Author
Noradin Ghadimi
Bio: Noradin Ghadimi is an academic researcher from Islamic Azad University. The author has contributed to research in topics: Electric power system & Distributed generation. The author has an hindex of 50, co-authored 127 publications receiving 6319 citations.
Papers
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TL;DR: A new prediction model for small scale load prediction i.e., buildings or sites is outlined, based on improved version of empirical mode decomposition (EMD) which is called sliding window EMD (SWEMD), a new feature selection algorithm and hybrid forecast engine.
335 citations
TL;DR: This work proposes a robust optimization approach for uncertainty modeling of cooling demand in order to obtain robust chiller loading in the uncertain environment which cooling demand is supplied by multi-chiller system.
329 citations
TL;DR: The MOPSO method has been used for management and optimal distribution of energy resources in proposed micro-grid and the problem was analyzed with the NSGA-II algorithm to demonstrate the efficiency of the proposed method.
327 citations
TL;DR: Short term power forecast of wind and solar power is proposed to evaluate the available output power of each production component and includes a feature selection filter and hybrid forecast engine based on neural network and an intelligent evolutionary algorithm.
Abstract: In this paper short term power forecast of wind and solar power is proposed to evaluate the available output power of each production component. In this model, lead acid batteries used in proposed hybrid power system based on wind-solar power system. So, before the predicting of power output, a simple mathematical approach to simulate the lead–acid battery behaviors in stand-alone hybrid wind-solar power generation systems will be introduced. Then, the proposed forecast problem will be evaluated which is taken as constraint status through state of charge (SOC) of the batteries. The proposed forecast model includes a feature selection filter and hybrid forecast engine based on neural network (NN) and an intelligent evolutionary algorithm. This method not only could maintain the SOC of batteries in suitable range, but also could decrease the on-or-off switching number of wind turbines and PV modules. Effectiveness of the proposed method has been applied over real world engineering data. Obtained numerical analysis, demonstrate the validity of proposed method.
312 citations
TL;DR: A conflict bi-objective model for cost-emission based operation of industrial consumer in the presence of peak load management is proposed and fuzzy decision making approach is provided to select the trade-off solution from the Pareto solutions.
285 citations
Cited by
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TL;DR: In this article, the authors present a review of recent optimization methods applied to solve the problem of placement and sizing of distributed generation units from renewable energy sources based on a classification of the most recent and highly cited papers.
345 citations
TL;DR: A new prediction model for small scale load prediction i.e., buildings or sites is outlined, based on improved version of empirical mode decomposition (EMD) which is called sliding window EMD (SWEMD), a new feature selection algorithm and hybrid forecast engine.
335 citations
TL;DR: This work proposes a robust optimization approach for uncertainty modeling of cooling demand in order to obtain robust chiller loading in the uncertain environment which cooling demand is supplied by multi-chiller system.
329 citations
TL;DR: The MOPSO method has been used for management and optimal distribution of energy resources in proposed micro-grid and the problem was analyzed with the NSGA-II algorithm to demonstrate the efficiency of the proposed method.
327 citations
TL;DR: Short term power forecast of wind and solar power is proposed to evaluate the available output power of each production component and includes a feature selection filter and hybrid forecast engine based on neural network and an intelligent evolutionary algorithm.
Abstract: In this paper short term power forecast of wind and solar power is proposed to evaluate the available output power of each production component. In this model, lead acid batteries used in proposed hybrid power system based on wind-solar power system. So, before the predicting of power output, a simple mathematical approach to simulate the lead–acid battery behaviors in stand-alone hybrid wind-solar power generation systems will be introduced. Then, the proposed forecast problem will be evaluated which is taken as constraint status through state of charge (SOC) of the batteries. The proposed forecast model includes a feature selection filter and hybrid forecast engine based on neural network (NN) and an intelligent evolutionary algorithm. This method not only could maintain the SOC of batteries in suitable range, but also could decrease the on-or-off switching number of wind turbines and PV modules. Effectiveness of the proposed method has been applied over real world engineering data. Obtained numerical analysis, demonstrate the validity of proposed method.
312 citations