It is essential to enhance the ability of wind speed forecasting for wind farm managers. The contribution of this research is to develop a novel hybrid method for multi-step ahead wind speed forecasting including empi...
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It is essential to enhance the ability of wind speed forecasting for wind farm managers. The contribution of this research is to develop a novel hybrid method for multi-step ahead wind speed forecasting including empirical wavelet transformation-Kullback-Leibler divergence, autoregressive fractionally integrated moving average and improved back-propagation neural network. The empirical wavelet transformation-Kullback-Leibler divergence is used to extract these valuable features of wind speed fluctuations. The autoregressive fractionally integrated moving average is utilized to extract the long memory characteristics and capture the linear fluctuations of wind speed. The improved back propagation neural network is established to capture the corresponding nonlinear fluctuations, its inputs and outputs are determined by phase space reconstruction and its weights and thresholds are optimized by a modified bat algorithm with cognition strategy. Three prediction cases are employed to test the developed model. The simulation results demonstrate that the developed model outperforms several common benchmark models. (C) 2019 Elsevier B.V. All rights reserved.
This paper suggests a new stochastic framework based on 2 m+1 point estimate method (PEM) to solve the mid-term generation scheduling (SMGS) problem. The new formulation makes use of an adaptive modified bat algorithm...
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This paper suggests a new stochastic framework based on 2 m+1 point estimate method (PEM) to solve the mid-term generation scheduling (SMGS) problem. The new formulation makes use of an adaptive modified bat algorithm and a novel self-adaptive wavelet mutation strategy for the establishment of new robust algorithm for the present problem. In addition, this work improves the modeling process of wind-thermal system in the MGS problem by considering the possible uncertainties when scheduling the generators of power system of the problem. The proposed model can concurrently capture the uncertainty effect of load and wind speed variations. The feasibility and efficiency of the proposed method is examined using two test systems.
In this paper, a realistic formulation for the non-convex economic dispatch problem is proposed. It considers different practical constraints including ramp rate limits, valve loading effect, prohibited operating zone...
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In this paper, a realistic formulation for the non-convex economic dispatch problem is proposed. It considers different practical constraints including ramp rate limits, valve loading effect, prohibited operating zones, spinning reserve and multi-fuel options. In this regard, a new optimization method based on the batalgorithm is proposed to solve the problem. Meanwhile, because the proposed problem is complex, nonlinear, and constrained, a new self-adaptive modification method, called modified BA or shortly MBA, is proposed. The satisfying performance of the proposed optimization method is examined using IEEE 15-unit, 40-unit and 100-unit test systems. Comparative studies demonstrate the consistent superiority of the proposed method over other alternative optimization techniques widely used in the literature for solving the economic dispatch problem.
In the chemical industry, fault diagnosis is a challenging task due to the complexity of chemical equipment. This paper proposes a machine learning-based approach to achieve the goal of fault diagnosis. First, in orde...
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In the chemical industry, fault diagnosis is a challenging task due to the complexity of chemical equipment. This paper proposes a machine learning-based approach to achieve the goal of fault diagnosis. First, in order to reduce the impact of redundant features, support vector machine recursive feature elimination (SVMRFE) is used to select important features. The trained probabilistic neural network (PNN) is then used for fault diagnosis. Considering that the diagnostic performance is affected by its hidden layer element smoothing factor (sigma), the modified bat algorithm (MBA) is used to optimize the PNN to obtain optimal global parameter values. The MBA adopts a better optimization mechanism than the basic algorithm and achieves excellent global convergence. It can globally optimize the smoothing factor, which effectively improves the fault diagnosis ability of the PNN. During the testing of the Tennessee Eastman (TE) process data set, we evaluate the performance of the proposed model by comparing the F-1-score and accuracy of the different methods. The charts provided describe the fault diagnostic results and classification for the different models. The results indicate that the MBA has a better optimization ability than other traditional optimization algorithms. At the same time, the combination method proposed in this paper is also superior to others and can significantly improve the accuracy of TE process fault diagnosis.
Plant growth is significantly dependent upon the combination and concentration of mineral nutrients in the soil, where the adequate supply of these nutrients is a severe issue in fulfilling the fundamental cellular pr...
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Plant growth is significantly dependent upon the combination and concentration of mineral nutrients in the soil, where the adequate supply of these nutrients is a severe issue in fulfilling the fundamental cellular process requirements. This study collected 22 physicochemical properties of tilled potato soils from eight stations in two Atlantic Canadian provinces (Prince Edward Island and New Brunswick). Along with the experimental investigation, an explainable dual pre-processing inspired-intelligent paradigm comprised of the SelectKbest feature selection (FS), modified bat algorithm (MBA), adaptive boosting (AdaBoost), and Shapley Additive Explanations (SHAP) explainer was designed to monitor the micronutrients, including copper (Cu) and zinc (Zn). The significant data predictors were filtered using the SelectKbest FS to monitor Cu and Zn. The MBA was coupled with AdaBoost for tuning the hyperparameters to ensure accurate predictions. To validate the outcomes of the MBAAdaBoost, four advanced machine learning approaches, including categorical boosting (CatBoost) coupled with MBA (MBA-CatBoost), classical AadaBoost, ridge kernel regression (KRR), and multivariate adaptive regression splines (MARS), were examined to compare the accuracies. The robustness of the developed framework and the performance of comparative models were examined through several statistical metrics. Results revealed that the MBA-CatBoost performed the best in predicting the Cu (R = 0.9425, U1 = 0.1020, and U22 = 0.2036) and Zn in the soil (R = 0.9454, U1 = 0.0942, and U2 = 0.1858) when compared with other models. Furthermore, the SHAP explainer interpreted the block-box main model during the training phase by introducing the CEC and Fe as significant predictors to monitor the soil Cu and Zn, respectively. These findings demonstrate a clear and robust relationship between the presented modeling approach and the accurate prediction of soil micronutrient concentrations. Accurate predictions of mi
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