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Expected Value01:15

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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:
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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...
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Related Experiment Video

Updated: May 8, 2025

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Short time solar power forecasting using P-ELM approach.

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
Summary
This summary is machine-generated.

Accurate short-term solar power forecasting is crucial for smart grids. A new hybrid machine learning method using a pre-trained extreme learning machine (P-ELM) algorithm improves prediction accuracy for photovoltaic power output.

Keywords:
Extreme learning machine (ELM)Pre-trained extreme learning machine (P-ELM)Short-term forecastingSolar power forecasting

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Area of Science:

  • Renewable Energy Systems
  • Artificial Intelligence in Power Engineering

Background:

  • Integrating large-scale photovoltaic (PV) generation into power systems presents challenges in accurate solar power forecasting.
  • Economical operation of microgrids and smart grids relies on precise solar power prediction.

Purpose of the Study:

  • To propose an accurate short-term solar power forecasting method.
  • To enhance the reliability of real-time solar power forecasting for grid integration.

Main Methods:

  • A hybrid machine learning algorithm utilizing a pre-trained extreme learning machine (P-ELM) for system training.
  • Input parameters include temperature, irradiance, and power output at instant i.
  • Output parameters predict temperature, irradiance, and power output at instant i+1 for next-day forecasting.

Main Results:

  • The P-ELM algorithm demonstrated higher accuracy in short-term solar power prediction compared to the standard extreme learning machine (ELM) algorithm.
  • Performance was evaluated using mean absolute error (MAE) and root mean square error (RMSE).

Conclusions:

  • The P-ELM algorithm offers a suitable solution for accurate and reliable short-term solar power forecasting.
  • This method supports the seamless integration of PV generation into smart grids and microgrids.