skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: An application of the ECMWF Ensemble Prediction System for short-term solar power forecasting

Authors:
; ;
Publication Date:
Sponsoring Org.:
USDOE
OSTI Identifier:
1341218
Grant/Contract Number:
EE0006016
Resource Type:
Journal Article: Publisher's Accepted Manuscript
Journal Name:
Solar Energy
Additional Journal Information:
Journal Volume: 133; Journal Issue: C; Related Information: CHORUS Timestamp: 2017-10-03 21:31:18; Journal ID: ISSN 0038-092X
Publisher:
Elsevier
Country of Publication:
United States
Language:
English

Citation Formats

Sperati, Simone, Alessandrini, Stefano, and Delle Monache, Luca. An application of the ECMWF Ensemble Prediction System for short-term solar power forecasting. United States: N. p., 2016. Web. doi:10.1016/j.solener.2016.04.016.
Sperati, Simone, Alessandrini, Stefano, & Delle Monache, Luca. An application of the ECMWF Ensemble Prediction System for short-term solar power forecasting. United States. doi:10.1016/j.solener.2016.04.016.
Sperati, Simone, Alessandrini, Stefano, and Delle Monache, Luca. 2016. "An application of the ECMWF Ensemble Prediction System for short-term solar power forecasting". United States. doi:10.1016/j.solener.2016.04.016.
@article{osti_1341218,
title = {An application of the ECMWF Ensemble Prediction System for short-term solar power forecasting},
author = {Sperati, Simone and Alessandrini, Stefano and Delle Monache, Luca},
abstractNote = {},
doi = {10.1016/j.solener.2016.04.016},
journal = {Solar Energy},
number = C,
volume = 133,
place = {United States},
year = 2016,
month = 8
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record at 10.1016/j.solener.2016.04.016

Citation Metrics:
Cited by: 5works
Citation information provided by
Web of Science

Save / Share:
  • A knowledge-based expert system is proposed for the short term load forecasting of Taiwan power system. The developed expert system, which was implemented on a personal computer, was written in PROLOG using a 5-year data base. To benefit from the expert knowledge and experience of the system operator, eleven different load shapes, each with different means of load calculations, are established. With these load shapes at hand, some peculiar load characteristics pertaining to Taiwan Power Company can be taken into account. The special load types considered by the expert system include the extremely low load levels during the week ofmore » the Chinese New Year, the special load characteristics of the days following a tropical storm or a typhoon, the partial shutdown of certain factories on Saturdays, and the special event caused by a holiday on Friday or on Tuesday, etc. A characteristic feature of the proposed knowledge-based expert system is that it is easy to add new information and new rules to the knowledge base. To illustrate the effectiveness of the presented expert system, short-term load forecasting is performed on Taiwan power system by using both the developed algorithm and the conventional Box-Jenkins statistical method. It is found that a mean absolute error of 2.52% for a year is achieved by the expert system approach as compared to an error of 3.86% by the statistical method.« less
  • 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 smoothingmore » 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. (author)« less
  • This paper presents the development of an Artificial Neural Network (ANN) based short-term load forecasting model for the Energy Control Center of the Greek Public Power Corporation (PPC). The model can forecast daily load profiles with a lead time of one to seven days. Attention was paid for the accurate modeling of holidays. Experiences gained during the development of the model regarding the selection of the input variables, the ANN structure, and the training data set are described in the paper. The results indicate that the load forecasting model developed provides accurate forecasts.
  • The prediction skill of tropical synoptic scale transients (SSTR) such as monsoon low and depression during the boreal summer of 2007–2009 are assessed using high resolution ECMWF and NCEP TIGGE forecasts data. By analyzing 246 forecasts for lead times up to 10 days, it is found that the models have good skills in forecasting the planetary scale means but the skills of SSTR remain poor, with the latter showing no skill beyond 2 days for the global tropics and Indian region. Consistent forecast skills among precipitation, velocity potential, and vorticity provide evidence that convection is the primary process responsible formore » precipitation. The poor skills of SSTR can be attributed to the larger random error in the models as they fail to predict the locations and timings of SSTR. Strong correlation between the random error and synoptic precipitation suggests that the former starts to develop from regions of convection. As the NCEP model has larger biases of synoptic scale precipitation, it has a tendency to generate more random error that ultimately reduces the prediction skill of synoptic systems in that model. Finally, the larger biases in NCEP may be attributed to the model moist physics and/or coarser horizontal resolution compared to ECMWF.« less