標(biāo)題: Titlebook: Data Analytics for Renewable Energy Integration; Second ECML PKDD Wor Wei Lee Woon,Zeyar Aung,Stuart Madnick Conference proceedings 2014 Sp [打印本頁] 作者: GERM 時(shí)間: 2025-3-21 18:18
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: overbearing 時(shí)間: 2025-3-21 22:14
The Research on Vulnerability Analysis in OpenADR for Smart Grid,tracted the violations of rules such as OBJ01-J that means the scope of declaring member variables which should be obeyed in Object-Oriented Programming and IDS00-J that means the validation for input data which should be obeyed in Web environment. By eliminating the weaknesses we could enhance the security of Smart Grid communications.作者: 關(guān)心 時(shí)間: 2025-3-22 02:07 作者: 真實(shí)的你 時(shí)間: 2025-3-22 07:01 作者: 對(duì)待 時(shí)間: 2025-3-22 11:53
https://doi.org/10.1057/9780230616493gularities and patterns and the correlation between operating different devices. Subsequently, we show the existence of detectable time and energy flexibility in device operations. Finally, we provide various results providing a foundation for load- and flexibility-detection and -prediction at the device level.作者: nerve-sparing 時(shí)間: 2025-3-22 14:14 作者: nerve-sparing 時(shí)間: 2025-3-22 19:53
Towards Flexibility Detection in Device-Level Energy Consumption,gularities and patterns and the correlation between operating different devices. Subsequently, we show the existence of detectable time and energy flexibility in device operations. Finally, we provide various results providing a foundation for load- and flexibility-detection and -prediction at the device level.作者: Palatial 時(shí)間: 2025-3-22 22:24
Systematical Evaluation of Solar Energy Supply Forecasts,y, we describe our idea of a standardized forecasting process and the typical parameters possibly influencing the selection of a specific model. We discuss model combination as an optimization option and evaluate this approach comparing different statistical algorithms against flexible hybrid models in a case study.作者: 有權(quán)威 時(shí)間: 2025-3-23 04:13 作者: Gerontology 時(shí)間: 2025-3-23 06:51
https://doi.org/10.1007/978-3-319-50400-1refine these day-ahead forecasts to obtain same-day hourly PV production updates that for a given hour . use PV energy readings up?to that hour to derive updated PV forecasts for hours .. While simple from a Machine Learning point of view, these methods yield encouraging first results and also suggest ways to further improve them.作者: 可忽略 時(shí)間: 2025-3-23 12:30
,: A Lost Novel of Women’s Emancipationtracted the violations of rules such as OBJ01-J that means the scope of declaring member variables which should be obeyed in Object-Oriented Programming and IDS00-J that means the validation for input data which should be obeyed in Web environment. By eliminating the weaknesses we could enhance the security of Smart Grid communications.作者: 進(jìn)取心 時(shí)間: 2025-3-23 16:15
Claims for Secession and Federalismlated research questions are discussed. The different modules of WindML reach from standard machine learning algorithms to advanced techniques for handling missing data and monitoring high-dimensional time series.作者: KIN 時(shí)間: 2025-3-23 19:13 作者: prediabetes 時(shí)間: 2025-3-23 22:55
Towards Flexibility Detection in Device-Level Energy Consumption,d requires demand management by flexibility in the consumption. In this paper, we perform a state-of-the-art analysis on the flexibility and operation patterns of the devices in a set of real households. We propose a number of specific pre-processing steps such as operation stage segmentation, and a作者: Popcorn 時(shí)間: 2025-3-24 02:36
Machine Learning Prediction of Large Area Photovoltaic Energy Production,tion on Spain using as inputs Numerical Weather Prediction forecasts of global horizontal radiation and total cloud cover. We then introduce an empirical “clear sky” PV energy curve that we use to disaggregate these predictions into hourly day-ahead PV forecasts. Finally, we use Ridge Regression to 作者: 愛哭 時(shí)間: 2025-3-24 09:56
The Research on Vulnerability Analysis in OpenADR for Smart Grid,inuously despite its limited capacity. The demand reduction in Smart Grid can be achieved through DR (Demand Response) which reduces demand for electric power. In this paper, we analyzed the weaknesses of open source of Open ADR, protocol for Smart Grid DR, using CERT Java secure coding rules. We ex作者: Commission 時(shí)間: 2025-3-24 14:10 作者: 召集 時(shí)間: 2025-3-24 18:10 作者: cipher 時(shí)間: 2025-3-24 21:09
Machine Learning Techniques for Supporting Renewable Energy Generation and Integration: A Survey,ess energy from sun, wind, geothermal and many other renewable sources. Because of the negative effects on the environment and the economy, conventional energy sources like natural gas, crude oil and coal are coming under political and economic pressure. Thus, they require a better mix of energy sou作者: hardheaded 時(shí)間: 2025-3-25 00:35
A Framework for Data Mining in Wind Power Time Series,seen as large sensor system that screens the wind energy at a high temporal and spatial resolution. The resulting databases consist of huge amounts of wind energy time series data that can be used for prediction, controlling, and planning purposes. In this work, we describe WindML, a Python-based fr作者: MAUVE 時(shí)間: 2025-3-25 06:35
Systematical Evaluation of Solar Energy Supply Forecasts,nd supply. To allow for a better integration of solar energy supply into the power grids, a lot of research was dedicated to the development of precise forecasting approaches. However, there is still no straightforward and easy-to-use recommendation for a standardized forecasting strategy. In this p作者: 打包 時(shí)間: 2025-3-25 10:35 作者: configuration 時(shí)間: 2025-3-25 12:26 作者: 可以任性 時(shí)間: 2025-3-25 19:00 作者: 得罪 時(shí)間: 2025-3-25 22:45 作者: 謙虛的人 時(shí)間: 2025-3-26 03:28 作者: 白楊 時(shí)間: 2025-3-26 04:58 作者: ANNUL 時(shí)間: 2025-3-26 11:37 作者: hereditary 時(shí)間: 2025-3-26 15:53 作者: 滑稽 時(shí)間: 2025-3-26 17:03 作者: Interregnum 時(shí)間: 2025-3-26 22:51
Claims for Secession and Federalismseen as large sensor system that screens the wind energy at a high temporal and spatial resolution. The resulting databases consist of huge amounts of wind energy time series data that can be used for prediction, controlling, and planning purposes. In this work, we describe WindML, a Python-based fr作者: subordinate 時(shí)間: 2025-3-27 04:19 作者: 手工藝品 時(shí)間: 2025-3-27 05:58
Cyclic-guanylate-specific phosphodiesterase, renewable energy generation dominates and consideration tends to center on finding optimal combinations of different energy sources and generation technologies. In this context, it is very important that decision makers, investors and other stakeholders are able to keep up?to date with the latest d作者: 侵略 時(shí)間: 2025-3-27 12:34 作者: Exterior 時(shí)間: 2025-3-27 15:36
Wei Lee Woon,Zeyar Aung,Stuart MadnickIncludes supplementary material: 作者: PSA-velocity 時(shí)間: 2025-3-27 21:07
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/d/image/262703.jpg作者: AGONY 時(shí)間: 2025-3-28 01:57 作者: Heretical 時(shí)間: 2025-3-28 03:13
https://doi.org/10.1007/978-3-319-13290-7Artificial Intelligence; Data Analytics; Data Mining; Electrical Engineering; Energy Distribution; Fault 作者: Microgram 時(shí)間: 2025-3-28 07:52
978-3-319-13289-1Springer International Publishing Switzerland 2014作者: 金桌活畫面 時(shí)間: 2025-3-28 10:32
Data Analytics for Renewable Energy Integration978-3-319-13290-7Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: detach 時(shí)間: 2025-3-28 16:10 作者: 投射 時(shí)間: 2025-3-28 20:07 作者: Demonstrate 時(shí)間: 2025-3-29 02:49
Forecasting and Visualization of Renewable Energy Technologies Using Keyword Taxonomies,tly, we are particularly interested in the detection of technologies that are in the . phase, characterized by rapid increases in the number of relevant publications. Secondly, there is a strong emphasis on visualization rather than just the generation of ranked lists of the various technologies. Th作者: MEN 時(shí)間: 2025-3-29 06:26 作者: 柔聲地說 時(shí)間: 2025-3-29 10:37 作者: 態(tài)度暖昧 時(shí)間: 2025-3-29 14:22 作者: GIBE 時(shí)間: 2025-3-29 17:13
