Forecasting solar irradiance with hybrid classical–quantum models: A comprehensive evaluation of deep learning and quantum-enhanced techniques.

Predicting solar irradiance has proven to be a challenging task due to its inherently unpredictable and chaotic characteristics. Although machine learning and deep learning models have demonstrated considerable success in this field, the emergence of quantum computing brings a new layer of potential to this complex problem. This paper delves into these emerging methodologies, focusing specifically on their application in the domain of solar irradiance forecasting. This study investigates the integration of quantum layers into a conventional deep feedforward neural network (FFN) and the development of a fully connected quantum neural network (QNN). A bidirectional long short-term memory (BiLSTM) model is employed as a performance benchmark. Among the various models developed within this study, a few have demonstrated notable performance. For example, an FFN composed of five regular layers and two quantum layers (FFN5L2Q), having a total parameter count of 339, generated an error rate of 6.737%. Conversely, another FFN model, featuring eight regular layers and one quantum layer (FFN8L1Q) and having a total parameter count of 5551, exhibited superior performance with an error rate of 4.254%. When compared to the BiLSTM model, which has 49,666 parameters and an error rate of 3.875%, these quantum-enhanced FFNs display robust and competitive performance. This study, therefore, emphasizes the promising prospects of quantum-integrated techniques in augmenting the precision of solar irradiance prediction models. • Quantum computing boosts solar irradiance predictions. • Quantum layers in Feed-Forward Networks is promising. • Hybrid model with two quantum layers achieves 6.737% error. • 8-layer Hybrid model with one quantum layer hits 4.254% error. • Quantum-enhanced model architecture impacts effectiveness. [ABSTRACT FROM AUTHOR]