AN ARTIFICIAL NEURAL NETWORK BASED APPROACH FOR ESTIMATING DIRECT NORMAL, DIFFUSE HORIZONTAL AND GLOBAL HORIZONTAL IRRADIANCES USING SATELLITE IMAGES

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1 AN ARTIFICIAL NEURAL NETWORK BASED APPROACH FOR ESTIMATING DIRECT NORMAL, DIFFUSE HORIZONTAL AND GLOBAL HORIZONTAL IRRADIANCES USING SATELLITE IMAGES Yehia Eissa Prashanth R. Marpu Hosni Ghedira Taha B.M.J. Ouarda Matteo Chiesa Research Center for Renewable Energy Mapping and Assessment Masdar Institute, PO Box 54224, Abu Dhabi, UAE ABSTRACT This study proposes the use of an artificial neural network approach to estimate the direct normal irradiance (DNI), diffuse horizontal irradiance (DHI) and global horizontal irradiance (GHI) at temporal and spatial resolutions of 15min and 3km, respectively. Inputs to the models are six thermal channels of the SEVIRI instrument, onboard Meteosat Second Generation, along with solar zenith angle, latitude, longitude, solar time, day number and eccentricity correction. The study will show the generalization of the results when using an ensemble approach as opposed to a single network. For all sky conditions the testing dataset for DNI estimations have relative root mean square error (rrmse) and relative mean bias error (rmbe) values of 17.8% and -3%, respectively. Results for DHI estimations are 13.4% and +1.6%, respectively, and finally GHI estimation results show error values of 7.3% and -1.7%, respectively. Keywords: solar resource assessment, solar maps, neural networks, satellite images 1. INTRODUCTION Direct normal irradiance (DNI), diffuse horizontal irradiance (DHI) and global horizontal irradiance (GHI) assessments are valuable in a number of solar applications ranging from concentrating solar power (CSP) plants to photovoltaic (PV) plants to other heating, cooling and daylight applications [1]. Satellite-based models for solar resource assessments provide estimates in locations where no ground measurements are available, still ground measurements are necessary for model calibration and validations. Vignola et al. [2] tested the model of Perez et al. [3] for GHI, DNI and DHI assessments using an independent dataset of 1-year hourly ground measurements from Kimberly, Idaho. Their results show relative root mean square error (rrmse) values of 12.5%, 40.9% and 54.2% for the GHI, DNI and DHI, respectively, and relative mean bias error (rmbe) values of -4.9%, +2% and +15.4%, respectively. Model of Schillings et al. [4] was tested over eight stations in Saudi Arabia and for hourly DNI estimates, the model shows an rrmse of 36.1% and an rmbe of +4.3 [5]. Rigollier et al. [6] have shown that results of Heliosat, a satellite-based model, are location dependent. Accurate assessments of solar radiation through satellitebased models assist decision makers and stakeholders in choosing locations and designing solar powered plants. Moreover, near real-time assessments are capable of monitoring solar radiation remotely. On the other hand, inaccurate assessments cause financial troubles for the stakeholders. In the United Arab Emirates (UAE), characterized by high aerosol concentrations and high humidity in the vicinity of the coast, inaccurate satellitebased DNI assessments for Shams 1, a 100MW CSP plant, meant the plant would require an increase in the number of 1

2 Fig. 1: Flowchart of the ANN ensemble to estimate the DNI, DHI and GHI. mirrors to achieve its target capacity. Luckily, the extra financial burdens were within the margin of error [7]. An artificial neural network (ANN) for DNI estimation has shown promising results in Eissa et al. [8]. This study proposes the use of an ANN approach to estimate the DNI, DHI and GHI through employment of SEVIRI thermal channels, onboard Meteosat Second Generation. Inputs to this model are six thermal channels of SEVIRI, which are the T04 (3.9μm), T05 (6.2μm), T06 (7.3μm), T07 (8.7μm), T09 (10.8μm) and T10 (12.0μm) channels. Additional inputs to the model are the solar zenith angle (θ Z ), latitude, longitude, solar time, day number and eccentricity correction (ε). The model is trained using data collected from three ground measurement stations in the UAE for the year 2009, and is capable of estimating the DNI, DHI and GHI at the spatial and temporal resolutions of the SEVIRI instrument, 15min and 3km, respectively. This study will show the better generalization of the results when using an ANN ensemble approach as opposed to a single network. 2. METHODOLOGY 2.1 ANN Approach Feed forward neural networks [9] are employed in this study, where the scaled conjugate gradients algorithm [10] is used for training. In order to avoid overfitting of the data, the sample is split into a training dataset, used to compute and update the synaptic weights, and a validation dataset, used to avoid overfitting by monitoring the validation error during the training process. The testing dataset is an independent dataset used in assessing the trained ANN. The network architecture in this work consisted of an input layer, 2 hidden layers comprising of 25 neurons each and an output layer. In the literature, an ensemble approach rather than a single weak learner has shown to provide a better generalization [11, 12]. In this work, an ensemble of 100 networks is chosen with the training and validation data selected randomly for every network from the same dataset. The final result is obtained by finding the median of the results of the individual networks after removing the outliers based on skewness. The skewness of the distribution of the results obtained by the networks in the ensemble is calculated and the top or bottom 10% values are removed depending on whether the value of skewness is positive or negative, respectively. This way the outliers are discarded. No values are discarded when the absolute value of the skewness is less than Flowchart of the Model The flowchart of the process is shown in Fig. 1. First, a pixel is classified as either cloud-free or cloudy through a cloud mask that employs the T09 and T10 channels of SEVIRI [13], then the T09 and T10 channels are combined with T04, T05, T06 and T07 channels along with θ Z, latitude, longitude, solar time, day number and ε to estimate 2

3 the total optical depth of the atmosphere (δ) and the DHI. It is assumed that δ comprises of all the factors causing solar radiation attenuation. The obtained δ is then used in the Beer-Bouguer-Lambert law, Eq. (1), to estimate the DNI [14], DNI I 0 exp( m), (1) where I 0 is the solar constant of 1,367W/m 2 and m is the air mass [15]. The model is then able to calculate the GHI from the DNI and DHI estimates using Eq. (2), GHI DNI cos Z DHI. (2) The final products have temporal and spatial resolutions of 15min and 3km, respectively. 3. EXPERIMENTAL SETUP 3.1 Ground Measurements The measurements of DNI, DHI and GHI obtained every 10 minutes are available for three ground stations in the UAE for the year These measurements were collected by a Rotating Shadowband Pyranometer (RSP). The locations of the stations are illustrated in the map shown in Fig. 2. Station 1 is a near-coastal station, station 2 is in the middle of the desert and station 3 is closer to the border of Oman, where the altitude is higher than sea level. 3.2 Satellite Data Images of the six thermal channels (T04, T05, T06, T07, T09 and T10) of the SEVIRI instrument are available for the year 2009 and are converted into brightness temperature. 3.3 Time and Location Parameters The solar zenith angle (θ Z ), solar time and eccentricity correction (ε) are computed for each pixel; details on how they are computed could be found in Duffie and Beckman [16], while latitude, longitude and day number are collected for each pixel. 3.3 Filtering and Dividing the Dataset Scenes containing erroneous brightness temperature values are filtered out, along with scenes where θ Z is greater than 70 as indicated in Rigollier et al. [17]. The dataset is Fig. 2: Location of the three stations used in this study. then divided between cloud-free and cloudy sky conditions. The cloud-free sky dataset made up 88% of all observations, due to the modest cloud cover over the UAE. Each dataset is then split into two parts, one for training and validation of the ANN ensemble and the other is kept as an independent dataset to test the model. Days in the cloud-free sky dataset were randomly split into 80% for training and validation and 20% for testing. That finally made up 13,167 observations for training and validation and 3,620 for testing. Due to the smaller size of the cloudy sky dataset, all observations were randomly split into 80% for training and validation and 20% for testing. That finally made up 1,838 observations for training and validation and 459 observations for testing. 4. RESULTS AND DISCUSSION 4.1 DNI Estimation Cloud-free sky testing dataset shows rrmse and rmbe values of 16.6% and -2.8%, respectively, while the cloudy sky testing dataset show 28.2% and -5.2%, respectively. For DNI estimation, the cloud-free sky results are more accurate than those for the cloudy sky. The estimated δ for the cloudy observations now includes the optical depth of clouds along with the optical depth of all other atmospheric constituents, which might be causing the increased error. Combining the results of the cloud-free and cloudy datasets results in rrmse and rmbe values of 17.8% and -3%, respectively. Fig. 3 shows the rrmse and the rmbe values for each of the 100 ANNs, along with the results of the ANN ensemble. This clearly shows the increased accuracy and more stability of the model when using an ensemble approach as opposed 3

4 to using a single ANN which could have varying results as shown. Moreover, the ensemble approach avoids outliers such as ANN #26 in Fig. 3a and ANN #20 in Fig. 3c. Fig. 4 shows the scatter plots of the estimated DNI along with the error versus the ground measured DNI values. The error is the estimated DNI minus the ground measured DNI, therefore negative values are underestimations and vice versa. The error plots for the cloud-free sky conditions in Fig. 4b show that the higher DNI values are systematically underestimated. This might be due to the lower number of observations of such high DNI values when training the ANNs. The error plot for the cloudy sky conditions in Fig. 4d generally shows a trend of overestimating some of the lower DNI values and underestimation the higher ones. 4.2 DHI Estimation Cloud-free sky testing dataset has rrmse and rmbe values of 13.7% and +1.9%, respectively, while those for the cloudy sky testing dataset are 11.8% and -0.7%, respectively. DHI results are more accurate than those of the DNI. The reason behind this may be due to the measurements of the sensor onboard the satellite, which measures the radiation emitted by the surface of the Earth and the constituents of the atmosphere. Also, unlike the DNI case, the DHI results for the cloudy sky are more accurate than those for the cloud-free sky. The explanation behind this is that the DHI reaching the surface under cloudy conditions is highly correlated with the radiation emitted by the clouds under such conditions. Combining the results of the cloud-free and cloudy datasets results in rrmse and rmbe values of 13.4% and +1.6%, respectively. GHI estimates are based on the final DNI and DHI results. Cloud-free sky testing dataset shows rrmse and rmbe values of 6.3% and -1.5%, respectively, while those for the cloudy sky testing dataset are 13.2% and -3.9%, respectively. Combining the results of the cloud-free and cloudy datasets results in rrmse and rmbe values of 7.3% and -1.7%. Fig. 7 shows the scatter plots of the estimated GHI, along with the error versus the ground measured GHI values. The points on the scatter plot for the cloud-free sky case in Fig. 7a shows a strong fit with respect to the diagonal line. The lower DHI values which are overestimated along with the higher DNI values which are underestimated combined together produce GHI results with a lower rrmse than both cases and a small underestimation overall. The cloudy sky results show lower accuracy due to the relatively higher rrmse and rmbe of the cloudy sky DNI estimations. Fig. 5 shows the rrmse and the rmbe values for each of the 100 ANNs, along with the results of the ANN ensemble. Similar to the DNI case, this clearly shows the increased accuracy and more stability of the model when using an ensemble approach as opposed to using a single ANN, which could have varying results as shown. Fig. 6 shows the scatter plots of the estimated DHI along with the error versus the ground measured DHI values. The error plots for the cloud-free sky conditions in Fig. 6b generally show a slight overestimation of the DHI estimates; still it shows a strong fit with the diagonal line in Fig. 6a. The scatter plot for the cloudy sky conditions in Fig. 6c generally shows a strong fit to the diagonal line and the error plot in Fig. 6d shows a slight overestimation in the lower DHI values and slight underestimation in the higher DHI values. 4.3 GHI Estimation 4

5 Fig. 3: Error of each network along with that of the ensemble for DNI estimation: cloud-free sky rrmse (a) and rmbe (b), cloudy sky rrmse (c) and rmbe (d). Fig. 4: Scatter plots of estimated versus ground measured DNI along with the error versus ground measured DNI: cloud-free sky case (a) and (b), cloudy case (c) and (d). 5

6 Fig. 5: Error of each network along with that of the ensemble for DHI estimation: cloud-free sky rrmse (a) and rmbe (b), cloudy sky rrmse (c) and rmbe (d). Fig. 6: Scatter plots of estimated versus ground measured DHI along with the error versus ground measured DHI: cloud-free sky case (a) and (b), cloudy case (c) and (d). 6

7 Fig. 7: Scatter plots of estimated versus ground measured GHI along with the error versus ground measured GHI: cloud-free sky case (a) and (b), cloudy case (c) and (d). 5. CONCLUSION After classifying the pixels as cloud-free or cloudy, the proposed model employs six thermal channels of the SEVIRI instrument along with the solar zenith angle, latitude, longitude, solar time, day number and eccentricity correction to train an ANN ensemble for DNI and DHI estimations. The GHI is then computed from those estimates. The presented results show the advantage of using an ANN ensemble as opposed to using a single ANN. At temporal and spatial resolutions of 15min and 3km, respectively, for all sky conditions the testing dataset for DNI estimations have rrmse and rmbe values of 17.8% and -3%. Results for DHI estimations are 13.4% and +1.6%, respectively, and finally GHI estimation results have values of 7.3% and -1.7%, respectively. This model could be applied to derive yearly irradiation maps and it could also be used to simulate SEVIRI images on a near real-time basis for remote monitoring of solar radiation. 6. REFERENCES (1) Hammer, A., Heinemann, D., Hoyer, C., Kuhlemann, R., Lorenz, E., Müller, R., Beyer, H.G., Solar Energy Assessment using Remote Sensing Technologies, Remote Sensing of Environment, 86, , 2003 (2) Vignola, F., Harlan, P., Perez, R., Kmiecik, M., Analysis of Satellite Derived Beam and Global Solar Radiation Data, Solar Energy, 81, , 2007 (3) Perez, R., Ineichen, P., Moore, K., Kmiecik, M., Chain, C., George, R., Vignola, F., A New Operational Model for Satellite-derived Irradiances: Description and Validation, Solar Energy, 73, , 2002 (4) Schillings, C., Mannstein, H., Meyer, R., Operational Method for Deriving High Resolution Direct Normal Irradiance from Satellite Data, Solar Energy, 76, , 2004 (5) Schillings, C., Meyer, R., Mannstein, H., Validation of a Method for Deriving High Resolution Direct Normal Irradiance from Satellite Data and Application for the Arabian Peninsula, Solar Energy, 76, , 2004 (6) Rigollier, C., Lefèvre, M., Wald, L., The Method Heliosat-2 for deriving Shortwave Solar Radiation from Satellite Images, Solar Energy, 77, , 2004 (7) Hashem, H., Solar Slip-ups: Shams 1, CSP Today, Jan 2, 2012 (8) Eissa, Y., Marpu, P. R., Gherboudj, I., Ghedira, H., Ouarda, T. B.M.J., Chiesa, M., Estimation of Direct Normal Irradiance from Meteosat SEVIRI Thermal Channels using a Neural Network Ensemble, Submitted, 2012 (9) Bishop, C. M., Pattern Recognition and Machine Learning, Springer, 2006 (10) Moller, M. F., A Scaled Conjugate Gradient Algorithm for Fast Supervised Learning, Neural Networks, 6, , 1993 (11) Dietterich, T., Ensemble Methods in Machine 7

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