期刊
IET SCIENCE MEASUREMENT & TECHNOLOGY
卷 8, 期 3, 页码 90-97出版社
INST ENGINEERING TECHNOLOGY-IET
DOI: 10.1049/iet-smt.2013.0135
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; power system management; multiregression analysis; Elmann artificial neural network; ANN; power production prediction; Italy; time series; meteorological variable measurement; decomposition; amplitude error identification; phase error identification; kurtosis parameter; skewness parameter; power 960 kW
资金
- Project BEAMS [285194]
- 7th Framework Program - European Commission
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据