Main challenges (generation and returned energy) in a deep intelligent analysis technique for renewable energy applications.

Abstract

In recent years, there has been an increasing demand for Renewable Energy (RE), which refers to energy generated from natural sources such as solar and wind power. Consequently, numerous scientific studies have been conducted to explore various approaches for controlling this type of energy. This work aims to highlight the main challenges associated with the generation and return of RE by employing intelligent data analysis techniques, specifically deep learning. These challenges are examined from different perspectives, including pre-processing, the methodology and techniques used in deep learning, and the evaluation measures employed. Some of the research in this area is focused on predicting the highest amount of energy that can be generated at a particular time and location, while others aim to predict the largest amount of electrical energy that can be returned to the electricity grid to optimize the use of surplus RE resources and maximize their benefits. These efforts are crucial to ensure the effective and continuous operation of the electrical grid. However, despite the efficiency and high accuracy of these models, they are hindered by complex calculations that require considerable time to produce the desired outcomes. Additionally, numerous measures are employed to evaluate the models' performance, including assessing their completion rate, quality of performance, accuracy of results, efficiency, error rate, feasibility of investing in RE, and the largest amount of surplus energy that can be returned to the electricity generation network.