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从能源/交通相关场景到水管理领域的方法转换
Methodology transfer from energy/mobility related scenarios to water management domain |
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| 课程网址: |
https://videolectures.net/water4cities_kenda_senozetnik_methodolo...
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| 主讲教师: |
Klemen Kenda; Matej Senožetnik
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| 开课单位: |
Water4City网络研讨会
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| 开课时间: |
2017-12-19
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| 课程语种: |
英语
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| 中文简介: |
Water4Cities项目的第一次网络研讨会侧重于以前FP6、FP7和H2020项目的工作和成果，这些项目可以在水管理场景中有效地重复利用。介绍了以下项目的成果和工具:NRG4CAST(不同能源相关数据的能源预测和数据分析)、Sunseed(智能电网的能源预测)和Optimum(数据收集基础设施)。网络研讨会的第一部分在理论层面上介绍概念和方法，而第二部分是关于这些方法在Water4Cities项目中的潜在用途的实践研讨会。特别地，介绍了一般的流挖掘工作流程和以下数据预处理步骤:•用于收集卢布尔雅那含水层地下水数据的数据收集基础设施(采用自Optimum项目);来自darksky.net和Skiathos的天气数据从遗留系统Excel文件中抽取数据\•用于数据检索的API(也来自Optimum项目)\•卢布尔雅那含水层数据的数据清洗基础设施并呈现结果\•智能电网数据流上的数据融合基础设施\•使用Python/scikit-learn/pandas在W4C数据收集基础设施之上的简单快速数据驱动建模功能。
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| 课程简介: |
The first webinar of the Water4Cities project focuses on the work and achievements from previous FP6, FP7 and H2020 projects that can be efficiently re-used in water management scenarios. Outcomes and tools from the following projects: NRG4CAST (energy forecasting and data analysis on different energy-related data), Sunseed (energy forecasting for smart grids) and Optimum (data gathering infrastructure) are presented. The first part of the webinar introduces concepts and approaches at a theoretical level, while the second part is a hands-on seminar on the potential usage of these methods in the Water4Cities project. In particular, general stream-mining workflow and the following data pre-processing steps are presented:\ • data gathering infrastructure (adopted from Optimum project) for collecting Ljubljana aquifer groundwater data, weather data from darksky.net and Skiathos pumping data from legacy system Excel files\ • API for data retrieval (also adopted from Optimum project) on the previously mention datasets\ • data cleaning infrastructure on Ljubljana aquifer data and present the results\ • data fusion infrastructure on a stream of smart-grid data\ • simple and fast data-driven modelling capabilities on the top of W4C data gathering infrastructure with the usage of Python/scikit-learn/pandas.
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| 关 键 词: |
水管理场景; 能源预测; 数据分析
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| 课程来源: |
视频讲座网
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| 数据采集: |
2024-03-12：liyq
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| 最后编审: |
2024-03-12：liyq
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| 阅读次数: |
9
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图 书 馆