A Comprehensive Analysis of Recent Advances in Deep Learning based Solar Irradiance Forecasting

Abstract

Powering remote mobile communication towers is essential for establishing reliable communication. If the backup diesel generators in the remote mobile towers are replaced by renewable energy source such as solar PV system, the environmental pollution is reduced. Solar irradiance (SI) forecast is crucial for effective planning and operation of solar energy systems. Deep learning (DL) algorithms have become an optimistic method for predicting SI in recent years owing to their ability for handling intricate non-linear relationships and sizable data sets. This study reviews the most prominent Deep learning models (DLM) for predicting SI, which include Convolution Neural Networks (CNNs), Long Short term Memory (LSTM), Deep Belief Networks (DBNs), Recurrent Neural Networks (RNNs) and Hybrid Models. The study discusses each model's benefits and drawbacks as well as how well it was applied to different SI predicting issues. The review also highlights the importance of carefully selecting the model architecture and hyperparameters, and the training data quantity and quality, for the success of DLM for solar irradiance forecasting. The models are compared based on their accuracy and efficiency in predicting solar irradiance for different time horizons, from few seconds to several hours. The study provides insights into the suitability of different DLM for solar irradiance forecasting and outlining the future paths in the area of DL for solar irradiance predictions.