765","DOIUrl":"https://doi.org/10.1177/01445987231219765","url":null,"abstract":"In this study, the energy performance analysis of a representative residential building located in Izmir, Turkey, was carried out, utilizing the DesignBuilder energy simulation program. Heating, ventilation, and air conditioning systems are modeled as detailed in Design Builder in order to consider parameters such as thermal properties of materials, duct layout and airflow dynamics of the system in detail in the analysis. The technical and economic analysis of transforming the building into a nearly zero energy building (NZEB) was performed for eight different retrofit scenarios, including passive and active energy efficiency measures. The most effective scenario, Scenario 8 (S8), reduced the building's annual net primary energy consumption by 96.08% to 7.45 kWh/m²/year. S8's annual CO2 emissions decreased by 100.07% compared with the reference scenario, resulting in −0.042 kg CO2/m²/year. The overall energy performance class of the building was determined using Turkey's national calculation program for the preparation of the energy identity certificate. The energy performance class of the reference building was determined as D, and the class of the building designed according to S8 as A. Investment evaluations were carried out for the retrofit scenarios, revealing that the investment cost for S8, having the lowest net primary energy consumption, amounted to USD 17,565.87. This finding establishes S8 as a more financially viable option in the short term. This study demonstrates the potential of NZEBs in reducing greenhouse gas emissions and achieving sustainable development goals.","PeriodicalId":507696,"journal":{"name":"Energy Exploration & Exploitation","volume":"143 2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139159560","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-23DOI: 10.1177/01445987231210966
Ademide O. Mabadeje, Jose J. Salazar, Jesus Ochoa, Lean Garland, Michael J. Pyrcz
Identifying subsurface resource analogs from mature subsurface datasets is vital for developing new prospects due to often initial limited or absent information. Traditional methods for selecting these analogs, executed by domain experts, face challenges due to subsurface dataset's high complexity, noise, and dimensionality. This article aims to simplify this process by introducing an objective geostatistics-based machine learning workflow for analog selection. Our innovative workflow offers a systematic and unbiased solution, incorporating a new dissimilarity metric and scoring metrics, group consistency, and pairwise similarity scores. These elements effectively account for spatial and multivariate data relationships, measuring similarities within and between groups in reduced dimensional spaces. Our workflow begins with multidimensional scaling from inferential machine learning, utilizing our dissimilarity metric to obtain data representations in a reduced dimensional space. Following this, density-based spatial clustering of applications with noise identifies analog clusters and spatial analogs in the reduced space. Then, our scoring metrics assist in quantifying and identifying analogous data samples, while providing useful diagnostics for resource exploration. We demonstrate the efficacy of this workflow with wells from the Duvernay Formation and a test scenario incorporating various well types common in unconventional reservoirs, including infill, outlier, sparse, and centered wells. Through this application, we successfully identified and grouped analog clusters of test well samples based on geological properties and cumulative gas production, showcasing the potential of our proposed workflow for practical use in the field.
{"title":"A Machine Learning Workflow to Support the Identification of Subsurface Resource Analogs","authors":"Ademide O. Mabadeje, Jose J. Salazar, Jesus Ochoa, Lean Garland, Michael J. Pyrcz","doi":"10.1177/01445987231210966","DOIUrl":"https://doi.org/10.1177/01445987231210966","url":null,"abstract":"Identifying subsurface resource analogs from mature subsurface datasets is vital for developing new prospects due to often initial limited or absent information. Traditional methods for selecting these analogs, executed by domain experts, face challenges due to subsurface dataset's high complexity, noise, and dimensionality. This article aims to simplify this process by introducing an objective geostatistics-based machine learning workflow for analog selection. Our innovative workflow offers a systematic and unbiased solution, incorporating a new dissimilarity metric and scoring metrics, group consistency, and pairwise similarity scores. These elements effectively account for spatial and multivariate data relationships, measuring similarities within and between groups in reduced dimensional spaces. Our workflow begins with multidimensional scaling from inferential machine learning, utilizing our dissimilarity metric to obtain data representations in a reduced dimensional space. Following this, density-based spatial clustering of applications with noise identifies analog clusters and spatial analogs in the reduced space. Then, our scoring metrics assist in quantifying and identifying analogous data samples, while providing useful diagnostics for resource exploration. We demonstrate the efficacy of this workflow with wells from the Duvernay Formation and a test scenario incorporating various well types common in unconventional reservoirs, including infill, outlier, sparse, and centered wells. Through this application, we successfully identified and grouped analog clusters of test well samples based on geological properties and cumulative gas production, showcasing the potential of our proposed workflow for practical use in the field.","PeriodicalId":507696,"journal":{"name":"Energy Exploration & Exploitation","volume":"6 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139244270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-21DOI: 10.1177/01445987231215365
Houda El Khachine, M. H. Ouahabi, Driss Taoukil
Geothermal research advances earth-to-air heat exchanger (EAHE) technology, offering promising air conditioning solutions for all buildings. Our study targets improved energy efficiency for the EAHE system, focusing on cost-effective approaches to enhance its technical, economic, and environmental performance. The thermal performance and economic viability of the EAHE system hinge on the thermal characteristics of the surrounding soil. The EAHE model features a single pipe with dimensions of 0.5 meters in diameter, 1 centimeter in thickness, and 10 meters in length. These pipes are strategically placed at depths of 1 meter, 2 meters, 3 meters, and 4 meters below the ground's surface. To optimize heat exchange efficiency while minimizing pipe length, we propose using a secondary soil material with high thermal conductivity as a lining for the EAHE pipes. Our innovative approach carefully considers the economic and environmental aspects of various lining materials, resulting in optimal performance at a minimal cost. Extensive simulations and data analysis lead us to identify an ideal lining material, naturally available, environmentally friendly, and cost-effective, ensuring peak efficiency. Our investigation assesses the EAHE system's thermal performance for both summer cooling and winter heating, demonstrating its effectiveness across seasons. This research underscores the case for utilizing EAHE systems during winter and autumn for heating and during spring and summer for cooling. Our findings are supported by robust performance indicators, confirming the effectiveness of our approach.
{"title":"Improvement of earth-to-air heat exchanger performance by adding cost-efficient soil","authors":"Houda El Khachine, M. H. Ouahabi, Driss Taoukil","doi":"10.1177/01445987231215365","DOIUrl":"https://doi.org/10.1177/01445987231215365","url":null,"abstract":"Geothermal research advances earth-to-air heat exchanger (EAHE) technology, offering promising air conditioning solutions for all buildings. Our study targets improved energy efficiency for the EAHE system, focusing on cost-effective approaches to enhance its technical, economic, and environmental performance. The thermal performance and economic viability of the EAHE system hinge on the thermal characteristics of the surrounding soil. The EAHE model features a single pipe with dimensions of 0.5 meters in diameter, 1 centimeter in thickness, and 10 meters in length. These pipes are strategically placed at depths of 1 meter, 2 meters, 3 meters, and 4 meters below the ground's surface. To optimize heat exchange efficiency while minimizing pipe length, we propose using a secondary soil material with high thermal conductivity as a lining for the EAHE pipes. Our innovative approach carefully considers the economic and environmental aspects of various lining materials, resulting in optimal performance at a minimal cost. Extensive simulations and data analysis lead us to identify an ideal lining material, naturally available, environmentally friendly, and cost-effective, ensuring peak efficiency. Our investigation assesses the EAHE system's thermal performance for both summer cooling and winter heating, demonstrating its effectiveness across seasons. This research underscores the case for utilizing EAHE systems during winter and autumn for heating and during spring and summer for cooling. Our findings are supported by robust performance indicators, confirming the effectiveness of our approach.","PeriodicalId":507696,"journal":{"name":"Energy Exploration & Exploitation","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139253763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-19DOI: 10.1177/01445987231215445
D. Brkić
Serbia's energy sector is heavily reliant on Russian influence. On the other hand, Serbia's status as a candidate country for joining the European Union (EU) membership requires active working toward diversifying energy sources of supply. In the past decade, Serbia has secured a reduced price for natural gas through a bilateral agreement with Russia, addressing the shortfall in its domestic production. The former agreement priced Russian gas at US$270 per thousand cubic meters and expired in 2021. The new deal links gas prices to crude oil and ranges between US$310 and US$408, maintaining its competitive position as one of Europe's lowest import prices. Furthermore, alongside the new gas pipeline for Russian gas exports, the EU is funding the construction of a new interconnector, both with entry points from Bulgaria. Serbia also faces significant dependence on crude oil, and this reliance is compounded by the inability to import it from Russia any longer. Opposite, Serbia is usually self-sufficient in electricity production which still remains under state ownership. The domestic exploration and processing of oil and gas, as well as the sole underground gas storage facility in Serbia, have partial ownership by Russian Gazprom while the transportation of gas is under the full control of the Serbian government. This Communication about the energy situation in the Republic of Serbia put particular emphasis on the evolving political dynamics in the global energy market with a specific focus on the Russia–Ukraine war. The topic is also linked to the contentious status of the southern Serbian autonomous province, recognized as an independent state by the majority of Western nations but not by Serbia. It is feared that Serbia's energy dependence on Russia could have significant ramifications for its EU candidacy.
{"title":"Serbian Energy Sector in a Gap Between East and West","authors":"D. Brkić","doi":"10.1177/01445987231215445","DOIUrl":"https://doi.org/10.1177/01445987231215445","url":null,"abstract":"Serbia's energy sector is heavily reliant on Russian influence. On the other hand, Serbia's status as a candidate country for joining the European Union (EU) membership requires active working toward diversifying energy sources of supply. In the past decade, Serbia has secured a reduced price for natural gas through a bilateral agreement with Russia, addressing the shortfall in its domestic production. The former agreement priced Russian gas at US$270 per thousand cubic meters and expired in 2021. The new deal links gas prices to crude oil and ranges between US$310 and US$408, maintaining its competitive position as one of Europe's lowest import prices. Furthermore, alongside the new gas pipeline for Russian gas exports, the EU is funding the construction of a new interconnector, both with entry points from Bulgaria. Serbia also faces significant dependence on crude oil, and this reliance is compounded by the inability to import it from Russia any longer. Opposite, Serbia is usually self-sufficient in electricity production which still remains under state ownership. The domestic exploration and processing of oil and gas, as well as the sole underground gas storage facility in Serbia, have partial ownership by Russian Gazprom while the transportation of gas is under the full control of the Serbian government. This Communication about the energy situation in the Republic of Serbia put particular emphasis on the evolving political dynamics in the global energy market with a specific focus on the Russia–Ukraine war. The topic is also linked to the contentious status of the southern Serbian autonomous province, recognized as an independent state by the majority of Western nations but not by Serbia. It is feared that Serbia's energy dependence on Russia could have significant ramifications for its EU candidacy.","PeriodicalId":507696,"journal":{"name":"Energy Exploration & Exploitation","volume":"151 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139259950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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