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Spatiotemporal assessment of solar potential is one of the most promising solutions to find suitable locations for future photovoltaic systems' placement. However, accurate assessment of solar potential on large areas can be challenging due to missing data or computational complexity. In this paper, a fully convolutional neural network based method for high-resolution spatiotemporal assessment of solar potential by using remote sensing data is presented. The method is trained and validated on the area of 32 km2 of the Maribor city, Slovenia, and tested on the 6 different locations in Slovenia and Germany. The proposed method was tested against the simulation algorithm, which utilized isotropic Liu Jordan diffuse model or anisotropic Perez model with sky view factor based shading. On average, a normalized root mean square error (NRMSE) of 6.06% and mean absolute percentage error (MAPE) of 4.29% was achieved in test locations against the simulation based on Liu-Jordan model. When training the fully convolutional neural network models against the ground truth, generated with more advanced Perez diffuse model, the average NRMSE was 8.37% and MAPE of 10.90% was achieved across all test locations. Additionally, it was shown that the proposed method can assess solar potential in high-resolution from the input in lower resolution with higher accuracy than the simulation algorithm, while being up to 1500-times faster. [ABSTRACT FROM AUTHOR]

Spatiotemporal assessment of solar potential is one of the most promising solutions to find suitable locations for future photovoltaic systems' placement. However, accurate assessment of solar potential on large areas can be challenging due to missing data or computational complexity. In this paper, a fully convolutional neural network based method for high-resolution spatiotemporal assessment of solar potential by using remote sensing data is presented. The method is trained and validated on the area of 32 km2 of the Maribor city, Slovenia, and tested on the 6 different locations in Slovenia and Germany. The proposed method was tested against the simulation algorithm, which utilized isotropic Liu Jordan diffuse model or anisotropic Perez model with sky view factor based shading. On average, a normalized root mean square error (NRMSE) of 6.06% and mean absolute percentage error (MAPE) of 4.29% was achieved in test locations against the simulation based on Liu-Jordan model. When training the fully convolutional neural network models against the ground truth, generated with more advanced Perez diffuse model, the average NRMSE was 8.37% and MAPE of 10.90% was achieved across all test locations. Additionally, it was shown that the proposed method can assess solar potential in high-resolution from the input in lower resolution with higher accuracy than the simulation algorithm, while being up to 1500-times faster. [ABSTRACT FROM AUTHOR]

Under the dual stimulus of the new energy demand and the increasing competitiveness of wind energy, the construction of wind speed prediction models began to be placed in a position that cannot be ignored. To overcome the challenges brought by wind speed fluctuations to wind speed forecasting, this paper proposes a novel hybrid wind speed forecasting deep model. The model has three modules, including data preprocessing, multi-learner ensemble, and adaptive multiple error correction. We used four real wind series in Xinjiang, China to verify the performance of the model. The results of the case study show that: (a) The proposed hybrid deep model for wind speed forecasting is superior to several state-of-the-art models in terms of both forecasting stability and forecasting accuracy; (b) The proposed hybrid deep model is excellent in multi-step forecasting, taking the site #1 as an example, the MAEs of the proposed model are 0.0250 m/s, 0.0417 m/s, and 0.0570 m/s, respectively. • A hybrid framework based on deep learning is proposed for wind speed forecasting. • The data preprocessing method reduces the instability of wind speed data. • The multi-learner ensemble method integrates the advantages of deep learning models. • An adaptive multiple error correction method is developed. [ABSTRACT FROM AUTHOR]

The estimation of the electrical power output and energy yield of high concentration photovoltaic (HCPV) modules is a hard task because of the many parameters involved. In this work, we propose the usage of an Artificial Neural Networks (ANN) method to estimate the maximum power output of a FLATCON® CPV-module manufactured by Fraunhofer ISE. The advantage of the ANN is that it is a part of the Artificial Intelligence Deep Learning domain, which makes it easy to model complex systems. A detailed knowledge about the underlying module technology is not necessary. In this work, various meteorological parameters are tested as inputs of the ANN including spectral matching ratios for considering the spectral variation of the solar irradiance to evaluate the maximum power output of the HCPV module under study. Eleven scenarios considering different combinations of input parameters have been investigated. It is demonstrated that the ANN gives excellent results and allows for an accurate prediction of the HCPV module's instantaneous power output with only a few amount of data used on training the ANN. The model has been tested using the measured data set of a FLATCON® CPV-module located on the rooftop of the Fraunhofer ISE in Freiburg, Germany. The module has been electrically characterized outdoors in the period from April 2013 to April 2014. The accuracy of the proposed ANN approach was analyzed using this measurement data in combination with error metrics. It was found that the best agreement between the measured and the predicted maximum power output was achieved when using direct normal irradiance, wind speed, ambient temperature and spectral matching ratios as input. The normalized root mean square error for the maximum power output over one year is found to be between 2.2 and 4.6%. The deviation of the modelled energy yield to the measured one is in the range of 0.2–2.2%. • We use ANN method to predict the maximum power output of a FLATCON® CPV-module. • The accuracy of model used was validated through measurements and metrics errors. • The spectral effect of DNI through the use of SMRs has improved the model performance. • The knowledge of module technology is not necessary for performance prediction. • Only small amount of data are needed for performance prediction using this approach. Short Abstract: This paper uses the artificial Neural Networks model to estimate the electrical power and the energy yield of high concentration photovoltaic modules. The model has been verified using a whole year measurement data of power output of FLATCON CPV Module manufactured by Fraunhofer ISE, Germany. A best agreement between the measured and computed energy output was found trough the analysis of eleven scenarios where different input parameters were combined. [ABSTRACT FROM AUTHOR]