Developing a Deep Learning and Reliable Optimization Techniques for Solar Photovoltaic Power Prediction
Kumaraswamy Shivashankaraiah, Gadigi Shashibhushan, Arumugam Prema Kirubakaran, C. Anjanappa, Pradosh Kumar Sharma, Ishwarya Mayiladuthurai Vaidyanathan, Syed Noeman Taqui, Sami Al Obaid, Saleh Alfarraj
Abstract
Due to its significant solar energy generation, solar PV power facilities have recently been utilized. Although PV power stations are highly preferable, the state’s fundamental drawback is that its output current qualities are unpredictable. Consequently, to ensure a balance and complete functioning, it would be crucial to build systems that allow accurate future projections of solar PV production in the short or medium term. Research postulates a strategy for using deep learning to estimate the short-term electricity generated by solar photovoltaic facilities. This study offers a novel method for predicting photovoltaic systems output power utilizing a Hybrid Deep Neural Network framework, making significant advancements in the field of deep learning applications to transmission system prediction issues. CNN and LSTM are combined in the postulated HDNN paradigm. Traditional deep learning techniques are employed in the initial stage. The effectiveness assessments of these techniques are instead presented in greater detail after they have been trained using firefly optimization techniques. The method with the highest reliability is chosen out of all the techniques used in previous research. Deep learning and power efficiency create a combination that appears to have a successful future, predominantlyin improving sustainable management and the digitalization of the electrical sector.