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Research involving international research communities has been advocated in the field of behavior analysis (Dymond et al., 2000; Martin et al., 2016). The purpose of the present study was to report the status of behavioral research in mainland China, Hong Kong, and Taiwan, in terms of number of publications, types of research, and frequency of collaboration with international researchers. Fifteen behavioral journals were selected from the list by Cooper et al. (2020). These were searched by hand to find publications conducted in or authored by researchers from mainland China, Hong Kong, and Taiwan dating from each journal's inception to December 2021. The earliest publication we found appeared in 1980 in The Psychological Record. Over the following four decades (1980–1989; 1990–1999; 2000–2009; 2010–2021), the number of publications per decade increased dramatically and continues in recent years to rise. Publications include research reports, review papers, and conceptual articles, with the majority being basic research reports published in Behavioral Processes. Approximately half the publications involve collaboration with international researchers, mostly in North America. Implications for behavioral research, practice, and policy in mainland China, Hong Kong, and Taiwan are discussed. [ABSTRACT FROM AUTHOR]

With the increasing penetration of renewable energy, the traditional energy storage operation based on individual framework —users own and operate independently mode may become impracticable due to conflicting interests, insufficient utilization, and poor interoperability. Riding on the wave of the proliferation of sharing economy, storage energy sharing expands the existing storage energy without requiring costly and time-consuming infrastructure investments. However, the development path of shared energy storage (SES) mode is not clear due to the asymmetric decision-making of the owners of energy storage systems under bounded rationality. In this paper, the diffusion of the business model of SES among multiple renewable energy stations (the owners, RES) and its key factors are analyzed based on the evolutionary game. The goal is to maximize social welfare and ensure the continuous growth of the sharing market. The numerical simulation is carried out based on the data from northwest China to interpret the development path. The result shows that, in renewable energy cluster the stations with intermittent output or with the higher prediction accuracy are more willing to participate in sharing. The energy storage sharing mode fails when the energy storage capacity ratio of RES is less than 10%. While the high-level ratio (more than 30%) is not conducive to the diffusion of the sharing model in RESs with low power generation prediction accuracy. [Display omitted] [ABSTRACT FROM AUTHOR]

Wind power is just in place for a major improvement to the power grid in security and economy. More accurate forecast methods are emerged to benefit grid operators and their customers. A wind speed behavior prediction method based on multi-feature and multi-scale integrated learning (MFMS) is developed in this paper. The behavior characteristics are proposed to solve the problem of the temporal and spatial feature extraction distinctly of wind speed. Multi-scale feature fusion and spatial pyramid pooling algorithms are proposed to reduce the loss of micro-space-scale and short-time-scale information. Meanwhile, environmental features and background features are applied to consider the influence of the overall wind farm environment on the wind speed of local wind turbines. The wind speed data of sixteen wind turbines in a wind farm at Zhangjiakou in North China are used to verify the method. Results show that MAPE, RMSE and R2 of 4-h ultra-short-term wind speed prediction are 6.164%, 0.275 and 0.966, respectively, which are improved by 1.786%–6.757%, 0.051–0.216 and 0.014–0.074 compared with other methods. The method proposed in this paper can be applied to wind speed prediction and provide a guideline for wind farm power prediction. • A multi-step wind speed prediction model of wind turbine is proposed. • Meteorological factors and wind behavior were used as background/foreground feature. • Features are deeply excavated with the shallow feature loss been overcome. • The model efficiency and accuracy is validated by wind data from a wind farm. • The model shows high accuracy in ultra-short-term wind speed prediction. [ABSTRACT FROM AUTHOR]