Yazar "Ahmadi, Mikaeel" seçeneğine göre listele

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    A blueprint for sustainable electrification by designing and implementing pv systems in small scales
    (Springer Nature, 2024) Dinçer, Hasan; Ibrahimi, Abdul Matin; Ahmadi, Mikaeel; Danish, Mir Sayed Shah
    This chapter presents a comprehensive analysis of the planning, design, and implementation of photovoltaic (PV) systems, emphasizing their role in sustainable rural electrification and renewable energy integration. The chapter begins by examining the integration of solar energy into the electricity market, highlighting its contribution to energy security and climate change mitigation. It deals with the challenges and dynamics of incorporating distributed energy resources, with a special focus on solar PV systems. The chapter methodically explores the planning and design aspects of PV systems, considering factors like site location, climatic conditions, and grid connectivity. A case study on electrifying a rural community provides practical insights into the application of these principles. This chapter further details the components, specifications, and costs of PV systems, presenting exhaustive tables and guidelines for implementation. It also includes calculations and estimations essential for system balance and optimization, covering environmental, technical, and economic aspects. The chapter concludes with a discussion of lessons learned and provides a comprehensive conclusion, synthesizing the key findings and implications of the study for future renewable energy projects.
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    Data-driven pathways to sustainable energy solutions
    (Springer Nature, 2024) Danish, Mir Sayed Shah; Ahmadi, Mikaeel; Ibrahimi, Abdul Matin; Dinçer, Hasan; Zahra, Shirmohammadi; Khosravy, Mahdi; Senjyu, Tomonobu
    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.