Related Concept Videos
Random Error
Maxwell-Boltzmann Distribution: Problem Solving
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
Propagation of Uncertainty from Systematic Error
Propagation of Uncertainty from Random Error
Prediction Intervals
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
Gauss's Law: Problem-Solving
You might also read
Related Articles
Articles linked to this work by shared authors, journal, and citation graph.
An Integrated Computer Vision and Force Sensing Framework for Automated Fugl-Meyer Hand-Related Assessment Using Artificial Neural Networks.
Related Experiment Video
Updated: Jul 4, 2025

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator
Published on: February 14, 2025
Modeling forecast errors for microgrid operation using Gaussian process regression.
1Department of Electrical Engineering, Myongji University, Yongin, 17058, Republic of Korea.
This study introduces a novel method for modeling net-load forecast errors in microgrids. It uses Gaussian process regression to better predict uncertainties from renewable energy sources.
Area of Science:
- Electrical Engineering
- Power Systems
- Renewable Energy Integration
Background:
- Microgrids are crucial for integrating renewable energy sources like solar and wind.
- Renewable energy's inherent variability requires effective uncertainty management strategies.
- Accurate assessment of uncertainty factors is vital for cost-effective microgrid operation.
Purpose of the Study:
- To develop a method for modeling the probability distribution of net-load forecast errors in microgrids.
- To account for the temporal inter-dependencies among various uncertainty factors.
- To improve the accuracy of net-load error distribution estimation.
Main Methods:
- Utilized Gaussian process regression for a data-driven approach.
- Transformed diverse uncertainty factors into normal distributions while preserving marginal characteristics.
- Modeled conditional probability distributions among uncertainty factors.
Main Results:
- The proposed method effectively models net-load forecast error distribution.
- Conditional probability density functions were trained and validated.
- The approach enhances the suitability of density functions for net-load error approximation.
Conclusions:
- The developed methodology provides a superior approach to approximating net load error distribution in microgrids.
- This enhances the reliability and efficiency of microgrid operations with high renewable penetration.
- The technique offers a robust framework for managing complex uncertainties in power systems.

