Application of Machine Learning in Multi-Directional Model to Follow Solar Energy Using Photo Sensor Matrix - P. Dhanalakshmi; V. Venkatesh; P. S. Ranjit; N. Hemalatha; S. Divyapriya; R. Sandhiya; Sumit Kushwaha; Asmita Marathe; Mekete Asmare Huluka (International Journal of Photoenergy)
In this paper, we introduce a deep neural network (DNN) for forecasting the intra-day solar irradiance, photovoltaic PV plants, regardless of whether or not they have energy storage, can benefit from the work being done here. The proposed DNN utilises a number of different methodologies, two of which are cloud motion analysis and machine learning, in order to make forecasts regarding the climatological conditions of the future. In addition to this, the accuracy of the model was evaluated in light of the data sources that were easily accessible. In general, four different cases have been investigated. According to the findings, the DNN is capable of making more accurate and reliable predictions of the incoming solar irradiance than the persistent algorithm. This is the case across the board. Even without any actual data, the proposed model is considered to be state-of-the-art because it outperforms the current NWP forecasts for the same time horizon as those forecasts. When making predictions for the short term, using actual data to reduce the margin of error can be helpful. When making predictions for the long term, however, weather information can be beneficial.