Photovoltaic power forecasting using statistical methods: impact of weather data

被引：226

作者：

De Giorgi, Maria Grazia ^{[1
]}

Congedo, Paolo Maria ^{[1
]}

Malvoni, Maria ^{[1
]}

机构：

[1] Univ Salento, Dept Engn Innovat, I-73100 Lecce, Italy

来源：

IET SCIENCE MEASUREMENT & TECHNOLOGY | 2014年 / 8卷 / 03期

关键词：

photovoltaic power systems; load forecasting; statistical analysis; power grids; power system management; regression analysis; neural nets; time series; power system measurement; power system identification; power engineering computing; photovoltaic power forecasting; statistical method; weather data impact; grid-connected photovoltaic system; PV system; multiregression analysis; Elmann artificial neural network; ANN; power production prediction; Italy; meteorological variable measurement; decomposition; amplitude error identification; phase error identification; kurtosis parameter; skewness parameter; power; 960; kW; NEURAL-NETWORKS; PREDICTION; FEEDFORWARD; IRRADIANCE;

D O I：

10.1049/iet-smt.2013.0135

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

080906 [电磁信息功能材料与结构]; 082806 [农业信息与电气工程];

摘要：

An important issue for the growth and management of grid-connected photovoltaic (PV) systems is the possibility to forecast the power output over different horizons. In this work, statistical methods based on multiregression analysis and the Elmann artificial neural network (ANN) have been developed in order to predict power production of a 960 kW(P) grid-connected PV plant installed in Italy. Different combinations of the time series of produced PV power and measured meteorological variables were used as inputs of the ANN. Several statistical error measures are evaluated to estimate the accuracy of the forecasting methods. A decomposition of the standard deviation error has been carried out to identify the amplitude and phase error. The skewness and kurtosis parameters allow a detailed analysis of the distribution error.

引用

页码：90 / 97

页数：8

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