Application of Time Series and Artificial Neural Network Models in Short-term Forecasting of PV Power Generation

被引：0

作者：

Kardakos, E. G. ^{[1
]}

Alexiadis, M. C. ^{[1
]}

Vagropoulos, S. I. ^{[1
]}

Simoglou, C. K. ^{[1
]}

Biskas, P. N. ^{[1
]}

Bakirtzis, A. G. ^{[1
]}

机构：

[1] Aristotle Univ Thessaloniki, Dept Elect & Comp Engn, Power Syst Lab, Thessaloniki 54124, Greece

来源：

2013 48TH INTERNATIONAL UNIVERSITIES' POWER ENGINEERING CONFERENCE (UPEC) | 2013年

关键词：

Artificial neural networks; autoregressive integrated moving average (ARIMA) models; day-ahead forecasting; photovoltaic plants; solar radiation; SOLAR-RADIATION;

D O I：

暂无

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

This paper addresses two practical methods for electricity generation forecasting of grid-connected PV plants. The first model is based on seasonal ARIMA time-series analysis and is further improved by incorporating short-term solar radiation forecasts derived from NWP models. The second model adopts artificial neural networks with multiple inputs. Day-ahead and rolling intra-day forecast updates are implemented to evaluate the forecasting errors. All models are compared in terms of the Normalized (with respect to the PV installed capacity) Root Mean Square Error (NRMSE). Simulation results from the application of the forecasting models in different PV plants of the Greek power system are presented.

引用

页数：6

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