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M. Hadi Amini

Researcher at Florida International University

Publications -  133
Citations -  3298

M. Hadi Amini is an academic researcher from Florida International University. The author has contributed to research in topics: Smart grid & Electric power system. The author has an hindex of 27, co-authored 124 publications receiving 2306 citations. Previous affiliations of M. Hadi Amini include Joint Institute for Nuclear Research & University of Miami.

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Simultaneous allocation of electric vehicles’ parking lots and distributed renewable resources in smart power distribution networks

TL;DR: In this article, a two-stage approach for allocation of EV parking lots and distributed renewable resources (DRRs) in power distribution network is proposed, which considers both the economical benefits of parking lot investor and the technical constraints of distribution network operator.
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ARIMA-based decoupled time series forecasting of electric vehicle charging demand for stochastic power system operation

TL;DR: In this article, an autoregressive integrated moving average (ARIMA) method was proposed for demand forecasting of conventional electrical load and charging demand of EV (CDE) parking lots simultaneously.
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A simultaneous approach for optimal allocation of renewable energy sources and electric vehicle charging stations in smart grids based on improved GA-PSO algorithm

TL;DR: In this article, a multi-objective optimization problem is formulated to obtain objective variables in order to reduce power losses, voltage fluctuations, charging and demand supplying costs, and EV battery cost.
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A Survey on Federated Learning for Resource-Constrained IoT Devices

TL;DR: This survey article proposes to answer the question: how to train distributed machine learning models for resource-constrained IoT devices, and highlights an overview of FL and provides a comprehensive survey of the problem statements and emerging challenges.
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A novel multi-time-scale modeling for electric power demand forecasting: From short-term to medium-term horizon

TL;DR: A generalized technique for modeling historical load data in the form of time-series with different cycles of seasonality in a given power network, using the hourly-metered load data of PJM network as a real-world input dataset is presented.