Research on Probability Distribution of Short-Term Photovoltaic Output Forecast Error Based on Numerical Characteristic Clustering

Abstract

The forecast error characteristic analysis of short-term photovoltaic power generation can provide a reliable reference for power system optimal dispatching. In this paper, the total in-day error level was stratified by fuzzy C-means algorithm. Then the historical PV output data based on the numerical characteristics of point prediction output were classified. A General Gauss Mixed Model was proposed to fit the forecast error distribution of various photovoltaic output forecast error distribution. The impact of meteorological factors together with numerical characteristics on the forecast error was taken into full consideration in this analysis method. The predicted point output with high volatility can be accurately captured, and the reliable confidence interval is given. The proposed method is independent of the point prediction algorithm and has strong applicability. The General Gauss Mixed Model can meet the peak diversity, bias, and multimodal properties of the error distribution, and the fitting effect is superior to the normal distribution, the Laplace distribution, and the t Location-Scale distribution model. The error model has a flexible shape, a concise expression, and high practical value for engineering.

Machine learning-driven energy management of a hybrid nuclear-wind-solar-desalination plant

“…Then, Wu et al [42] developed a mixed LaPlace-Gaussian distribution to model persistence-based forecasts for wind power. While Yan et al [43] consider analysing PV power forecasting error distribution using a Gauss model. Note that the common limitation of the aforementioned work is to limit their analysis to few distributions, or to develop complex tailor-made solutions.…”

Retracted: Research on Probability Distribution of Short‐Term Photovoltaic Output Forecast Error Based on Numerical Characteristic Clustering

“…Tis article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. Tis investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”

“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [ 1 ]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process: Discrepancies in scope Discrepancies in the description of the research reported Discrepancies between the availability of data and the research described Inappropriate citations Incoherent, meaningless and/or irrelevant content included in the article Peer-review manipulation …”