Short-term wind speed forecasting has a significant influence on enhancing the operation efficiency and increasing the economic benefits of wind power generation systems. A substantial number of wind speed forecasting...
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Short-term wind speed forecasting has a significant influence on enhancing the operation efficiency and increasing the economic benefits of wind power generation systems. A substantial number of wind speed forecasting models, which are aimed at improving the forecasting performance, have been proposed. However, some conventional forecasting models do not consider the necessity and importance of data preprocessing. Moreover, they neglect the limitations of individual forecasting models, leading to poor forecasting accuracy. In this study, a novel model combining a data preprocessing technique, forecasting algorithms, an advanced optimization algorithm, and no negative constraint theory is developed. This combined model successfully overcomes some limitations of the individual forecasting models and effectively improves the forecasting accuracy. To estimate the effectiveness of the proposed combined model, 10-min wind speed data from the wind farm in Peng Lai, China are used as case studies. The experiment results demonstrate that the developed combined model is definitely superior compared to all other conventional models. Furthermore, it can be used as an effective technique for smart grid planning.
Machine learning models hybridized with optimizationalgorithms have been applied to many real-life applications, including the prediction of water quality. However, the emergence of newly developed advancedalgorithm...
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Machine learning models hybridized with optimizationalgorithms have been applied to many real-life applications, including the prediction of water quality. However, the emergence of newly developed advancedalgorithms can provide new scopes and possibilities for further enhancements. In this study, the least-square support vector machine (LSSVM) integrated with advanced optimization algorithms is presented, for the first time, in the prediction of water quality index (WQI) at the Klang River of Malaysia. Thereafter, the LSSVM model using RBF kernel was optimized using the hybrid particle swarm optimization and genetic algorithm (HPSOGA), whale optimization based on self-adapting parameter adjustment and mix mutation strategy (SMWOA) as well as ameliorative moth-flame optimization (AMFO) separately. It was found that the SMWOA-LSSVM model had the better performance for WQI prediction by having the best achievement root means square error (RMSE), mean absolute error (MAE), coefficient of determination (R-2) and mean absolute percentage error (MAPE). Comprehensive comparison was done using the global performance indicator (GPI), whereby the SMWOA-LSSVM had the highest average score of 0.31. This could be attributed to the internal architecture of the SMWOA, which was catered to avoid local optima within short optimization period.
Short-term wind speed prediction is an indispensable part of the operation and control of wind energy power generation systems. However, many prediction models proposed by researchers did not preprocess the original d...
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Short-term wind speed prediction is an indispensable part of the operation and control of wind energy power generation systems. However, many prediction models proposed by researchers did not preprocess the original data or consider the limitations of a single prediction model, resulting in poor prediction accuracy. Based on the no-negative constraint theory, this study uses five neural networks with advanced optimization algorithms and data preprocessing to obtain high-precision prediction results. Four experiments were designed to test the effectiveness of the proposed model and four analysis strategies were used to discuss the experimental results. The empirical study used wind speed data from China. The results show that the MAPE and Std performance indicators in the multi-step prediction of the hybrid model are lower than in other benchmark models;the proposed model is far superior to comparable models in terms of accuracy and stability.
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