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Abstract:Hybridizing chaotic evolutionary algorithms with support vector regression (SVR) to improve forecasting accuracy is a hot topic in electricity load forecasting. Trapping at local optima and premature convergence are critical shortcomings of the tabu search (TS) algorithm. This paper investigates potential improvements of the TS algorithm by applying quantum computing mechanics to enhance the search information sharing mechanism (tabu memory) to improve the forecasting accuracy. This article presents an SVR-based load forecasting model that integrates quantum behaviors and the TS algorithm with the support vector regression model (namely SVRQTS) to obtain a more satisfactory forecasting accuracy. Numerical examples demonstrate that the proposed model outperforms the alternatives.

Accurate electricity forecasting is still the critical issue in many energy management fields. The applications of hybrid novel algorithms with support vector regression (SVR) models to overcome the premature convergence problem and improve forecasting accuracy levels also deserve to be widely explored. This paper applies chaotic function and quantum computing concepts to address the embedded drawbacks including crossover and mutation operations of genetic algorithms. Then, this paper proposes a novel electricity load forecasting model by hybridizing chaotic function and quantum computing with GA in an SVR model (named SVRCQGA) to achieve more satisfactory forecasting accuracy levels. Experimental examples demonstrate that the proposed SVRCQGA model is superior to other competitive models.

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A particularly interesting phenomenon that has arisen through Internet community is the virtual world style of online game. This paper presents a research framework for identifying the influential factors of player's loyalty toward online games, with emphasis on player's loyalty for achieving commercial success. The main methodologies are canonical correlation analysis and stepwise regression analysis employing 245 questionnaires collected. As an important insight, this research reveals that motives of player's participation are significantly related to business operation and game design, and further have a positive effect upon player's loyalty – addictive behavior and flow experience. Under close observation of the sub-criteria factors, role-playing and achievement play crucial roles in the influencing player's engagement process. Contrarily, storyline and public phrase are less relevant to player's loyalty. Finally, this study discusses the managerial implications regarding these findings and future research avenues.

Since the implementation of the Belt and Road Initiative, China’s total amount of outward foreign direct investment (OFDI) has increased each year and this has caused its relationship with carbon emissions (CO2e) to receive great attention recently. Forecasting China’s CO2e accurately by considering the impact of OFDI is important since the government can use it to formulate an appropriate emissions plan to fulfill its carbon reduction commitments. Because the relationship between OFDI and CO2e has nonlinear characteristics, a nonlinear grey multivariable model with fractional-order accumulation (NFGM(1,N)) was proposed in this study. To enhance the prediction accuracy, an optimization process was used to determine the parameters. The outcomes of the variable fractional order showed that fractional-order accumulation can better extract the grey information hidden in the original data, which confirmed the principle of new information priority. The result of the power coefficient indicated a nonlinear relationship between the CO2e and OFDI. Based on the prediction performance, the prediction accuracy of the NFGM(1,N) model was proven to be superior to those of the ARMA model, linear regression model, the GM(1,1), GM(1,N), and FGM(1,N) models. The empirical results also revealed that OFDI increased the CO2e in China. The relationship between the CO2e and OFDI exhibits a U-shaped development based on further predictions for the 2018–2030 period. Finally, some suggestions for long-term CO2e reduction plans were provided in this paper.

The importance of this study in bridging the gap between existing research literature works by analysing the influence of green marketing and awareness of the green brand on customer satisfaction of mineral water products. The analysis adopted a quantitative and analytical approach by administering structured questionnaires. The questionnaire developed based on the objectives of the research and the analysis of the relevant literature on green marketing, green brand awareness, and customer satisfaction. The results revealed green marketing had no influence on customer satisfaction in the case of the Pristine 8 + bottled mineral water customers. However, it was found that green brand awareness has a positive influence on customer satisfaction. Green marketing and green brand awareness simultaneously have a positive influence on customer satisfaction of the Pristine 8 + bottled mineral water brand. This study expands the scientific literature by providing empirical evidence on green marketing, green brand awareness on customer satisfaction that also can use as a consideration that might help companies to make decisions that will allow them to surpass their competitors through green marketing and green brand awareness, and to meet their customer satisfaction.

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