Data Driven Model for Short Term PV Power Forecasting using Least Square Support Vector Regression

被引：0

作者：

Fentis, Ayoub ^{[1
]}

Bahatti, Lhoussine ^{[1
]}

Tabaa, Mohamed ^{[2
]}

Chouri, Brahim ^{[2
]}

Mestari, Mohammed ^{[1
]}

机构：

[1] ENSET Mohammedia, Lab Signals Distributed Syst & Artificial Intelli, Mohammadia, Morocco

[2] EMSI Casablanca, Pluridisciplinary Lab Res & Innovat LPRI, Casablanca, Morocco

来源：

PROCEEDINGS OF 2017 INTERNATIONAL RENEWABLE & SUSTAINABLE ENERGY CONFERENCE (IRSEC' 17) | 2017年

关键词：

Photovoltaic Power; Least Square Support Vector Regression; Forecasting; Machine Learning; Grid Management; Smart Grid; HYBRID METHOD; OUTPUT;

D O I：

暂无

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

This paper presents an off-line model for forecasting photovoltaic power. This model is suitable to provide short-term forecasts without the need of Numerical Weather predictions data. This is interesting especially for power system operators as well as for individuals who do not have access to weather data and forecasts. In this paper we investigate the influence of an additional input parameter to the accuracy of an already tested and validated offline model. To rectify the performances of our models we will compare their performances with a usual persistent model. The results of simulation shows the benefits of adding this input to improve the accuracy of our PV forecasting model.

引用

页码：1117 / 1122

页数：6

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