Paper Info
Title
Binding statistical and machine learning models for short-term forecasting of global solar radiation
Abstract
A model for short-term forecasting of continuous time series has been developed. This model binds the use of both statistical and machine learning methods for short-time forecasting of continuous time series of solar radiation. The prediction of this variable is needed for the integration of photovoltaic systems in conventional power grids. The proposed model allows us to manage not only the information in the time series, but also other important information supplied by experts. In a first stage, we propose the use of statistical models to obtain useful information about the significant information for a continuous time series and then we use this information, together with machine learning models, statistical models and expert knowledge, for short-term forecasting of continuous time series. The results obtained when the model is used for solar radiation series show its usefulness.
Year
DOI
Venue
2011
10.1007/978-3-642-24800-9_28
IDA
Keywords
Field
DocType
time series,solar radiation series,statistical model,significant information,short-term forecasting,global solar radiation,short-time forecasting,useful information,important information,continuous time series,machine learning
Computer science,Statistical model,Artificial intelligence,Photovoltaic system,Machine learning,Global solar radiation
Conference
Volume
ISSN
Citations 
7014
0302-9743
2
PageRank 
References 
Authors
0.44
11
4
Name
Order
Citations
PageRank
Llanos Mora-López1121.78
Ildefonso Martínez-Marchena220.77
Michel Piliougine321.11
M. Sidrach-de-Cardona4103.10