Short‐term probabilistic forecasting for regional wind power using distance‐weighted kernel density estimation

2018 ◽  
Vol 12 (15) ◽  
pp. 1725-1732 ◽  
Author(s):  
Zhao Wang ◽  
Weisheng Wang ◽  
Chun Liu ◽  
Bo Wang ◽  
Shuanglei Feng
Keyword(s):  
Wind Power ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Kernel Density ◽  
Probabilistic Forecasting ◽  
Short Term ◽  
Distance Weighted ◽  
Weighted Kernel

Regional wind power probabilistic forecasting based on an improved kernel density estimation, regular vine copulas, and ensemble learning

Energy ◽  
2022 ◽  
Vol 238 ◽  
pp. 122045
Author(s):  
Weichao Dong ◽  
Hexu Sun ◽  
Jianxin Tan ◽  
Zheng Li ◽  
Jingxuan Zhang ◽  
...  
Keyword(s):  
Wind Power ◽  
Density Estimation ◽  
Ensemble Learning ◽  
Kernel Density Estimation ◽  
Kernel Density ◽  
Probabilistic Forecasting ◽  
Vine Copulas

scholarly journals Hybrid methodology for modeling short-term wind power generation using conditional Kernel density estimation and singular spectrum analysis

DYNA ◽  
2017 ◽  
Vol 84 (201) ◽  
pp. 145 ◽  
Author(s):  
Soraida Aguilar-Vargas ◽  
Reinaldo Castro-Souza ◽  
José Francisco Pessanha ◽  
Fernando Luiz Cyrino-Oliveira
Keyword(s):  
Power Generation ◽  
Wind Power ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Kernel Density ◽  
Wind Power Generation ◽  
Short Term ◽  
Hybrid Methodology

Una parte fundamental del proceso de previsión probabilística de energía eólica es tener en cuenta las previsiones de la velocidad del viento. Para obtener pronósticos probabilísticos precisos de la producción eólica ha sido desarrollada una metodología híbrida utilizando técnicas no paramétricas conocidas como SSA (Análisis Singular Espectral) y Estimación Condicional de la Densidad por Kernel (CKDE). SSA es empleada para predecir la velocidad del viento y CKDE para obtener previsiones probabilísticas de la energía eólica, dado que la generación de la energía eólica tiene una relación no lineal con la velocidad del viento y ambas son variables aleatorias distribuidas que siguen una función de densidad conjunta. Haciendo uso de una base de datos brasilera horaria que incluye la velocidad del viento y la energía eólica es ilustrada dicha metodología. Una vez que las previsiones de la velocidad del viento son obtenidas, los correspondientes pronósticos probabilísticos de la generación de energía eólica son estimados para un horizonte de 24 horas. Los resultados obtenidos son comparados con otras metodologías existentes.

A Statistical Model for Wind Power Forecast Error Based on Kernel Density Estimation

2014 ◽  
Vol 8 (1) ◽  
pp. 501-507
Author(s):  
Liyang Liu ◽  
Junji Wu ◽  
Shaoliang Meng
Keyword(s):  
Probability Density Function ◽  
Probability Density ◽  
Wind Power ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Forecast Error ◽  
Kernel Density ◽  
Wind Power Forecast ◽  
Proposed Model ◽  
Chi Squared

Wind power has been developed rapidly as a clean energy in recent years. The forecast error of wind power, however, makes it difficult to use wind power effectively. In some former statistical models, the forecast error was usually assumed to be a Gaussian distribution, which had proven to be unreliable after a statistical analysis. In this paper, a more suitable probability density function for wind power forecast error based on kernel density estimation was proposed. The proposed model is a non-parametric statistical algorithm and can directly obtain the probability density function from the error data, which do not need to make any assumptions. This paper also presented an optimal bandwidth algorithm for kernel density estimation by using particle swarm optimization, and employed a Chi-squared test to validate the model. Compared with Gaussian distribution and Beta distribution, the mean squared error and Chi-squared test show that the proposed model is more effective and reliable.

Sign in / Sign up

Export Citation Format

Share Document