Ayda Darvishan's research while affiliated with University of Houston and other places

Publications (7)

Article
In the recent years, the energy issue is known as one of the main entries for economic and social development of human. So the biomass fuels as one of the approaches for supplying energy become the attractive topic for investigation. The higher heating value (HHV) is a key parameter for evaluation of energy of biomasses; so in the present study, a...
Article
In this paper, a new prediction model for aggregated loads of buildings has been propose. Due to high correlation of prediction performance with related horizons and aggregated more customers, a new strategy is developed to provide a forecasting model based on high accuracy. While, consumption shape of a single users normally has low structure to b...
Article
Full-text available
In this work, we proposed a new prediction model based neural network (NN) and neuro-fuzzy systems. The mentioned model is applied over solar radiation signal which is used as new clean energy sources, recently. The proposed forecasting model is consists of two main steps as; feature selection and forecast engine. In first step the best candidate i...
Article
Full-text available
This paper presents an intelligent method based on Improved Particle Swarm Optimization (IPSO) to solve a unit commitment problem that takes into account the uncertainty in the demand. This uncertainty is included in the optimization problem as a joint chance constraint that bounds the minimum value of the probability to jointly meet the determinis...

Citations

... In recent years, deep learning has attracted many researchers for its wide application in various fields of research such as optimization-related problems, forecasting, and prediction-related problems, etc. [64][65][66][67][68]. It has also gradually been established as one of the significant techniques for an image processing task and computer vision such as image de-noising, image enhancement, image detection, identification of several diseases, and many more. ...
... Ozveren U [16] used ANN model for predicting the gross heating value of lignocellulosic fuels and reported that the ANN model achieved higher accuracy with minimum MAPE (4.20%) and RMSE (0.0460%) compared to nonlinear regression model and published correlations. Darvishan et al. [17] predicted HHV using multilayer perceptron (MLP) ANN method where proximate analysis data of 78 samples were used from published literature. They reported R 2 value for training and testing sets are [18]. ...
... On the other hand, evolving intelligent algorithms have been commonly used in recent years to forecast short-term load for EV charging, with their applicability verified in a large number of studies. Among them, intelligent algorithms such as neural networks are often used [34][35][36][37], support vector machine [38][39][40], and deep learning [41]. As shown in the preceding study, these studies do not account for changes in EV ownership, and thus are only applicable to short-term forecasting of EV charging load. ...
... In recent years, deep learning has attracted many researchers for its wide application in various fields of research such as optimization-related problems, forecasting, and prediction-related problems, etc. [64][65][66][67][68]. It has also gradually been established as one of the significant techniques for an image processing task and computer vision such as image de-noising, image enhancement, image detection, identification of several diseases, and many more. ...
... Conversely, the statistical approach improves the accuracy and minimizes the error of the model by managing the correlation mapping between input and output parameters (Ruhang 2016). Grey theory (Li and Zhang 2019), Regression Analysis (Doorga et al. 2019), Fuzzy method (Reza Parsaei et al. 2020), time-series method (Bigdeli et al. 2017) and machine learning (ML) ) are the different categories of statistical approach. The regression Auto Regressive Integrated Moving Average (ARIMA) techniques (Shadab et al. 2020), support vector regression (SVR) (Mohammadi and Aghashariatmadari 2020), Gaussian Progress Regression (GPR) (Sheng et al. 2018) and ML approach based models proved to be best in the recent years in terms of forecasting . ...
... Numerous researchers have examined an assortment of evolutionary optimization techniques, related to UC problem, in diverse dimensions. This paper proposes to present a complete review of the UC problem, integrated with numerous types of evolutionary optimization techniques like UC problem incorporated with GA [133]- [159], UC problem incorporated with PSO [160]- [170], UC problem incorporated with EA [171]- [177], UC problem incorporated with EP [178]- [183], UC problem incorporated with DE [184]- [190], UC problem incorporated with SFLA [191]- [193], UC problem incorporated with FA [194]- [199], UC problem incorporated with other evolutionary optimization techniques like BFA [200], FSA [201], [202] and CSA [203], UC problem incorporated with Hybrid evolutionary optimization techniques [204]- [244], in the subsequent sections. Distinctive features of the references, connected to evolutionary optimization techniques, are captured and showed in Table 4, including the year of the publication. ...