Wind power forecasting techniques have been well developed over the last half-century. There has been a large number of research literature as well as review analyses. Over the past 5 decades, considerable advancements have been achieved in wind power forecasting. A large body of research literature has been produced, including review articles that have addressed various aspects of the subject. However, these reviews have predominantly utilized horizontal comparisons and have not conducted a comprehensive analysis of the research that has been undertaken. This survey aims to provide a systematic and analytical review of the technical progress made in wind power forecasting. To accomplish this goal, we conducted a knowledge map analysis of the wind power forecasting literature published in the Web of Science database over the last 2 decades. We examined the collaboration network and development context, analyzed publication volume, citation frequency, journal of publication, author, and institutional influence, and studied co-occurring and bursting keywords to reveal changing research hotspots. These hotspots aim to indicate the progress and challenges of current forecasting technologies, which is of great significance for promoting the development of forecasting technology. Based on our findings, we analyzed commonly used traditional machine learning and advanced deep learning methods in this field, such as classical neural networks, and recent Transformers, and discussed emerging technologies like large language models. We also provide quantitative analysis of the advantages, disadvantages, forecasting accuracy, and computational costs of these methods. Finally, some open research questions and trends related to this topic were discussed, which can help improve the understanding of various power forecasting methods. This survey paper provides valuable insights for wind power engineers.

Internal solitary waves (ISWs) can cause strong seafloor sediment resuspension and induce nepheloid layers in both shallow and deep-water environments. However, the roles of ISWs in the >1000 m deep sea sediment resuspension and seafloor geomorphic changes are still unclear. To answer the above question, in the Dongsha area of the northern South China Sea, we measured suspended particulate matter along with a section covering the entire continental slope between 300 and 2000 m water depths, together with high-resolution multibeam bathymetric data for examining geomorphic changes. The results indicate that, on the upper slope with water depth <700 m, seafloor sediments were heavily disturbed and resuspended. We find that ISWs could suspend seabed sediments and shape a bedform at water depths ≲1000 m. The maximum water depth of sediment resuspension by ISWs measured is found as deep as 1500 m. The distribution pattern of the seafloor surface sediments on the east of the Dongsha continental slope (fine and silty sand in 1500 m) also indicates that they are mainly controlled and impacted by ISWs. The wave refraction theory could be applied to the upper slope, but sediment resuspension is related to the seabed topography on the lower slope. Our study shows that the suspension and transport of sediments induced by episodic ISWs on the Dongsha slope of the northern South China Sea could shape the bedform and affect the sedimentary seabed geomorphology. This research will help explain the impacts of the ISWs on the deep-water sediment resuspension and seafloor geopmorphic changes along with the continental slope in the margin sea.

China implemented a strict lockdown policy to prevent the spread of COVID-19 in the worst-affected regions, including Wuhan and Shanghai. This study aims to investigate impact of these lockdowns on air quality index (AQI) using a deep learning framework. In addition to historical pollutant concentrations and meteorological factors, we incorporate social and spatio-temporal influences in the framework. In particular, spatial autocorrelation (SAC), which combines temporal autocorrelation with spatial correlation, is adopted to reflect the influence of neighbouring cities and historical data. Our deep learning analysis obtained the estimates of the lockdown effects as − 25.88 in Wuhan and − 20.47 in Shanghai. The corresponding prediction errors are reduced by about 47% for Wuhan and by 67% for Shanghai, which enables much more reliable AQI forecasts for both cities.