標(biāo)題: Titlebook: Data Analytics for Renewable Energy Integration; Third ECML PKDD Work Wei Lee Woon,Zeyar Aung,Stuart Madnick Conference proceedings 2015 Sp [打印本頁] 作者: 貪吃的人 時間: 2025-3-21 18:12
書目名稱Data Analytics for Renewable Energy Integration影響因子(影響力)
書目名稱Data Analytics for Renewable Energy Integration影響因子(影響力)學(xué)科排名
書目名稱Data Analytics for Renewable Energy Integration網(wǎng)絡(luò)公開度
書目名稱Data Analytics for Renewable Energy Integration網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Data Analytics for Renewable Energy Integration被引頻次
書目名稱Data Analytics for Renewable Energy Integration被引頻次學(xué)科排名
書目名稱Data Analytics for Renewable Energy Integration年度引用
書目名稱Data Analytics for Renewable Energy Integration年度引用學(xué)科排名
書目名稱Data Analytics for Renewable Energy Integration讀者反饋
書目名稱Data Analytics for Renewable Energy Integration讀者反饋學(xué)科排名
作者: Coordinate 時間: 2025-3-21 21:16
What Form Did Class Consciousness Take?,ative work. We also illustrate that the argument theoretical model leads to reduce participants’ performance. Moreover, we conclude that there is no significant difference between narrative representation and graph representation in the participants’ performance to construct knowledge.作者: 分離 時間: 2025-3-22 04:11 作者: Seminar 時間: 2025-3-22 08:26 作者: gout109 時間: 2025-3-22 10:12 作者: expansive 時間: 2025-3-22 15:38 作者: expansive 時間: 2025-3-22 18:35 作者: Inertia 時間: 2025-3-22 22:57
Performance Analysis of Data Mining Techniques for Improving the Accuracy of Wind Power Forecast Comore critical especially for unstable energy sources such as wind. The focus of this work is the performance analysis of several alternative wind forecast combination models in comparison to the current forecast combination module of the wind power monitoring and forecast system of Turkey, developed作者: 地名表 時間: 2025-3-23 01:59 作者: 者變 時間: 2025-3-23 08:23
Classification Cascades of Overlapping Feature Ensembles for Energy Time Series Data,me series is relevant in various applications, e.g., in the task of learning meta-models of feasible schedules for flexible components in the energy domain. In this paper, we introduce a classification approach that employs a cascade of classifiers based on features of overlapping time series steps.作者: 睨視 時間: 2025-3-23 12:30 作者: 組成 時間: 2025-3-23 15:51
Predicting Hourly Energy Consumption. Can Regression Modeling Improve on an Autoregressive Baseline landscape, creating the so-called “smart grid”. Such a grid not only has to rely on predicting electricity production, but also its consumption. A growing body of literature exists on the topic of energy consumption and demand forecasting. Many contributions consist of presenting a methodology, and作者: 鑒賞家 時間: 2025-3-23 19:55
An OPTICS Clustering-Based Anomalous Data Filtering Algorithm for Condition Monitoring of Power Equdentify the Clustering Structure) clustering-based condition monitoring anomalous data filtering algorithm. Through the characteristic analysis of historical primary equipment condition monitoring data, an anomalous data filtering mechanism was built based on density clustering. The effectiveness of作者: 分散 時間: 2025-3-24 00:53 作者: cathartic 時間: 2025-3-24 02:52
The Paradoxical Class Politics in , the common “rule” of 6.5°C/km. Based on these findings, the air temperatures of all remote, mountainous spots can be calculated, and, therefore, the estimation of the energy needs of buildings has been provided, with a?high level of accuracy.作者: 弓箭 時間: 2025-3-24 09:08 作者: 脾氣暴躁的人 時間: 2025-3-24 12:13
Quantifying Energy Demand in Mountainous Areas, the common “rule” of 6.5°C/km. Based on these findings, the air temperatures of all remote, mountainous spots can be calculated, and, therefore, the estimation of the energy needs of buildings has been provided, with a?high level of accuracy.作者: coalition 時間: 2025-3-24 15:08
An OPTICS Clustering-Based Anomalous Data Filtering Algorithm for Condition Monitoring of Power Equown significant performance in identifying the features of anomalous data as well as filtering condition monitoring anomalous data. Noises were reduced effectively and the overall reliability of condition monitoring data was also improved.作者: 食道 時間: 2025-3-24 21:36
0302-9743 ble Energy Integration, DARE 2015, held in Porto, Portugal,in September 2015. .The 10 papers presented in this volume were carefully reviewed and selected forinclusion in this book..978-3-319-27429-4978-3-319-27430-0Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: modest 時間: 2025-3-25 01:50
https://doi.org/10.1007/978-3-030-73036-9 decision-making agent. The empirical performances in terms of Levelized Energy Cost (LEC) of the obtained agent are compared to the expert performances obtained in the case where the scenarios are known in advance. Preliminary results are promising.作者: epicardium 時間: 2025-3-25 06:20
Performance Analysis of Data Mining Techniques for Improving the Accuracy of Wind Power Forecast Co within the course of the RITM project. These accuracy improvement studies are within the scope of data mining approaches, Association Rule Mining (ARM), Distance-based approach, Decision Trees and k-Nearest Neighbor (k-NN) classification algorithms and comparative results of the algorithms are presented.作者: Vo2-Max 時間: 2025-3-25 10:26
Imitative Learning for Online Planning in Microgrids, decision-making agent. The empirical performances in terms of Levelized Energy Cost (LEC) of the obtained agent are compared to the expert performances obtained in the case where the scenarios are known in advance. Preliminary results are promising.作者: Expand 時間: 2025-3-25 14:54
Springer Monographs in Mathematics within the course of the RITM project. These accuracy improvement studies are within the scope of data mining approaches, Association Rule Mining (ARM), Distance-based approach, Decision Trees and k-Nearest Neighbor (k-NN) classification algorithms and comparative results of the algorithms are presented.作者: colloquial 時間: 2025-3-25 19:32 作者: 憂傷 時間: 2025-3-25 21:23 作者: 輕浮思想 時間: 2025-3-26 01:58
Michael Hillard,Richard McIntyregressive baseline and second, by evaluating the models in term of industrial applicability, in close collaboration with domain experts. It appears that the computationally costly regression models fail to significantly beat the baseline.作者: Concomitant 時間: 2025-3-26 04:52 作者: 柔美流暢 時間: 2025-3-26 11:57
Classification Cascades of Overlapping Feature Ensembles for Energy Time Series Data,the problem of combined heat and power plant operation schedules and an artificial similarly structured data set. We identify conditions under which the cascade approach shows better results than a classic One-Class-SVM.作者: Glucocorticoids 時間: 2025-3-26 13:42 作者: Radiculopathy 時間: 2025-3-26 20:20 作者: Cantankerous 時間: 2025-3-26 22:29 作者: Mitigate 時間: 2025-3-27 01:44
https://doi.org/10.1007/978-3-030-73036-9consumption are not known in advance. Using expert knowledge obtained from solving a family of linear programs, we build a learning set for training a decision-making agent. The empirical performances in terms of Levelized Energy Cost (LEC) of the obtained agent are compared to the expert performanc作者: 不能強迫我 時間: 2025-3-27 08:43 作者: indoctrinate 時間: 2025-3-27 13:22 作者: 我吃花盤旋 時間: 2025-3-27 15:51 作者: 群島 時間: 2025-3-27 19:04 作者: Hamper 時間: 2025-3-27 23:54
Henry Miller and the Embrace of Defilementme series is relevant in various applications, e.g., in the task of learning meta-models of feasible schedules for flexible components in the energy domain. In this paper, we introduce a classification approach that employs a cascade of classifiers based on features of overlapping time series steps.作者: 擴(kuò)張 時間: 2025-3-28 03:04 作者: galley 時間: 2025-3-28 06:16 作者: 拖債 時間: 2025-3-28 11:09 作者: ACRID 時間: 2025-3-28 16:31 作者: 多余 時間: 2025-3-28 21:59 作者: 不易燃 時間: 2025-3-29 02:42
0302-9743 ble Energy Integration, DARE 2015, held in Porto, Portugal,in September 2015. .The 10 papers presented in this volume were carefully reviewed and selected forinclusion in this book..978-3-319-27429-4978-3-319-27430-0Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 無價值 時間: 2025-3-29 07:05 作者: ALLAY 時間: 2025-3-29 10:45 作者: Incorporate 時間: 2025-3-29 14:47
978-3-319-27429-4Springer International Publishing Switzerland 2015
