Online short-term solar power forecasting

This paper describes a new approach to online forecasting of power production from PV systems. The method is suited to online forecasting in many applications and in this paper it is used to predict hourly values of solar power for horizons of up to 36 h. The data used is 15-min observations of solar power from 21 PV systems located on rooftops in a small village in Denmark. The suggested method is a two-stage method where first a statistical normalization of the solar power is obtained using a clear sky model. The clear sky model is found using statistical smoothing techniques. Then forecasts of the normalized solar power are calculated using adaptive linear time series models. Both autoregressive (AR) and AR with exogenous input (ARX) models are evaluated, where the latter takes numerical weather predictions (NWPs) as input. The results indicate that for forecasts up to 2 h ahead the most important input is the available observations of solar power, while for longer horizons NWPs are the most important input. A root mean square error improvement of around 35% is achieved by the ARX model compared to a proposed reference model.

[1]  Jonathan D. Cryer,et al.  Time Series Analysis , 1986 .

[2]  Suri Marcel,et al.  Solar Energy Resource Management for Electricity Generation from Local Level to Global Scale , 2006 .

[3]  R. Koenker.  Quantile Regression: Fundamentals of Quantile Regression , 2005 .

[4]  Peder Bacher.  Short-term Solar Power Forecasting , 2008 .

[5]  Jiacong Cao,et al.  Study of hourly and daily solar irradiation forecast using diagonal recurrent wavelet neural networks , 2008 .

[6]  Saifur Rahman,et al.  Forecasting sub-hourly solar irradiance for prediction of photovoltaic output , 1987 .

[7]  E. Lorenz,et al.  Forecasting Solar Radiation , 2021, Journal of Cases on Information Technology.

[8]  Pierre Pinson,et al.  Standardizing the Performance Evaluation of Short-Term Wind Power Prediction Models , 2005 .

[9]  Athanasios Sfetsos,et al.  Univariate and multivariate forecasting of hourly solar radiation with artificial intelligence techniques , 2000 .

[10]  C. K. Chan,et al.  Prediction of hourly solar radiation using a novel hybrid model of ARMA and TDNN , 2011 .

[11]  Jan Kloppenborg Møller,et al.  ARTICLE IN PRESS Computational Statistics & Data Analysis ( ) – Time-adaptive quantile regression , 2022 .

[12]  G. Koeppel,et al.  Using storage devices for compensating uncertainties caused by non-dispatchable generators , 2006, 2006 International Conference on Probabilistic Methods Applied to Power Systems.

[13]  Detlev Heinemann,et al.  FORECAST OF ENSEMBLE POWER PRODUCTION BY GRID-CONNECTED PV SYSTEMS , 2007 .

[14]  Hans-Georg Beyer,et al.  Irradiance Forecasting for the Power Prediction of Grid-Connected Photovoltaic Systems , 2009, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[15]  James D. Hamilton.  Time Series Analysis , 1994 .

[16]  F. Hocaoglu,et al.  Hourly solar radiation forecasting using optimal coefficient 2-D linear filters and feed-forward neural networks , 2008 .

[17]  Stanley R. Johnson,et al.  Varying Coefficient Models , 1984 .

[18]  H. Aa,et al.  PREDICTION OF WIND POWER USING TIME-VARYING COEFFICIENT-FUNCTIONS , 2002 .