Despite the growing literature on renewable energy sources, causal relationships between the variables that are selected as inputs of the models proposed in forecasting studies have not been investigated so far. In this paper, a novel approach to decide prediction input variables of wind and/or temperature forecasting models is suggested. This approach uses time series techniques; more specifically, Granger causality and impulse-response analyses between some meteorological variables. To conduct our study, wind speed, temperature and pressure data obtained from different regions of Turkey are employed. The results suggest that bidirectional causal relationships exist between these variables and that short-run dynamics differ with respect to location (inland versus coastal area). From this, it is concluded that renewable energy models must be built accordingly to improve prediction accuracy.
(private-note)Shows use of a statistical technique from econometrics that finds *causal* relationships in multivariate timeseries. They mention a climatological application linking SSTs to the NAO causally, which sounds good. Their application in this paper is a bit odd, as it's short-term meteorological forecasting for renewables. Surely we know the causal relationships between variables here? If anything we'd want to simplify them down to statistical relationships, not worrying about causality. Ho hum.
%0 Journal Article
%1 Hocaoglu2013Time
%A Hocaoglu, Fatih O.
%A Karanfil, Fatih
%D 2013
%J Renewable and Sustainable Energy Reviews
%K wind energy statistics review renewables model
%P 204--214
%R 10.1016/j.rser.2013.07.054
%T A time series-based approach for renewable energy modeling
%U http://dx.doi.org/10.1016/j.rser.2013.07.054
%V 28
%X Despite the growing literature on renewable energy sources, causal relationships between the variables that are selected as inputs of the models proposed in forecasting studies have not been investigated so far. In this paper, a novel approach to decide prediction input variables of wind and/or temperature forecasting models is suggested. This approach uses time series techniques; more specifically, Granger causality and impulse-response analyses between some meteorological variables. To conduct our study, wind speed, temperature and pressure data obtained from different regions of Turkey are employed. The results suggest that bidirectional causal relationships exist between these variables and that short-run dynamics differ with respect to location (inland versus coastal area). From this, it is concluded that renewable energy models must be built accordingly to improve prediction accuracy.
@article{Hocaoglu2013Time,
abstract = {Despite the growing literature on renewable energy sources, causal relationships between the variables that are selected as inputs of the models proposed in forecasting studies have not been investigated so far. In this paper, a novel approach to decide prediction input variables of wind and/or temperature forecasting models is suggested. This approach uses time series techniques; more specifically, Granger causality and impulse-response analyses between some meteorological variables. To conduct our study, wind speed, temperature and pressure data obtained from different regions of Turkey are employed. The results suggest that bidirectional causal relationships exist between these variables and that short-run dynamics differ with respect to location (inland versus coastal area). From this, it is concluded that renewable energy models must be built accordingly to improve prediction accuracy.},
added-at = {2018-06-18T21:23:34.000+0200},
author = {Hocaoglu, Fatih O. and Karanfil, Fatih},
biburl = {https://www.bibsonomy.org/bibtex/24707a64fc0711786263cb65c4505408c/pbett},
citeulike-article-id = {12614268},
citeulike-attachment-1 = {Hocaoglu_Karanfil_2013_timeseriesapproachtoenergy.pdf; /pdf/user/pbett/article/12614268/933254/Hocaoglu_Karanfil_2013_timeseriesapproachtoenergy.pdf; 7a35585e99bd6a8f904128858a5aa4777a4b49c5},
citeulike-linkout-0 = {http://dx.doi.org/10.1016/j.rser.2013.07.054},
comment = {(private-note)Shows use of a statistical technique from econometrics that finds *causal* relationships in multivariate timeseries. They mention a climatological application linking SSTs to the NAO causally, which sounds good. Their application in this paper is a bit odd, as it's short-term meteorological forecasting for renewables. Surely we know the causal relationships between variables here? If anything we'd want to simplify them down to statistical relationships, not worrying about causality. Ho hum.},
doi = {10.1016/j.rser.2013.07.054},
file = {Hocaoglu_Karanfil_2013_timeseriesapproachtoenergy.pdf},
interhash = {a2c3e8ea33228bc1773017993f0a03bf},
intrahash = {4707a64fc0711786263cb65c4505408c},
issn = {13640321},
journal = {Renewable and Sustainable Energy Reviews},
keywords = {wind energy statistics review renewables model},
month = dec,
pages = {204--214},
posted-at = {2013-09-10 10:43:47},
priority = {2},
timestamp = {2018-06-22T18:33:42.000+0200},
title = {A time series-based approach for renewable energy modeling},
url = {http://dx.doi.org/10.1016/j.rser.2013.07.054},
volume = 28,
year = 2013
}