NDLI: 24-hour-ahead forecasting of energy production in solar PV systems

Content Provider	IEEE Xplore Digital Library
Author	Cococcioni, M. D'Andrea, E. Lazzerini, B.
Copyright Year	2011
Description	Author affiliation: Dipartimento di Ingegneria dell'Informazione: Elettronica, Informatica, Telecomunicazioni, University of Pisa Largo Lucio Lazzarino, 1, 56122 Pisa - Italy (Cococcioni, M.; D'Andrea, E.; Lazzerini, B.)
Abstract	This paper presents a flexible approach to forecasting of energy production in solar photovoltaic (PV) installations, using time series analysis and neural networks. Our goal is to develop a one day-ahead forecasting model based on an artificial neural network with tapped delay lines. Despite some methods already exist for energy forecasting problems, the main novelty of our approach is the proposal of a tool for the technician of a PV installation to correctly configure the forecasting model according to the particular installation characteristics. The correct configuration takes into account the number of hidden neurons, the number of delay elements, and the training window width, i.e., the appropriate number of days, before the predicted day, employed for the training. The irradiation along with the sampling hour are used as input variables to predict the daily accumulated energy with a percentage error less than 5%.
Starting Page	1276
Ending Page	1281
File Size	564512
Page Count	6
File Format	PDF
ISBN	9781457716768
ISSN	21647151
e-ISBN	9781457716768
DOI	10.1109/ISDA.2011.6121835
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-11-22
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Time series analysis Neurons forecasting solar photovoltaic energy Production Artificial neural networks Predictive models time series Forecasting NARX
Content Type	Text
Resource Type	Article

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