A Multistage Hybrid Deep Learning Model for Enhanced Solar Tracking

Solar tracking helps maximize the efficiency of solar applications, such as photovoltaic (PV) solar panels. In the recent past, machine learning (ML) techniques have been extensively used to implement automatic solar tracking. However, applying predictive models in solar trackers is a non-trivial task due to the randomness and non-linearity of meteorological data, limiting their ability to clearly represent the underlying data patterns. Most existing predictive models take a monolithic approach to addressing limitations related to meteorological data, thereby limiting their performance. Therefore, this paper proposes a deep hybrid learning (DHL) model to enhance solar tracking performance. Furthermore, the proposed model improves feature representation in the data by using combined normalization methods and conversion of numerical data to images. In a nutshell, the model integrates sine and cosine transformations (SCT) to reveal cyclical patterns in the data, sigmoid and minimum-maximum data transformations to scale the data to a Gaussian distribution, and Gramian Angular Fields (GAF) to convert tabular data into 2D image representation to take advantage of the feature extraction capability of a convolutional neural network (CNN). The model also utilizes long-term short-term memory (LSTM) and gated recurrent units (GRU) for both spatial and temporal feature extraction. The results show that the aggregation of the above-mentioned methods significantly enhances solar tracking. The proposed hybrid model outperforms existing methods on a publicly available dataset, achieving outstanding performance with MAE, MAPE, and RMSE scores of 0.0073, 1.4635, and 0.0097, respectively.