Sky image-based solar irradiance prediction methodologies using artificial neural networks

In order to decelerate global warming, it is important to promote renewable energy technologies. Solar energy, which is one of the most promising renewable energy sources, can be converted into electricity by using photovoltaic power generation systems. Whether the photovoltaic power generation systems are connected to an electrical grid or not, predicting near-future global solar radiation is useful to balance electricity supply and demand. In this work, two methodologies utilizing artificial neural networks (ANNs) to predict global horizontal irradiance in 1 to 5 minutes in advance from sky images are proposed. These methodologies do not require cloud detection techniques. Sky photo image data have been used to detect the clouds in the existing techniques, while color information at limited number of sampling points in the images are used in the proposed methodologies. The proposed methodologies are able to capture the trends of fluctuating solar irradiance with minor discrepancies. The minimum root mean square errors of 143 W/m2, which are comparable with the existing prediction techniques, are achieved for both of the methodologies. At the same time, the proposed methodologies require much less image data to be handled compared to the existing techniques

Performance of a small-scale solar cogeneration system in the equatorial zone of Malaysia

Solar energy is one of the most promising renewable energy sources in the equatorial zone of Malaysia. The availability of moderate solar energy during the year makes it possible to be used as a heat source for a solar thermal system. In order to determine the potential application of the solar thermal system, a feasibility study of a small-scale solar cogeneration system under measured solar radiation condition is conducted. In this work, the concept design of a 50-kW solar cogeneration system is proposed which consists of a parabolic trough collector with aperture area of 1154 m2, thermal storage system and an Organic Rankine Cycle power conversion system. The performance of the system was simulated using the measured direct normal irradiance during the months of May, June and July in 2018. A net gross electric power of 6373 kWh, 6201 kWh and 5155 kWh were produced with the total monthly available solar energy of 82,809 kWh, 77,592 kWh and 64,239 kWh for May, June and July, respectively. The efficiency of the solar to electric was around 8% and was in good agreement with the findings in the literature. The results for the solar cogeneration system can serve as an evaluation reference for the application of solar thermal system in the moderate solar radiation localities in Malaysia

Solar Radiation Forecast Using Cloud Velocity for Photovoltaic Systems

Today, solar energy is used in a many different ways. One of the most popular technological developments for this purpose is photovoltaic conversion to electricity. However, power fluctuations due to the variability of solar energy are one of the challenges faced by the implementation of photovoltaic systems. To overcome this problem, forecasting solar radiation data several minutes in advance is needed. In this research, a methodology to forecast solar radiation using cloud velocity and cloud moving angle is proposed. Generally, a red-to-blue ratio (RBR) color model and correlation analysis are used for obtaining the cloud velocity and moving angle. Artificial neural network (ANN) forecast models with different input combinations are established. This methodology requires lower computational time since it only uses part of the pixels in the sky image. Based on R-squared analysis, it can be concluded that the ANN model with inputs of cloud velocity and moving angle and average solar radiation showed the highest accuracy among other combinations of inputs. The R-squared value was 0.59 with only a relatively small sample size of 42. The proposed model showed a highest improvement of 75.79% when compared to the ANN model based on historical solar radiation data only

Cloud optical depth retrieval via sky’s infrared image for solar radiation prediction

Photovoltaic (PV) system is developed to harness solar energy as an alternative energy to reduce the dependency on fossil fuel energy. However, the output of the PV system is not stable due to the fluctuation of solar radiation. Hence, solar radiation prediction in advanced is needed to make sure the tap changer in PV system has enough time to respond. In this research, the cloud base temperature is identified from the sky’s thermal image. From the cloud base temperature, cloud optical depth (COD) is calculated. Artificial neural network (ANN) models are established by using different combinations of current solar radiation and COD to predict the solar radiation several minutes in advanced. R-squared value is used to measure the accuracy of the models. For prediction in advanced for every minute, with COD as input, always show the highest R-squared value. The highest R-squared value is 0.8899 for the prediction for 1 minute in advanced and dropped to 0.5415 as the minute of prediction in advanced increase to 5. This shows that the proposed methodology is suitable for prediction of solar radiation for short term in advanced

Cloud optical depth retrieval via sky\u27s infrared image for solar radiation prediction

Photovoltaic (PV) system is developed to harness solar energy as an alternative energy to reduce the dependency on fossil fuel energy. However, the output of the PV system is not stable due to the fluctuation of solar radiation. Hence, solar radiation prediction in advanced is needed to make sure the tap changer in PV system has enough time to respond. In this research, the cloud base temperature is identified from the sky\u27s thermal image. From the cloud base temperature, cloud optical depth (COD) is calculated. Artificial neural network (ANN) models are established by using different combinations of current solar radiation and COD to predict the solar radiation several minutes in advanced. R-squared value is used to measure the accuracy of the models. For prediction in advanced for every minute, with COD as input, always show the highest R-squared value. The highest R-squared value is 0.8899 for the prediction for 1 minute in advanced and dropped to 0.5415 as the minute of prediction in advanced increase to 5. This shows that the proposed methodology is suitable for prediction of solar radiation for short term in advanced. © 2019 Penerbit Akademia Baru