Author
Farzaneh Mirzapour
Bio: Farzaneh Mirzapour is an academic researcher from Shahid Bahonar University of Kerman. The author has contributed to research in topics: State of charge & Solar power. The author has an hindex of 1, co-authored 1 publications receiving 216 citations.
Papers
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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
Cited by
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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: 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
TL;DR: The proposed solution methodology uses linear programming along with Mixed Integer Genetic Algorithm (MIGA) to minimize the payment cost and different custom-designed functions have been added to the basic MIGA to decrease the solution time.
265 citations
TL;DR: A ridgelet transform is applied to a wind signal to decompose it into sub-signals and the output of ridgelettransform is considered as input of new feature selection to identify the best candidates to be used as the forecast engine input.
254 citations
TL;DR: This study combined support vector machine and improved dragonfly algorithm to forecast short-term wind power for a hybrid prediction model and has shown better prediction performance compared with the other models such as back propagation neural network and Gaussian process regression.
231 citations