Data-driven pathways to sustainable energy solutions - Danish, Mir Sayed Shah; Ahmadi, Mikaeel; Ibrahimi, Abdul Matin; Dinçer, Hasan; Zahra, Shirmohammadi; Khosravy, Mahdi; Senjyu, Tomonobu (Springer Nature, 2024)
In the rapidly evolving world of the energy sector, harnessing the power of neural networks and machine learning becomes crucial. This chapter deals with the intricate dimensions of datasets, delineating their types, structures, and classifications that are particularly relevant to energy-related applications. A meticulous exploration of data processing techniques, emphasizing preparation, transformation, labeling, and augmentation, is presented. Additionally, a comparative analysis of optimization algorithms clarifies their role in refining energy-focused models. The complexities, computation times, and accuracies of these optimizers are highlighted. Furthermore, the importance of hyperparameters, their optimal configurations, and the significance of adept tuning are underscored. Serving as a comprehensive guide, this chapter aims to bridge the knowledge gap of stakeholders in the energy domain, providing actionable insights into best practices for data-driven decision-making processes.
Keywords: Sustainable Energy Solutions; Energy-Related Applications; Data