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相关概念视频

Precipitation Processes01:12

Precipitation Processes

446
The experimental conditions in a gravimetric analysis should be optimized to maximize the particle size and purity of the obtained precipitate. Ideally, the concentration of the precipitating reagent should be low with effective stirring to maintain low relative supersaturation for the growth of large crystals. In homogeneous precipitation, the precipitant is slowly generated by a chemical reaction in the solution to avoid local reagent excesses. For example, urea decomposes gradually to...
446
Precipitation and Co-precipitation01:17

Precipitation and Co-precipitation

1.8K
Precipitation and coprecipitation methods can be used to separate a mixture of ions in a solution. In qualitative inorganic analysis, ions that form sparingly soluble precipitates with the same reagent are separated based on the differences in solubility products. For example, consider the separation of Cu(II) and Fe(II) ions by precipitation as insoluble sulfides. First, copper(II) sulfide is precipitated by the addition of acidic H2S, where the dissociation of H2S is suppressed. Adding H2S...
1.8K
Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

46
Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...
46
Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

191
The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:
191
The Power Flow Problem and Solution01:26

The Power Flow Problem and Solution

211
Power flow problem analysis is fundamental for determining real and reactive power flows in network components, such as transmission lines, transformers, and loads. The power system's single-line diagram provides data on the bus, transmission line, and transformer. Each bus k in the system is characterized by four key variables: voltage magnitude Vk​, phase angle δk​, real power Pk​, and reactive power Qk​. Two of these four variables are inputs, while the...
211
Survival Tree01:19

Survival Tree

84
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
84

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屋顶光伏系统的短期和中期预测模型与数据预处理.

Da-Sheng Lee1, Chih-Wei Lai1, Shih-Kai Fu1

  • 1National Taipei University of Technology Energy and Refrigerating Air-conditioning Engineering, Room 610, College of Mechanical & Electrical Engineering, Integrated Technology Complex, No.1, Sec. 3, Zhongxiao E. Rd., Da'an Dist., Taipei City 10608, Taiwan.

Heliyon
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概括

这项研究通过开发用于屋顶太阳能光伏电站的数据预处理方法,改善了太阳能预测. 人工智能模型显著减少了预测错误,提高了可再生能源预测.

关键词:
数据预处理数据的预处理.长时间的短期记忆 (LSTM)多层感知子 (MLP) 多层感知子 (MLP)预测太阳能能源的使用情况.

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科学领域:

  • 可再生能源工程可再生能源工程
  • 人工智能在能源中的作用
  • 数据科学用于电力系统的数据科学

背景情况:

  • 准确预测太阳能光伏发电对于电网稳定性和能源管理至关重要.
  • 屋顶太阳能装置由于变化的天气条件和数据可用性而存在独特的挑战.
  • 现有的数据预处理技术可能无法充分处理光伏数据集中的缺失值和异常值.

研究的目的:

  • 为屋顶太阳能光伏发电数据提出和评估一种新的数据预处理方法.
  • 为了提高短期和中期太阳能发电预测的准确性.
  • 评估拟议方法在与人工智能 (AI) 模型 (如多层感知器 (MLP) 和长短期记忆 (LSTM) 等) 集成时的有效性.

主要方法:

  • 收集了台湾17座屋顶太阳能光伏发电厂的数据 (2021年1月 - 2023年6月).
  • 开发了一种数据预处理技术,将线性回归和K-最近邻居 (k-NN) 结合起来,用于归纳缺失的天气和电力数据.
  • 采用历史数据来处理异常值,并确定与人工智能模型培训的发电相关的关键参数.
  • 经过培训和验证的MLP和LSTM模型用于预测太阳能发电.

主要成果:

  • 拟议的数据预处理方法显著减少了预测错误.
  • 对于MLP的短期预测,正常化根平均平方误差 (nRMSE) 降低了17.47%,中期降低了11.06%.
  • 对于LSTM的短期预测,nRMSE下降了20.20%,而中期预测下降了8.03%.
  • 数据预处理方法证明了人工智能驱动的太阳能预报的可靠性提高.

结论:

  • 综合数据预处理和人工智能建模方法有效提高了太阳能发电预测的准确性.
  • 开发的方法为在屋顶太阳能光伏系统中处理真实世界的数据挑战提供了强大的解决方案.
  • 这项研究通过提高预测准确度,有助于更可靠的可再生能源整合.