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相关概念视频

Expected Value01:15

Expected Value

3.8K
The expected value is known as the "long-term" average or mean. This means that over the long term of experimenting over and over, you would expect this average. The expected average is represented by the symbol μ. It is calculated as follows:
3.8K
Average Power01:13

Average Power

551
In practical electrical applications, the concept of time-varying instantaneous power is not frequently utilized. Instead, focus shifts to the more practical quantity known as average power. Average power is determined by integrating the instantaneous power over a specified time period and subsequently dividing it by that duration.
551
Power System Three-Phase Short Circuits01:21

Power System Three-Phase Short Circuits

59
Determining the subtransient fault current in a power system involves representing transformers by their leakage reactances, transmission lines by their equivalent series reactances, and synchronous machines as constant voltage sources behind their subtransient reactances. In this analysis, certain elements are excluded, such as winding resistances, series resistances, shunt admittances, delta-Y phase shifts, armature resistance, saturation, saliency, non-rotating impedance loads, and small...
59
Econometric Views (EViews)01:29

Econometric Views (EViews)

87
Econometric Views, often stylized as EViews, is a package that merges statistical analysis with econometric studies. It is designed to provide tools for time series analysis, forecasting, and econometric model simulation. The software originated from MicroTSP software and has evolved significantly since its inception in 1981. The history of EViews is marked by a continuous effort to enhance its computational speed and user interface. It was initially developed for large computing systems but...
87
Multimachine Stability01:25

Multimachine Stability

100
Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
100

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使用P-ELM方法进行短期太阳能预测.

Shuqi Shi1,2, Boyang Liu3,4, Long Ren3,4

  • 1Hunan Provincial Key Laboratory of Grids Operation and Control on Multi-Power Sources Area, Shaoyang University, Shaoyang, 422000, China. shuqishi0706@163.com.

Scientific reports
|December 27, 2024
PubMed
概括

准确的短期太阳能预测对于智能电网至关重要. 使用预训练极端学习机器 (P-ELM) 算法的新混合机器学习方法提高了光伏发电输出的预测准确性.

关键词:
极端学习机器 (ELM) 是一种极端学习机器.预训练的极端学习机器 (P-ELM)短期预测 短期预测预测太阳能发电的情况

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科学领域:

  • 可再生能源系统可再生能源系统
  • 人工智能在电力工程中的应用

背景情况:

  • 将大型光伏发电 (PV) 集成到电力系统中,在准确预测太阳能发电方面存在挑战.
  • 微电网和智能电网的经济运行依赖于精确的太阳能预测.

研究的目的:

  • 提出一个准确的短期太阳能预测方法.
  • 提高实时太阳能预测的可靠性,以实现电网整合.

主要方法:

  • 一种混合机器学习算法,使用预训练的极端学习机器 (P-ELM) 进行系统训练.
  • 输入参数包括温度,辐射率和即时输出功率.
  • 输出参数预测温度,辐射强度和输出功率在瞬间i+1下一天的预测.

主要成果:

  • 与标准极端学习机器 (ELM) 算法相比,P-ELM算法在短期太阳能预测中显示出更高的准确性.
  • 使用平均绝对误差 (MAE) 和根平均平方误差 (RMSE) 评估性能.

结论:

  • P-ELM算法为准确可靠的短期太阳能预测提供了一个合适的解决方案.
  • 这种方法支持光伏发电无整合到智能电网和微电网中.