Intelligent model for solar energy forecasting and its implementation for solar photovoltaic applications

As the demand for renewable energy is surging day-by-day, the solar energy data are important for applications in the field of solar photovoltaic (PV) systems. However, there exists a challenge in the collection of data owing to expensive instruments and a limited number of meteorological stations. In addition, the output of the system is largely affected due to variation in sky-conditions; therefore, an intelligent model based on sky-conditions is essential for estimating global solar energy so as to meet the energy requirements. In this work, the sky-based model employing fuzzy logic modelling has been developed and presented to forecast global solar energy using the dew-point as the meteorological parameter along with other known available parameters, namely, sunshine duration, wind speed, ambient temperature, and relative humidity for different sky-conditions, namely, clear sky (type-a), hazy sky (type-b), partially foggy/cloudy sky (type-c), and fully foggy/cloudy sky (type-d) respectively. Simulations have been performed for five meteorological stations across India that represents distinct climate zones such as composite, warm and humid, hot and dry, cold and cloudy, and moderate climate zone respectively, and the performance of the proposed model has been evaluated by using statistical indicators. The applicability of the proposed sky-based model employing fuzzy logic modelling can further be exploited for solar PV systems. The model is implemented in 210W PV modules in forecasting the power output of solar photovoltaic systems in different sky-conditions. The obtained results reveal that the systems employing fuzzy logic modelling can be implemented for a wide range of applications and provide benefits. Furthermore, to check for accuracy of the proposed model, a comparative analysis has been carried out with the Angstrom model using statistical indicators. The value of the results, however, shows the supremacy of the proposed fuzzy logic prediction model. Published by AIP Publishing.