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

Crispim, E. M.

Ferreira, P. M.

Ruano, Antonio

Sapientia

International Journal of InnovativeComputing, Information and Control ICIC International c°2008 ISSN 1349-4198Volume 4, Number 5, May 2008 pp. 1121—1133PREDICTION OF THE SOLAR RADIATION EVOLUTION USINGCOMPUTATIONAL INTELLIGENCE TECHNIQUES ANDCLOUDINESS INDICESEduardo M. Crispim, Pedro M. Ferreira and Antonio E. RuanoCentre for Intelligent SystemsFaculty of Sciences and TechnologyUniversity of Algarve, Campus de Gambelas, 8005-139 Faro, Portugal{ ecrispim; pfrazao; aruano }@ualg.ptReceived February 2007; revised August 2007Abstract. In this paper, Artificial Neural Networks are applied for multi-step longterm solar radiation prediction. The input-output structure of the neural network modelsis selected using evolutionary computation methods. 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 models are compared, consid-ering cloudiness indices as inputs in the latter case. These indices are obtained throughpixel classification of ground-to-sky images, captured by a CCD camera.Keywords: Neural networks, Multi-objective genetic algorithms, Solar radiation, Cloudi-ness indices1. Introduction. Global solar radiation (SR) influences the majority of living beingsin many diﬀerent ways, which makes the accurate prediction of its evolution in timeimportant for several diﬀerent areas of application. One of the obvious areas of impactis the field of renewable energy (particularly solar energy) where a good prediction ofthe SR evolution enables eﬃcient sizing and performance improvement of stand-alonephotovoltaic systems [19, 22], and of hybrid power systems [4, 28]. A good review ofapplications in renewable energy systems, emphasising the use of SR forecasting, can befound in [16]. Another important area is people’s comfort in buildings, where possibleapplications are luminance control [29] and thermal comfort. Regarding the last case, ifthe influence of SR in the buildings temperature can be modelled accurately, more eﬃcientcontrol strategies for heating, ventilation and air-conditioner (HV AC) systems may beemployed, maintaining thermal comfort while minimizing the energy spent [3, 18, 27].In a total diﬀerent field, agriculture, SR plays an important role, for example, in thequantification of reference crop evapo-transpiration (needed in the calculus of crop waterrequirement), in irrigation scheduling or in water resource management [1, 13]. By thesame reasons presented above in the context of a person’s thermal comfort in buildings,the prediction of SR evolution allows more sophisticated climate control strategies whichenables maximized crop growth and production [17] whilst minimizing energy [7, 25].SR data is usually presented as a time series, for which there are no reliable statisticalassumptions that may be employed. This makes nonparametric methods, such as artificialneural networks (ANNs) or neuro-fuzzy systems, good tools for modelling it. For thisreason, several authors have employed these types of models for SR prediction [23, 24,30], to mention just a few. We are interested in SR models as long term predictorsto be employed in the predictive control of greenhouse climate and buildings thermalcomfort. In previous works [8, 9, 26], one-step ahead (OSA) non-linear auto-regressive1121

Prediction of the solar radiation evolution using computational intelligence techniques and cloudiness indices

Universidade do Algarve

International Journal of Innovative
Computing, Information and Control ICIC International c°2008 ISSN 1349-4198
Volume 4, Number 5, May 2008 pp. 1121—1133
PREDICTION OF THE SOLAR RADIATION EVOLUTION USING
COMPUTATIONAL INTELLIGENCE TECHNIQUES AND
CLOUDINESS INDICES
Eduardo M. Crispim, Pedro M. Ferreira and Antonio E. Ruano
Centre for Intelligent Systems
Faculty of Sciences and Technology
University of Algarve, Campus de Gambelas, 8005-139 Faro, Portugal
{ ecrispim; pfrazao; aruano }@ualg.pt
Received February 2007; revised August 2007
Abstract. In this paper, Artificial Neural Networks are applied for multi-step long
term solar radiation prediction. The input-output structure of the neural network models
is selected using evolutionary computation methods. 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 models are compared, consid-
ering cloudiness indices as inputs in the latter case. These indices are obtained through
pixel classification of ground-to-sky images, captured by a CCD camera.
Keywords: Neural networks, Multi-objective genetic algorithms, Solar radiation, Cloudi-
ness indices
1. Introduction. Global solar radiation (SR) influences the majority of living beings
in many diﬀerent ways, which makes the accurate prediction of its evolution in time
important for several diﬀerent areas of application. One of the obvious areas of impact
is the field of renewable energy (particularly solar energy) where a good prediction of
the SR evolution enables eﬃcient sizing and performance improvement of stand-alone
photovoltaic systems [19, 22], and of hybrid power systems [4, 28]. A good review of
applications in renewable energy systems, emphasising the use of SR forecasting, can be
found in [16]. Another important area is people’s comfort in buildings, where possible
applications are luminance control [29] and thermal comfort. Regarding the last case, if
the influence of SR in the buildings temperature can be modelled accurately, more eﬃcient
control strategies for heating, ventilation and air-conditioner (HV AC) systems may be
employed, maintaining thermal comfort while minimizing the energy spent [3, 18, 27].
In a total diﬀerent field, agriculture, SR plays an important role, for example, in the
quantification of reference crop evapo-transpiration (needed in the calculus of crop water
requirement), in irrigation scheduling or in water resource management [1, 13]. By the
same reasons presented above in the context of a person’s thermal comfort in buildings,
the prediction of SR evolution allows more sophisticated climate control strategies which
enables maximized crop growth and production [17] whilst minimizing energy [7, 25].
SR data is usually presented as a time series, for which there are no reliable statistical
assumptions that may be employed. This makes nonparametric methods, such as artificial
neural networks (ANNs) or neuro-fuzzy systems, good tools for modelling it. For this
reason, several authors have employed these types of models for SR prediction [23, 24,
30], to mention just a few. We are interested in SR models as long term predictors
to be employed in the predictive control of greenhouse climate and buildings thermal
comfort. In previous works [8, 9, 26], one-step ahead (OSA) non-linear auto-regressive
1121


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Prediction of the solar radiation evolution using computational intelligence techniques and cloudiness indices

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