Abstract:The accuracy of annual electric load forecasting plays an important role in the economic and social benefits of electric power systems. The least squares support vector machine (LSSVM) has been proven to offer strong potential in forecasting issues, particularly by employing an appropriate meta-heuristic algorithm to determine the values of its two parameters. However, these meta-heuristic algorithms have the drawbacks of being hard to understand and reaching the global optimal solution slowly. As a novel meta-heuristic and evolutionary algorithm, the fruit fly optimization algorithm (FOA) has the advantages of being easy to understand and fast convergence to the global optimal solution. Therefore, to improve the forecasting performance, this paper proposes a LSSVM-based annual electric load forecasting model that uses FOA to automatically determine the appropriate values of the two parameters for the LSSVM model. By taking the annual electricity consumption of China as an instance, the computational result shows that the LSSVM combined with FOA (LSSVM-FOA) outperforms other alternative methods, namely single LSSVM, LSSVM combined with coupled simulated annealing algorithm (LSSVM-CSA), generalized regression neural network (GRNN) and regression model.
Abstract:With the growing worldwide awareness of environmental protection and sustainable development, green purchasing has become an important issue for companies to gain environmental and developmental sustainability. Thermal power is the main power generation form in China, and the green supplier selection is essential to the smooth and sustainable construction of thermal power plants. Therefore, selecting the proper green supplier of thermal power equipment is very important to the company's sustainable development and the sustainability of China's electric power industry. In this paper, a hybrid fuzzy multi-attribute decision making approach (fuzzy entropy-TOPSIS) is proposed for selecting the best green supplier. The fuzzy set theory is applied to translate the linguistic preferences into triangular fuzzy numbers. The subjective criteria weights are determined by using decision makers' superiority linguistic ratings and the objective ones are determined by combining the superiority linguistic ratings and fuzzy-entropy weighting method. The fuzzy TOPSIS is employed to generate an overall performance score for each green supplier. An empirical green supplier selection is conducted to illustrate the effectiveness of this proposed fuzzy entropy-TOPSIS approach. This proposed fuzzy entropy-TOPSIS approach can select the proper green supplier of thermal power equipment, which contributes to promoting the company's sustainable development and the sustainability of China's electric power industry to some extent.
This study aimed at assessing the climatic factors influencing the wolfberry fruit morphology, and the composition of its nutritious metabolites. The cultivar Ningqi1, widely grown in Northwest China was collected from three typical ecological growing counties with contrasting climatic conditions: Ningxia Zhongning (NF), Xinjiang Jinghe (XF) and Qinghai Nomuhong (QF). During the ripening period, 45 fruits from different plantations at each location were sampled. A total of 393 metabolites were detected in all samples through the widely targeted metabolomics approach and grouped into 19 known classes. Fruits from QF were the biggest followed by those from XF and NF. The altitude, relative humidity and light intensity had negative and strong correlations with most of the metabolites, suggesting that growing wolfberry in very high altitudes and under high light intensity is detrimental for the fruit nutritional quality. Soil moisture content is highly and negatively correlated with vitamins, organic acids and carbohydrates while moderately and positively correlated with other classes of metabolites. In contrast, air and soil temperatures exhibited positive correlation with majority of the metabolites. Overall, our results suggest high soil and air temperatures, low altitude and light intensity and moderate soil moisture, as the suitable conditions to produce Lycium fruits with high content of nutritious metabolites.
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