標(biāo)題: Titlebook: Data Analytics for Renewable Energy Integration. Technologies, Systems and Society; 6th ECML PKDD Worksh Wei Lee Woon,Zeyar Aung,Stuart Mad [打印本頁] 作者: hearing-aid 時(shí)間: 2025-3-21 20:00
書目名稱Data Analytics for Renewable Energy Integration. Technologies, Systems and Society影響因子(影響力)
書目名稱Data Analytics for Renewable Energy Integration. Technologies, Systems and Society影響因子(影響力)學(xué)科排名
書目名稱Data Analytics for Renewable Energy Integration. Technologies, Systems and Society網(wǎng)絡(luò)公開度
書目名稱Data Analytics for Renewable Energy Integration. Technologies, Systems and Society網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Data Analytics for Renewable Energy Integration. Technologies, Systems and Society被引頻次
書目名稱Data Analytics for Renewable Energy Integration. Technologies, Systems and Society被引頻次學(xué)科排名
書目名稱Data Analytics for Renewable Energy Integration. Technologies, Systems and Society年度引用
書目名稱Data Analytics for Renewable Energy Integration. Technologies, Systems and Society年度引用學(xué)科排名
書目名稱Data Analytics for Renewable Energy Integration. Technologies, Systems and Society讀者反饋
書目名稱Data Analytics for Renewable Energy Integration. Technologies, Systems and Society讀者反饋學(xué)科排名
作者: 同時(shí)發(fā)生 時(shí)間: 2025-3-21 23:29
https://doi.org/10.1007/978-3-031-45948-1eraging mathematical optimization. Based on electricity consumption and location of the household, the algorithm finds PV module design parameters using Covariance Matrix Adaptation Evolution Strategy (hereafter CMA-ES). According to these computed design parameters, the algorithm finds the most sim作者: achlorhydria 時(shí)間: 2025-3-22 01:50 作者: Bumptious 時(shí)間: 2025-3-22 08:20 作者: ABHOR 時(shí)間: 2025-3-22 10:47 作者: indicate 時(shí)間: 2025-3-22 14:22
Social Work and Advanced Marginalitys and opportunities in power flow optimization. This promises reduced power generation costs through better integration of renewable energy generators into the Smart Grid. Unfortunately, renewable generators are fundamentally variable and uncertain. This uncertainty motivates our study of probabilis作者: indicate 時(shí)間: 2025-3-22 20:19
https://doi.org/10.1007/978-3-030-16222-1of time. With our method, we demonstrate a process of utilizing large-scale satellite images to classify a wave height with a continuous regressive output using a corresponding input for close shore sea. We generated and trained a convolutional neural network model that achieved an average loss of 0作者: 認(rèn)為 時(shí)間: 2025-3-22 22:15
Class, Individualization and Late Modernityorld’s primary energy consumption. Building Performance Simulation (BPS) can be used to model the relationship between building characteristics and energy consumption and to facilitate optimization efforts. However, BPS is computationally intensive and only a limited set of building configurations c作者: 恩惠 時(shí)間: 2025-3-23 04:11 作者: VALID 時(shí)間: 2025-3-23 07:38
Class, Surplus, and the Division of Labour In order to help achieve this balance in the grid, the renewable energy resources such as wind and stream-flow should be forecast at high accuracies on the generation side, and similarly, electricity consumption should be forecast using a high-performance system. In this paper, we deal with short-t作者: 散開 時(shí)間: 2025-3-23 11:12
Class, Surplus, and the Division of Labourhine learning techniques for condition monitoring in power transformers. Our objective is to classify the three different types of Partial Discharge (PD), the identify of which is highly correlated with insulation failure. Measurements from Acoustic Emission (AE) sensors are used as input data. Two 作者: Reservation 時(shí)間: 2025-3-23 16:39 作者: 周年紀(jì)念日 時(shí)間: 2025-3-23 19:50
https://doi.org/10.1057/9781137287731and location-based social networks has become a serious challenge for data management and analysis systems. In urban micro-climate, we need to deal with various types of data such as: environmental data measurements, Wi-Fi data and so on. The format and the nature of data coming from different senso作者: 嚴(yán)峻考驗(yàn) 時(shí)間: 2025-3-24 01:17
Data Analytics for Renewable Energy Integration. Technologies, Systems and Society978-3-030-04303-2Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 開頭 時(shí)間: 2025-3-24 04:11 作者: LEERY 時(shí)間: 2025-3-24 09:24
978-3-030-04302-5Springer Nature Switzerland AG 2018作者: watertight, 時(shí)間: 2025-3-24 12:00
Conference proceedings 2018 in Dublin, Ireland, in September 2018...The 9 papers presented in this volume were carefully reviewed and selected for inclusion in this book and handle topics such as time series forecasting, the detection of faults, cyber security, smart grid and smart cities, technology integration, demand response, and many others..作者: 傳授知識(shí) 時(shí)間: 2025-3-24 18:42 作者: 潛伏期 時(shí)間: 2025-3-24 21:30
Fused Lasso Dimensionality Reduction of Highly Correlated NWP Features,models, and the high correlation of the features, which can affect the performance of learning machines as Multilayer Perceptron (MLP). In this work we propose to reduce the dimension of the problem using a supervised Fused Lasso model, which generates meta-features corresponding to the average of t作者: 消極詞匯 時(shí)間: 2025-3-25 00:03
Sampling Strategies for Representative Time Series in Load Flow Calculations,grid. By using time series with high temporal resolution, information is getting more detailed, but at the same time, the computational costs of the algorithms increase. With the help of our algorithm, we create representative time series that have similar characteristics to the original time series作者: 斑駁 時(shí)間: 2025-3-25 04:29 作者: miscreant 時(shí)間: 2025-3-25 08:49 作者: 現(xiàn)實(shí) 時(shí)間: 2025-3-25 13:14
Deep Learning for Wave Height Classification in Satellite Images for Offshore Wind Access,of time. With our method, we demonstrate a process of utilizing large-scale satellite images to classify a wave height with a continuous regressive output using a corresponding input for close shore sea. We generated and trained a convolutional neural network model that achieved an average loss of 0作者: 高度贊揚(yáng) 時(shí)間: 2025-3-25 16:39 作者: violate 時(shí)間: 2025-3-25 22:49 作者: 歌劇等 時(shí)間: 2025-3-26 03:29
Short-Term Electricity Consumption Forecast Using Datasets of Various Granularities, In order to help achieve this balance in the grid, the renewable energy resources such as wind and stream-flow should be forecast at high accuracies on the generation side, and similarly, electricity consumption should be forecast using a high-performance system. In this paper, we deal with short-t作者: PURG 時(shí)間: 2025-3-26 05:09
Intelligent Monitoring of Transformer Insulation Using Convolutional Neural Networks,hine learning techniques for condition monitoring in power transformers. Our objective is to classify the three different types of Partial Discharge (PD), the identify of which is highly correlated with insulation failure. Measurements from Acoustic Emission (AE) sensors are used as input data. Two 作者: 脫水 時(shí)間: 2025-3-26 12:21
Nonintrusive Load Monitoring Based on Deep Learning, and recurrent neural network with fully connected layers, this paper develops a deep neural network based on sequence-to-sequence model and attention mechanism to perform nonintrusive load monitoring. The overall framework can be divided into three layers. In the first layer, the input active power作者: nonplus 時(shí)間: 2025-3-26 16:26
Urban Climate Data Sensing, Warehousing, and Analysis: A Case Study in the City of Abu Dhabi, Uniteand location-based social networks has become a serious challenge for data management and analysis systems. In urban micro-climate, we need to deal with various types of data such as: environmental data measurements, Wi-Fi data and so on. The format and the nature of data coming from different senso作者: 啟發(fā) 時(shí)間: 2025-3-26 17:19 作者: Euphonious 時(shí)間: 2025-3-26 22:54
Renewable Energy Integration: Bayesian Networks for Probabilistic State Estimation,es; auto-generation of Bayesian networks for probabilistic state estimation; integration with corrective Security-Constrained Optimal Power Flow; and application to Distributed Flexible AC Transmission Systems. We present novel models and algorithms for probabilistic state estimation using auto-gene作者: 大量 時(shí)間: 2025-3-27 02:47 作者: myriad 時(shí)間: 2025-3-27 08:06
Clustering River Basins Using Time-Series Data Mining on Hydroelectric Energy Generation,scussions. Based on these results, a new basin map is proposed which will be beneficial for enhanced hydrological and electrical analyses on hydropower and thereby for the maintenance of supply reliability and quality.作者: confide 時(shí)間: 2025-3-27 11:31 作者: 輕浮女 時(shí)間: 2025-3-27 14:44
Intelligent Monitoring of Transformer Insulation Using Convolutional Neural Networks,lts are particularly encouraging as manual feature extraction is a subjective process that may require significant redesign when confronted with new operating conditions and data types. In contrast, the ability to automatically learn feature sets from the raw input data (AE signals) promises better 作者: cardiopulmonary 時(shí)間: 2025-3-27 18:22 作者: Hirsutism 時(shí)間: 2025-3-27 23:51
https://doi.org/10.1007/978-3-031-45948-1tery takes into account the aging of batteries and the loss of energy due to storage. Battery life is set at 10 years, as we are considering using high quality batteries. In order to extend battery life, the algorithm counts with the charge level restrictions.作者: Adornment 時(shí)間: 2025-3-28 03:38
Social Work and Advanced Marginalityes; auto-generation of Bayesian networks for probabilistic state estimation; integration with corrective Security-Constrained Optimal Power Flow; and application to Distributed Flexible AC Transmission Systems. We present novel models and algorithms for probabilistic state estimation using auto-gene作者: 率直 時(shí)間: 2025-3-28 09:43
Class, Individualization and Late Modernity is combined with a Genetic Algorithm (GA) based optimization to evaluate tens of thousands of building configurations in terms of energy consumption, producing designs that are very close to the optimum.作者: 沙漠 時(shí)間: 2025-3-28 13:43
Class, Individualization and Late Modernityscussions. Based on these results, a new basin map is proposed which will be beneficial for enhanced hydrological and electrical analyses on hydropower and thereby for the maintenance of supply reliability and quality.作者: seruting 時(shí)間: 2025-3-28 15:24 作者: 檢查 時(shí)間: 2025-3-28 22:37
Class, Surplus, and the Division of Labourlts are particularly encouraging as manual feature extraction is a subjective process that may require significant redesign when confronted with new operating conditions and data types. In contrast, the ability to automatically learn feature sets from the raw input data (AE signals) promises better 作者: conspicuous 時(shí)間: 2025-3-29 02:04 作者: 圍巾 時(shí)間: 2025-3-29 04:52
Data Analytics for Renewable Energy Integration. Technologies, Systems and Society6th ECML PKDD Worksh作者: COLIC 時(shí)間: 2025-3-29 10:36
https://doi.org/10.1007/978-981-13-1102-4he stronger connections. As shown experimentally, training the models over the correlation graph-based reduced dataset allows to decrease the overall computational time while preserving almost the same error in the case of Support Vector Regressors and even improving the error of the MLPs, if the original dimension is high.作者: Urologist 時(shí)間: 2025-3-29 15:18
https://doi.org/10.1007/978-3-030-16222-1 the same results as with the original time series. In this work, we improve our previous algorithm with the help of specialized sampling strategies. Furthermore, we provide a new method to compare power analysis results achieved with the representative time series to the original time series.作者: 音樂等 時(shí)間: 2025-3-29 17:54 作者: 監(jiān)禁 時(shí)間: 2025-3-29 21:30
Sampling Strategies for Representative Time Series in Load Flow Calculations, the same results as with the original time series. In this work, we improve our previous algorithm with the help of specialized sampling strategies. Furthermore, we provide a new method to compare power analysis results achieved with the representative time series to the original time series.作者: 是突襲 時(shí)間: 2025-3-29 23:54 作者: 間諜活動(dòng) 時(shí)間: 2025-3-30 06:24
