Hamid Samadi
@unisa.it
Engineering
University of Salerno
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Silvia Licciardi, Guido Ala, Elisa Francomano, Pietro Catrini, Maurizio La Villetta, Rossano Musca, Antonio Piacentino, Eleonora Riva Sanseverino, and Hamid Samadi
IEEE
The research is focused on implementing neural network architectures in the field of Deep Learning for various applications involving energy context. In particular, recurrent neural networks (RNN) of type Long Short Term Memory (LSTM) have been studied for the classification of signals and are being upgraded, with particular attention to the augmentation of the dataset in order to obtain a wider ability of generalization of the results from the obtained nets, with suitable hyperparameters, choice of the more effective layers and relative options of training.
Nikta Shamsmohammadi, Hamid Samadi, Mohammad Rahimzadeh, Zohreh Asadi, and Davood Domiri Ganji
Elsevier BV
Hamid Samadi, Guido Ala, Valerio Lo Brano, Pietro Romano, and Fabio Viola
MDPI AG
Integrating photovoltaic technology has undergone significant development in recent years, owing to its manifold advantages. One of the most recent domains where this technology has found application is within the transportation sector. The utilization of this technology possesses the potential to initiate a new revolution in transportation by enhancing the range and reducing fuel consumption in vehicles. The performance of these systems is influenced by numerous factors, which have been thoroughly examined in this study. These factors have been categorized into three broad groups: installation site, solar cell characteristics, and environmental conditions. It is important to note that these conditions are inherently interdependent, so this study reveals that the radiation incident on the roof of a van can be 1.09–3.85 times greater than the radiation incident on its sides, according to varying meteorological conditions and different seasons. The current research serves as a valuable foundation for future investigations in this field, offering a targeted and practical overview of the work conducted thus far and summarizing the current state of research.
H. Samadi, M.J. Hosseini, A.A. Ranjbar, and Y. Pahamli
Elsevier BV
Guido Ala, Pietro Catrini, Mariano G. Ippolito, Maurizio La Villetta, Silvia Licciardi, Rossano Musca, Antonio Piacentino, Eleonora Riva Sanseverino, and Hamid Samadi
IEEE
Deep learning is becoming ever more significant for potential applications in the field of smart grid and the energy transition paradigm. Energy production forecasting from renewables and real time monitoring of the smart grid, are just two examples of how advanced neural network architectures can find wide and useful use in these fields. Due to the presence of volatile production and of new consumption profiles arising from electric vehicles recharge and from the interaction with other energy vectors, more efficient forecasting tools are needed to ensure balancing services or effective self consumption in energy districts. The present paper implements deep learning techniques which can be used for these purposes. In particular, training of neural networks of LSTM type for the classification of electrical signals have been compared and CNN-LSTM and ConvLSTM hybrid techniques for power production prevision by PV plants have been proposed.
Hamid Samadi, Guido Ala, Valerio Lo Brano, Pietro Romano, Fabio Viola, and Rosario Miceli
IEEE
Reducing the number of charging times and increasing the range of electric vehicles are the main advantages of solar cell integration in vehicles' bodies. The present work has reviewed and introduced the available methods for integrating different types of solar cells in the body of vehicles. After examining the efficiency of the most used solar cells in vehicle integrated photovoltaic (VIPV) field, including III-V, thin film, crystalline silicon, and transparent, these methods were examined separately for different cells. Various methods are introduced in this paper, such as direct integration, use of concentrator structure, modification of module structure and use of alternative materials, miniaturization, and change of cell layout. With a sufficient understanding of each solar cell's potential and their integration methods into the vehicle body, it can be designed high-efficiency VIPV systems and increase the coverage ratio of the vehicle's body with photovoltaic cells.
Hamid Samadi, Nikta Shams Mohammadi, Mohammad Shamoushaki, Zohreh Asadi, and Davood Domiri Ganji
Elsevier BV
Mohammad Rahimzadeh, Hamid Samadi, and Nikta Shams Mohammadi
MDPI AG
Environmental energy harvesting is a major operation in research and industries. Currently, researchers have started analyzing small-scale energy scavengers for the supply of energy in low-power electrical appliances. One area of interest is the use of piezoelectric materials, especially in the presence of mechanical vibrations. This study analyzed a unimorph cantilever beam in different modes by evaluating the effects of various parameters, such as geometry, piezoelectric material, lengths of layers, and the proof mass to the energy harvesting process. The finite element method was employed for analysis. The proposed model was designed and simulated in COMSOL Multiphysics, and the output parameters, i.e., natural frequencies and the output voltage, were then evaluated. The results suggested a considerable effect of geometrical and physical parameters on the energy harvesters and could lead to designing devices with a higher functional efficiency.