The progress in marine meteorology research achieved by scientists in China during the four-year period from 2003 to 2006 is summarized under four categories： marine disaster study, typhoon over the ocean, ocean-atmo...The progress in marine meteorology research achieved by scientists in China during the four-year period from 2003 to 2006 is summarized under four categories： marine disaster study, typhoon over the ocean, ocean-atmosphere monitoring technology, and ocean-atmosphere forecasting technology. Compared to the previous four years, many more first-hand datasets have been obtained and more scientific issues have been addressed. In particular, many contributions have been made by young scientists. A brief statement on the research strategy of marine meteorology in China for the coming years is given at the end.展开更多
Forecasting uncertainties among meteorological fields have long been recognized as the main limitation on the accuracy and predictability of air quality forecasts.However,the particular impact of meteorological foreca...Forecasting uncertainties among meteorological fields have long been recognized as the main limitation on the accuracy and predictability of air quality forecasts.However,the particular impact of meteorological forecasting uncertainties on air quality forecasts specific to different seasons is still not well known.In this study,a series of forecasts with different forecast lead times for January,April,July,and October of 2018 are conducted over the Beijing-Tianjin-Hebei(BTH)region and the impacts of meteorological forecasting uncertainties on surface PM_(2.5)concentration forecasts with each lead time are investigated.With increased lead time,the forecasted PM_(2.5)concentrations significantly change and demonstrate obvious seasonal variations.In general,the forecasting uncertainties in monthly mean surface PM_(2.5)concentrations in the BTH region due to lead time are the largest(80%)in spring,followed by autumn(~50%),summer(~40%),and winter(20%).In winter,the forecasting uncertainties in total surface PM_(2.5)mass due to lead time are mainly due to the uncertainties in PBL heights and hence the PBL mixing of anthropogenic primary particles.In spring,the forecasting uncertainties are mainly from the impacts of lead time on lower-tropospheric northwesterly winds,thereby further enhancing the condensation production of anthropogenic secondary particles by the long-range transport of natural dust.In summer,the forecasting uncertainties result mainly from the decrease in dry and wet deposition rates,which are associated with the reduction of near-surface wind speed and precipitation rate.In autumn,the forecasting uncertainties arise mainly from the change in the transport of remote natural dust and anthropogenic particles,which is associated with changes in the large-scale circulation.展开更多
Electrical load forecasting is very crucial for electrical power systems’planning and operation.Both electrical buildings’load demand and meteorological datasets may contain hidden patterns that are required to be i...Electrical load forecasting is very crucial for electrical power systems’planning and operation.Both electrical buildings’load demand and meteorological datasets may contain hidden patterns that are required to be investigated and studied to show their potential impact on load forecasting.The meteorological data are analyzed in this study through different data mining techniques aiming to predict the electrical load demand of a factory located in Riyadh,Saudi Arabia.The factory load and meteorological data used in this study are recorded hourly between 2016 and 2017.These data are provided by King Abdullah City for Atomic and Renewable Energy and Saudi Electricity Company at a site located in Riyadh.After applying the data pre-processing techniques to prepare the data,different machine learning algorithms,namely Artificial Neural Network and Support Vector Regression(SVR),are applied and compared to predict the factory load.In addition,for the sake of selecting the optimal set of features,13 different combinations of features are investigated in this study.The outcomes of this study emphasize selecting the optimal set of features as more features may add complexity to the learning process.Finally,the SVR algorithm with six features provides the most accurate prediction values to predict the factory load.展开更多
为挖掘复杂环境因素对电力负荷预测效果的影响,提高电力负荷预测精确度,提出了一种基于k-shape时间序列聚类与STL季节趋势分解算法相结合的负荷曲线聚类预测模型(k-shape-seasonal and trend decomposition using loess-gradient boosti...为挖掘复杂环境因素对电力负荷预测效果的影响,提高电力负荷预测精确度,提出了一种基于k-shape时间序列聚类与STL季节趋势分解算法相结合的负荷曲线聚类预测模型(k-shape-seasonal and trend decomposition using loess-gradient boosting decision tree,k-shape-STL-GBDT)。首先分析用户用电时序特征,利用k-shape时间序列聚类算法根据负荷曲线划分用户聚类,其次,使用STL算法将不同簇的负荷数据划分为季节项、趋势项与随机项。然后,结合温度、湿度等影响因素搭建预测模型,以麻省大学smart*可再生能源项目的公开数据集为例进行分析,并与多种主流聚类分解预测模型进行对比。结果表明新提出的模型框架MAPE减少了4%以上,针对短期负荷预测表现出了较好的性能与预测精度。展开更多
基金supported by the National Natural Science Foundation of China (Grant Nos.U0733002, 40506008 and 40576013)
文摘The progress in marine meteorology research achieved by scientists in China during the four-year period from 2003 to 2006 is summarized under four categories： marine disaster study, typhoon over the ocean, ocean-atmosphere monitoring technology, and ocean-atmosphere forecasting technology. Compared to the previous four years, many more first-hand datasets have been obtained and more scientific issues have been addressed. In particular, many contributions have been made by young scientists. A brief statement on the research strategy of marine meteorology in China for the coming years is given at the end.
基金supported by the National Key Research and Development Program of China(No.2022YFC3700701)National Natural Science Foundation of China(Grant Nos.41775146,42061134009)+1 种基金USTC Research Funds of the Double First-Class Initiative(YD2080002007)Strategic Priority Research Program of Chinese Academy of Sciences(XDB41000000).
文摘Forecasting uncertainties among meteorological fields have long been recognized as the main limitation on the accuracy and predictability of air quality forecasts.However,the particular impact of meteorological forecasting uncertainties on air quality forecasts specific to different seasons is still not well known.In this study,a series of forecasts with different forecast lead times for January,April,July,and October of 2018 are conducted over the Beijing-Tianjin-Hebei(BTH)region and the impacts of meteorological forecasting uncertainties on surface PM_(2.5)concentration forecasts with each lead time are investigated.With increased lead time,the forecasted PM_(2.5)concentrations significantly change and demonstrate obvious seasonal variations.In general,the forecasting uncertainties in monthly mean surface PM_(2.5)concentrations in the BTH region due to lead time are the largest(80%)in spring,followed by autumn(~50%),summer(~40%),and winter(20%).In winter,the forecasting uncertainties in total surface PM_(2.5)mass due to lead time are mainly due to the uncertainties in PBL heights and hence the PBL mixing of anthropogenic primary particles.In spring,the forecasting uncertainties are mainly from the impacts of lead time on lower-tropospheric northwesterly winds,thereby further enhancing the condensation production of anthropogenic secondary particles by the long-range transport of natural dust.In summer,the forecasting uncertainties result mainly from the decrease in dry and wet deposition rates,which are associated with the reduction of near-surface wind speed and precipitation rate.In autumn,the forecasting uncertainties arise mainly from the change in the transport of remote natural dust and anthropogenic particles,which is associated with changes in the large-scale circulation.
基金Funding Statement:The researchers would like to thank the Deanship of Scientific Research,Qassim University for funding the publication of this project.
文摘Electrical load forecasting is very crucial for electrical power systems’planning and operation.Both electrical buildings’load demand and meteorological datasets may contain hidden patterns that are required to be investigated and studied to show their potential impact on load forecasting.The meteorological data are analyzed in this study through different data mining techniques aiming to predict the electrical load demand of a factory located in Riyadh,Saudi Arabia.The factory load and meteorological data used in this study are recorded hourly between 2016 and 2017.These data are provided by King Abdullah City for Atomic and Renewable Energy and Saudi Electricity Company at a site located in Riyadh.After applying the data pre-processing techniques to prepare the data,different machine learning algorithms,namely Artificial Neural Network and Support Vector Regression(SVR),are applied and compared to predict the factory load.In addition,for the sake of selecting the optimal set of features,13 different combinations of features are investigated in this study.The outcomes of this study emphasize selecting the optimal set of features as more features may add complexity to the learning process.Finally,the SVR algorithm with six features provides the most accurate prediction values to predict the factory load.
文摘为挖掘复杂环境因素对电力负荷预测效果的影响,提高电力负荷预测精确度,提出了一种基于k-shape时间序列聚类与STL季节趋势分解算法相结合的负荷曲线聚类预测模型(k-shape-seasonal and trend decomposition using loess-gradient boosting decision tree,k-shape-STL-GBDT)。首先分析用户用电时序特征,利用k-shape时间序列聚类算法根据负荷曲线划分用户聚类,其次,使用STL算法将不同簇的负荷数据划分为季节项、趋势项与随机项。然后,结合温度、湿度等影响因素搭建预测模型,以麻省大学smart*可再生能源项目的公开数据集为例进行分析,并与多种主流聚类分解预测模型进行对比。结果表明新提出的模型框架MAPE减少了4%以上,针对短期负荷预测表现出了较好的性能与预测精度。
