Analysis and Selection of Optimal Perovskite/Silicon Tandem Configuration for Building Integrated Photovoltaics Based on Their Annual Outdoor Energy Yield Predicted by Machine Learning

Abstract

Accurate estimation of annual output energy yield () for perovskite/silicon (PSK/cSi) tandem solar cells is pivotal in assessing their suitability for building‐integrated photovoltaics (BIPV). This study pioneers five machine learning models of ensembles of trees, Gaussian process regressions, regression trees, support vector machines, and artificial neural networks (ANN) to predict output power density and compute for 2T, 3T, and 4T PSK/cSi tandem configurations in Japan's outdoor conditions. Seven predictive inputs of visible‐light solar irradiance, near‐infrared‐light solar irradiance, incident solar spectrum angle, solar module temperature, perovskite thickness, perovskite bandgap, and terminal of tandem configuration (T) drive the machine learning models. These models optimize predictions using k‐fold cross‐validation and Bayesian algorithms, showcasing superior precision in prediction compared to prior models. The ANN model emerges as the best model, displaying the minimal error in predicting , used to estimate across five Japanese locations (Gifu, Naganuma, Okinoerabu, Tosu, and Tsukuba). Results from these locations in blue‐rich solar spectrum zones identify the 4T PSK/cSi tandem configuration, featuring the most outstanding mean maximal (93.63, 263.02, 153.59, and 91.75 kWh m−2 for the east, rooftop, south, and west directions), as the prime candidate for BIPV applications.