In this study, Artificial Neural Networks are applied to multistep
long term solar radiation prediction. The networks are
trained as one-step-ahead predictors and iterated over time to
obtain multi-step longer term predictions. Auto-regressive and
Auto-regressive with exogenous inputs solar radiationmodels are
compared, considering cloudiness indices as inputs in the latter
case. These indices are obtained through pixel classification of
ground-to-sky images. The input-output structure of the neural
network models is selected using evolutionary computation
methods

Crispim, E. M.

Ferreira, P. M.

Ruano, Antonio

Sapientia

Prediction of the solar radiation using RBF NeuralNetworks and ground-to-sky imagesEduardo M. Crispim, Pedro M. Ferreira and Anto´nio. E. RuanoCentre for Intelligent SystemsFaculty of Sciences and TechnologyUniversity of AlgarveCampus de Gambelas8005-139 FaroPortugalEmail: {ecrispim,pfrazao,aruano}@ualg.ptEXTENDED ABSTRACTIn this study, Artificial Neural Networks are applied to multi-step long term solar radiation prediction. The networks aretrained as one-step-ahead predictors and iterated over time toobtain multi-step longer term predictions. Auto-regressive andAuto-regressive with exogenous inputs solar radiation models arecompared, considering cloudiness indices as inputs in the lattercase. These indices are obtained through pixel classification ofground-to-sky images. The input-output structure of the neuralnetwork models is selected using evolutionary computationmethods.Global solar radiation (SR) influences the majority of livingbeings in many different ways. In the particular case ofinterest it affects greatly the energy consumption patterns ofsociety. Having reasonably good predictions of SR evolutionwithin a given future horizon is a key factor for many practicalapplications where the minimization of energy consumptionconstitutes an objective. A review of applications in renewableenergy systems based on SR forecasting can be found in[1]. Many environmental control strategies in buildings areexpressed as optimization problems where at least two goalsneed to be met: the minimization of energy consumptionand the optimization of some criteria inherent to the activityin the building or its occupants. Consider, for example,heating ventilation and air conditioner (HVAC) systems inbuilding compartments [2]: one wants maximize the level ofthermal comfort with minimal energy consumption. In [3]it is shown that the temperature estimation of a particularcompartment depends mostly on SR. Another example areproduction greenhouses: the grower wants to achieve goodtemperature and humidity regulation at the smallest energy costspossible while maximizing crop growth and production. Theseare greatly dependent on temperature and SR integrals. In bothexamples the influence of SR on the processes is twofold: itaffects the controlled variables and the process being optimized.Previous work, showed that the predicted SR time seriesobtained, have a very smooth behaviour capturing the seriestrend within the prediction horizon. However, in cloudy daysthe models fail to capture the dynamics caused by cloudiness,partially because the models use only measured past SR valuesas inputs. In order to obtain more accurate SR predictions,information on cloudiness should be fed to the models. In thiswork cloudiness information is incorporated in the ArtificialNeural Network (ANN) SR model with the aim of obtainingbetter predictions in the presence of cloud activity. Non-linear Auto-regressive (NAR) and Non-linear Auto-regressivewith Exogenous inputs (NARX) models are compared. In thelater case a cloudiness index is the only exogenous input. Thecomparison will give insight about the usefulness of cloudinessindices to the SR models.The models employed in the NAR and NARX are based onthe Radial Basis Function Neural Networks (RBFNN). The feed-forward RBFNN was chosen for its structural simplicity, theuniversal approximation property [4], the nonlinear nature ofthe time series to be modelled, and also due to its toleranceto imprecision and noise. The training process employed canbe found in detail in [5], [6]. The model structure, witch are,the lagged inputs variables terms and the number of neuronsare achivied using the method described in [7], [8], [9], [10]and is based in an evolutionary computing approach, or, in amore concrete matter, the Multi-Objective Genetic Algorithm(MOGA) [11], [12].In order to train the RBFNN models, a data set are required.Real data were obtained from a acquisition system installed onthe roof of the Faculty of Sciences and Technology buildingSolar radiationNARX NARSky cleanc)b)Solar radiationNARXSolar radiationNARa)^SR^SRSR^SR^SRSR PCSPCSPCSPCSFig. 1. NAR AND NARX MODELSof the University of Algarve, Portugal. It consists of asolar radiation sensor, measuring the global solar radiation inWatts×m−2, and a Total Sky Imager (TSI), that takes picturesof the sky using a CCD camera. The data is stored in alocal computer with a period of 1 minute. Once collected,the sky images are processed in order to obtain the cloudinessindices (as percentages). This process consists a pixel-by-pixelclassification, involving also a RBFNN, witch in this particularprocess behaves as a RBFNN classifier.For prediction, three different models were used. The firstis a NAR solar radiation model, illustrated in Figure 1 a). Thismodel only requires the SR past value for one-step-ahead (OSA)prediction, while for the multi-step predictions, requires that thepredicted values (SˆR) need to be feed in the inputs of the NAR.Secondly a NARX model as depicted in Figure 1 b), incorporatesPCS values as exogenous inputs. Finally, as this model requiresPCS predicted values in order to perform long term multi-steppredictions, a third NAR model is considered for PCS as shownin Figure 1 c).For each NAR and NARX model presented in Figures 1, oneMOGA run was computed (please consult [7], [8], [9], [10] for amore detailed explanation of the method), and chosen an optimalmodel structure from a candidate set returned by the MOGA.This candidate set obtained are RBFNN models that meet pre-specified objectives parameters. In general, performance andmodel complexity objectives were specified.The results of the experimental setup demonstrated thatthe use of the cloudiness indices can be beneficial for solarradiation prediction. However, in terms of practical applicability,preliminary results are not completely satisfactory. The errorintroduced by the required cloudiness index estimated values,limits the accuracy of the NARX solar radiation predictions,in such way that the results are comparable to those obtainedby NAR solar radiation models. In order to overcomethis limitation, the cloudiness index predictive models needimprovement in terms of accuracy and dynamics. This can beobtained through better pixel classification of the sky images,which is ongoing research. Future work will address the useof an alternative radial basis function (multi-quadric) whichin recent tests showed improvements in the cloudiness indicesmodels.REFERENCES[1] S. A. Kalogirou, “Artificial neural networks in renewable energy systemsapplications: a review,” Renewable and Sustainable Energy Reviews,vol. 5, pp. 373–401, 2001.[2] E. M. Crispim, M. D. Martins, and A. E. Ruano, “Model comparisonfor temperature estimation inside buildings,” in IEEE InternationalWorkshop on Soft Computing Applications, Szeged-Hungary and Arad-Romania, 2005.[3] A. E. Ruano, E.M. Crispim, E.Z.E Conceic¸a˜o, and M. M. Lu´c¸io,“Prediction of building’s temperature using neural network models,”Energy & Buildings, Elsevier, 2005, In press.[4] F. Girosi and T. Poggio, “Networks and the best approximationproperty,” Biological Cybernetics, vol. 63, pp. 169–176, 1990.[5] P. M. Ferreira and A. E. Ruano, “Exploiting the separability oflinear and nonlinear parameters in radial basis function networks,”in IEEE Symposium 2000: Adaptive Systems for Signal Processing,Communications, and Control, Lake Louise, Alberta, Canada, 2000, pp.321–326.[6] P. M. Ferreira, E. A. Faria, and A. E. Ruano, “Neural network models ingreenhouse air temperature prediction,” Neurocomputing, Special Issueon Engineering Applications of Neural Networks, vol. 43, no. 1, pp. 51–75, 2001.[7] P. M. Ferreira, A. E. Ruano, and C. M. Fonseca, “Genetic assistedselection of rbf model structures for greenhouse inside air temperatureprediction,” in CCA 2003, IEEE Conference on Control Applications,Istanbul, Turkey, 2003, pp. 23–25.[8] P. M. Ferreira, A. E. Ruano, and C. M. Fonseca, “Evolutionarymultiobjective design of radial basis function networks for greenhouseenvironmental control,” in 16th IFAC World Congress, Praga, CzechRepublic, 2005, pp. 4–8.[9] P. M. Ferreira and A. E. Ruano, “Soft-computing methods ingreenhouse environmental and crop modelling,” in Proceedings of theEFITA/WCCA2005 Conference, the 5th Conference of the EuropeanFederation for Information Technology in Agriculture, Food andEnvironment, and the 3th World Congress on Computers in Agricultureand Natural Resources, Vila Real, Portugal, 2005, pp. 25–28.[10] A. E. Ruano, P. M. Ferreira, and C. M. Fonseca, “An overview ofnonlinear identification and control with neural networks,” in IntelligentControl Systems Using Computational Intelligence Techniques, A. E.Ruano, Ed., 70, pp. 37–87. IEE Control Engineering, The Institution ofElectrical Engineers, United Kingdom, 2005.[11] C. M. Fonseca and P. J. Fleming, “Non-linear system identification withmultiobjective genetic algorithms,” in Proceedings of the 13th IFACWorld Congress, San Francisco, California, USA, 1996, vol. C, pp. 187–192.[12] C. M. Fonseca and P. J. Flemming, “Multiobjective optimization andmultiple constraint handling with evolutionary algorithms i: A unifiedformulation,” IEEE Transactions on System, Man and Cybernetics-PartA: Systems and Humans, vol. 28, pp. 26–37, 1998.

Prediction of the solar radiation using RBF neural networks and ground-to-sky images

Universidade do Algarve

Prediction of the solar radiation using RBF Neural
Networks and ground-to-sky images
Eduardo M. Crispim, Pedro M. Ferreira and Anto´nio. E. Ruano
Centre for Intelligent Systems
Faculty of Sciences and Technology
University of Algarve
Campus de Gambelas
8005-139 Faro
Portugal
Email: {ecrispim,pfrazao,aruano}@ualg.pt
EXTENDED ABSTRACT
In this study, Artificial Neural Networks are applied to multi-
step long term solar radiation prediction. The networks are
trained as one-step-ahead predictors and iterated over time to
obtain multi-step longer term predictions. Auto-regressive and
Auto-regressive with exogenous inputs solar radiation models are
compared, considering cloudiness indices as inputs in the latter
case. These indices are obtained through pixel classification of
ground-to-sky images. The input-output structure of the neural
network models is selected using evolutionary computation
methods.
Global solar radiation (SR) influences the majority of living
beings in many different ways. In the particular case of
interest it affects greatly the energy consumption patterns of
society. Having reasonably good predictions of SR evolution
within a given future horizon is a key factor for many practical
applications where the minimization of energy consumption
constitutes an objective. A review of applications in renewable
energy systems based on SR forecasting can be found in
[1]. Many environmental control strategies in buildings are
expressed as optimization problems where at least two goals
need to be met: the minimization of energy consumption
and the optimization of some criteria inherent to the activity
in the building or its occupants. Consider, for example,
heating ventilation and air conditioner (HVAC) systems in
building compartments [2]: one wants maximize the level of
thermal comfort with minimal energy consumption. In [3]
it is shown that the temperature estimation of a particular
compartment depends mostly on SR. Another example are
production greenhouses: the grower wants to achieve good
temperature and humidity regulation at the smallest energy costs
possible while maximizing crop growth and production. These
are greatly dependent on temperature and SR integrals. In both
examples the influence of SR on the processes is twofold: it
affects the controlled variables and the process being optimized.
Previous work, showed that the predicted SR time series
obtained, have a very smooth behaviour capturing the series
trend within the prediction horizon. However, in cloudy days
the models fail to capture the dynamics caused by cloudiness,
partially because the models use only measured past SR values
as inputs. In order to obtain more accurate SR predictions,
information on cloudiness should be fed to the models. In this
work cloudiness information is incorporated in the Artificial
Neural Network (ANN) SR model with the aim of obtaining
better predictions in the presence of cloud activity. Non-
linear Auto-regressive (NAR) and Non-linear Auto-regressive
with Exogenous inputs (NARX) models are compared. In the
later case a cloudiness index is the only exogenous input. The
comparison will give insight about the usefulness of cloudiness
indices to the SR models.
The models employed in the NAR and NARX are based on
the Radial Basis Function Neural Networks (RBFNN). The feed-
forward RBFNN was chosen for its structural simplicity, the
universal approximation property [4], the nonlinear nature of
the time series to be modelled, and also due to its tolerance
to imprecision and noise. The training process employed can
be found in detail in [5], [6]. The model structure, witch are,
the lagged inputs variables terms and the number of neurons
are achivied using the method described in [7], [8], [9], [10]
and is based in an evolutionary computing approach, or, in a
more concrete matter, the Multi-Objective Genetic Algorithm
(MOGA) [11], [12].
In order to train the RBFNN models, a data set are required.
Real data were obtained from a acquisition system installed on
the roof of the Faculty of Sciences and Technology building
Solar radiation
NARX NAR
Sky clean
c)
b)
Solar radiation
NARX
Solar radiation
NAR
a)
^
SR^
SR
SR
^
SR^
SRSR PCS
PCSPCS
PCS
Fig. 1. NAR AND NARX MODELS
of the University of Algarve, Portugal. It consists of a
solar radiation sensor, measuring the global solar radiation in
Watts×m−2, and a Total Sky Imager (TSI), that takes pictures
of the sky using a CCD camera. The data is stored in a
local computer with a period of 1 minute. Once collected,
the sky images are processed in order to obtain the cloudiness
indices (as percentages). This process consists a pixel-by-pixel
classification, involving also a RBFNN, witch in this particular
process behaves as a RBFNN classifier.
For prediction, three different models were used. The first
is a NAR solar radiation model, illustrated in Figure 1 a). This
model only requires the SR past value for one-step-ahead (OSA)
prediction, while for the multi-step predictions, requires that the
predicted values (SˆR) need to be feed in the inputs of the NAR.
Secondly a NARX model as depicted in Figure 1 b), incorporates
PCS values as exogenous inputs. Finally, as this model requires
PCS predicted values in order to perform long term multi-step
predictions, a third NAR model is considered for PCS as shown
in Figure 1 c).
For each NAR and NARX model presented in Figures 1, one
MOGA run was computed (please consult [7], [8], [9], [10] for a
more detailed explanation of the method), and chosen an optimal
model structure from a candidate set returned by the MOGA.
This candidate set obtained are RBFNN models that meet pre-
specified objectives parameters. In general, performance and
model complexity objectives were specified.
The results of the experimental setup demonstrated that
the use of the cloudiness indices can be beneficial for solar
radiation prediction. However, in terms of practical applicability,
preliminary results are not completely satisfactory. The error
introduced by the required cloudiness index estimated values,
limits the accuracy of the NARX solar radiation predictions,
in such way that the results are comparable to those obtained
by NAR solar radiation models. In order to overcome
this limitation, the cloudiness index predictive models need
improvement in terms of accuracy and dynamics. This can be
obtained through better pixel classification of the sky images,
which is ongoing research. Future work will address the use
of an alternative radial basis function (multi-quadric) which
in recent tests showed improvements in the cloudiness indices
models.
REFERENCES
[1] S. A. Kalogirou, “Artificial neural networks in renewable energy systems
applications: a review,” Renewable and Sustainable Energy Reviews,
vol. 5, pp. 373–401, 2001.
[2] E. M. Crispim, M. D. Martins, and A. E. Ruano, “Model comparison
for temperature estimation inside buildings,” in IEEE International
Workshop on Soft Computing Applications, Szeged-Hungary and Arad-
Romania, 2005.
[3] A. E. Ruano, E.M. Crispim, E.Z.E Conceic¸a˜o, and M. M. Lu´c¸io,
“Prediction of building’s temperature using neural network models,”
Energy & Buildings, Elsevier, 2005, In press.
[4] F. Girosi and T. Poggio, “Networks and the best approximation
property,” Biological Cybernetics, vol. 63, pp. 169–176, 1990.
[5] P. M. Ferreira and A. E. Ruano, “Exploiting the separability of
linear and nonlinear parameters in radial basis function networks,”
in IEEE Symposium 2000: Adaptive Systems for Signal Processing,
Communications, and Control, Lake Louise, Alberta, Canada, 2000, pp.
321–326.
[6] P. M. Ferreira, E. A. Faria, and A. E. Ruano, “Neural network models in
greenhouse air temperature prediction,” Neurocomputing, Special Issue
on Engineering Applications of Neural Networks, vol. 43, no. 1, pp. 51–
75, 2001.
[7] P. M. Ferreira, A. E. Ruano, and C. M. Fonseca, “Genetic assisted
selection of rbf model structures for greenhouse inside air temperature
prediction,” in CCA 2003, IEEE Conference on Control Applications,
Istanbul, Turkey, 2003, pp. 23–25.
[8] P. M. Ferreira, A. E. Ruano, and C. M. Fonseca, “Evolutionary
multiobjective design of radial basis function networks for greenhouse
environmental control,” in 16th IFAC World Congress, Praga, Czech
Republic, 2005, pp. 4–8.
[9] P. M. Ferreira and A. E. Ruano, “Soft-computing methods in
greenhouse environmental and crop modelling,” in Proceedings of the
EFITA/WCCA2005 Conference, the 5th Conference of the European
Federation for Information Technology in Agriculture, Food and
Environment, and the 3th World Congress on Computers in Agriculture
and Natural Resources, Vila Real, Portugal, 2005, pp. 25–28.
[10] A. E. Ruano, P. M. Ferreira, and C. M. Fonseca, “An overview of
nonlinear identification and control with neural networks,” in Intelligent
Control Systems Using Computational Intelligence Techniques, A. E.
Ruano, Ed., 70, pp. 37–87. IEE Control Engineering, The Institution of
Electrical Engineers, United Kingdom, 2005.
[11] C. M. Fonseca and P. J. Fleming, “Non-linear system identification with
multiobjective genetic algorithms,” in Proceedings of the 13th IFAC
World Congress, San Francisco, California, USA, 1996, vol. C, pp. 187–
192.
[12] C. M. Fonseca and P. J. Flemming, “Multiobjective optimization and
multiple constraint handling with evolutionary algorithms i: A unified
formulation,” IEEE Transactions on System, Man and Cybernetics-Part
A: Systems and Humans, vol. 28, pp. 26–37, 1998.


https://sapientia.ualg.pt/bitstream/10400.1/2359/4/paper19.pdf

Prediction of the solar radiation using RBF neural networks and ground-to-sky images

Abstract

Similar works

Full text

Available Versions

Sapientia

Universidade do Algarve