Load time series analysis is critical for resource management and optimization decisions,especially automated analysis techniques.Existing research has insufficiently interpreted the overall characteristics of samples...Load time series analysis is critical for resource management and optimization decisions,especially automated analysis techniques.Existing research has insufficiently interpreted the overall characteristics of samples,leading to significant differences in load level detection conclusions for samples with different characteristics(trend,seasonality,cyclicality).Achieving automated,feature-adaptive,and quantifiable analysis methods remains a challenge.This paper proposes a Threshold Recognition-based Load Level Detection Algorithm(TRLLD),which effectively identifies different load level regions in samples of arbitrary size and distribution type based on sample characteristics.By utilizing distribution density uniformity,the algorithm classifies data points and ultimately obtains normalized load values.In the feature recognition step,the algorithm employs the Density Uniformity Index Based on Differences(DUID),High Load Level Concentration(HLLC),and Low Load Level Concentration(LLLC)to assess sample characteristics,which are independent of specific load values,providing a standardized perspective on features,ensuring high efficiency and strong interpretability.Compared to traditional methods,the proposed approach demonstrates better adaptive and real-time analysis capabilities.Experimental results indicate that it can effectively identify high load and low load regions in 16 groups of time series samples with different load characteristics,yielding highly interpretable results.The correlation between the DUID and sample density distribution uniformity reaches 98.08%.When introducing 10% MAD intensity noise,the maximum relative error is 4.72%,showcasing high robustness.Notably,it exhibits significant advantages in general and low sample scenarios.展开更多
Shallow lake eutrophication is a global environmental issue. This study investigated the effects of water level variation and nutrient loadings on the growth and nutrient accumulation of Phragmites australis （reed） ...Shallow lake eutrophication is a global environmental issue. This study investigated the effects of water level variation and nutrient loadings on the growth and nutrient accumulation of Phragmites australis （reed） by field samplings in Baiyangdian Lake, the largest shallow lake of northern China. The field samplings were conducted in two sites of different nutrient loadings during the whole growth period of reeds, and three types of zones with different water depths were chosen for each site, including the terrestrial zone with water level below the ground, the ecotone zone with the water level varying from belowground to aboveground, and the submerged zone with water level above the ground. The result showed that reed growth was more limited by water level variation than nutrient loadings. The average stem lengths and diameters in terrestrial zones were about 26.3%-27.5% and 7.2%-12.0% higher than those in submerged zones, respectively. Similarly, the terrestrial status increased the aboveground biomass of reeds by 36.6%-51.8% compared with the submerged status. Both the nutrient concentrations and storages in the aboveground reeds were mainly influenced by the nutrient loadings in surface water and sediment rather than the water level variation of the reed growth environment, and the nutrient storages reached their maxima in late August or early September. It was observed that the maximum nitrogen storage occurred in the terrestrial zone with higher nutrient loadings, with the value of 74.5 g/m2. This study suggested that water level variation and nutrient loadings should be considered when using reeds to control and remediate eutrophication of shallow lakes.展开更多
In this paper, through the nonlinear response of rock strain and stress, we have analized the physical mechanism of loading and unloading response ratio of the well level to the earth tides,the respouse of an aquife...In this paper, through the nonlinear response of rock strain and stress, we have analized the physical mechanism of loading and unloading response ratio of the well level to the earth tides,the respouse of an aquifer of confined well to bulk strain tide and showed two methods of the calculation of loading and unloading response ratio of the well level to the earth tides. We took the example of the Yu 01 well, which is near the epicenter of Heze M S 5.9 earthquake, calculated the response rate and loading and unloading response ratio of two kinds of the earth tides of it. The response rate and response ratio before the earthquake had the variation of increase.展开更多
为解决区域综合能源系统中多主体利益冲突、用户侧分布式储能投资成本高昂、容量利用不均以及碳排放量较高等问题,提出一种基于云储能服务商-综合能源系统运行商(integrated energy system operators,IESO)-负荷聚合商(load aggregators...为解决区域综合能源系统中多主体利益冲突、用户侧分布式储能投资成本高昂、容量利用不均以及碳排放量较高等问题,提出一种基于云储能服务商-综合能源系统运行商(integrated energy system operators,IESO)-负荷聚合商(load aggregators,LA)联盟三层博弈的区域综合能源系统低碳运行策略。首先,构建租赁云储能的IESO与LA的能源交易框架。其次,考虑到多个理性主体对盈利最大化的诉求,建立综合能源系统三层博弈模型。第一层为以IESO为主导者、LA联盟为伴随者的主从博弈;第二层为以云储能服务商为供给者、IESO为接收者的主从博弈;第三层是LA联盟成员之间的合作博弈,并采取非对称纳什议价法分配收益。最后,利用二分法、KKT条件结合交替方向乘子法(alternating direction multiplier method,ADMM)对该模型进行求解。仿真结果表明,该策略不仅能够促进系统低碳运行,而且能够满足各主体的经济性需求。展开更多
文摘Load time series analysis is critical for resource management and optimization decisions,especially automated analysis techniques.Existing research has insufficiently interpreted the overall characteristics of samples,leading to significant differences in load level detection conclusions for samples with different characteristics(trend,seasonality,cyclicality).Achieving automated,feature-adaptive,and quantifiable analysis methods remains a challenge.This paper proposes a Threshold Recognition-based Load Level Detection Algorithm(TRLLD),which effectively identifies different load level regions in samples of arbitrary size and distribution type based on sample characteristics.By utilizing distribution density uniformity,the algorithm classifies data points and ultimately obtains normalized load values.In the feature recognition step,the algorithm employs the Density Uniformity Index Based on Differences(DUID),High Load Level Concentration(HLLC),and Low Load Level Concentration(LLLC)to assess sample characteristics,which are independent of specific load values,providing a standardized perspective on features,ensuring high efficiency and strong interpretability.Compared to traditional methods,the proposed approach demonstrates better adaptive and real-time analysis capabilities.Experimental results indicate that it can effectively identify high load and low load regions in 16 groups of time series samples with different load characteristics,yielding highly interpretable results.The correlation between the DUID and sample density distribution uniformity reaches 98.08%.When introducing 10% MAD intensity noise,the maximum relative error is 4.72%,showcasing high robustness.Notably,it exhibits significant advantages in general and low sample scenarios.
基金supported by the Major State Basic Research Development Program (No.2010CB951104)the Program for New Century Excellent Talents in University (No. NCET-09-0233)the National Water Pollution Control and Treatment Project in China (No.2008ZX07209-009)
文摘Shallow lake eutrophication is a global environmental issue. This study investigated the effects of water level variation and nutrient loadings on the growth and nutrient accumulation of Phragmites australis （reed） by field samplings in Baiyangdian Lake, the largest shallow lake of northern China. The field samplings were conducted in two sites of different nutrient loadings during the whole growth period of reeds, and three types of zones with different water depths were chosen for each site, including the terrestrial zone with water level below the ground, the ecotone zone with the water level varying from belowground to aboveground, and the submerged zone with water level above the ground. The result showed that reed growth was more limited by water level variation than nutrient loadings. The average stem lengths and diameters in terrestrial zones were about 26.3%-27.5% and 7.2%-12.0% higher than those in submerged zones, respectively. Similarly, the terrestrial status increased the aboveground biomass of reeds by 36.6%-51.8% compared with the submerged status. Both the nutrient concentrations and storages in the aboveground reeds were mainly influenced by the nutrient loadings in surface water and sediment rather than the water level variation of the reed growth environment, and the nutrient storages reached their maxima in late August or early September. It was observed that the maximum nitrogen storage occurred in the terrestrial zone with higher nutrient loadings, with the value of 74.5 g/m2. This study suggested that water level variation and nutrient loadings should be considered when using reeds to control and remediate eutrophication of shallow lakes.
文摘In this paper, through the nonlinear response of rock strain and stress, we have analized the physical mechanism of loading and unloading response ratio of the well level to the earth tides,the respouse of an aquifer of confined well to bulk strain tide and showed two methods of the calculation of loading and unloading response ratio of the well level to the earth tides. We took the example of the Yu 01 well, which is near the epicenter of Heze M S 5.9 earthquake, calculated the response rate and loading and unloading response ratio of two kinds of the earth tides of it. The response rate and response ratio before the earthquake had the variation of increase.
文摘为解决区域综合能源系统中多主体利益冲突、用户侧分布式储能投资成本高昂、容量利用不均以及碳排放量较高等问题,提出一种基于云储能服务商-综合能源系统运行商(integrated energy system operators,IESO)-负荷聚合商(load aggregators,LA)联盟三层博弈的区域综合能源系统低碳运行策略。首先,构建租赁云储能的IESO与LA的能源交易框架。其次,考虑到多个理性主体对盈利最大化的诉求,建立综合能源系统三层博弈模型。第一层为以IESO为主导者、LA联盟为伴随者的主从博弈;第二层为以云储能服务商为供给者、IESO为接收者的主从博弈;第三层是LA联盟成员之间的合作博弈,并采取非对称纳什议价法分配收益。最后,利用二分法、KKT条件结合交替方向乘子法(alternating direction multiplier method,ADMM)对该模型进行求解。仿真结果表明,该策略不仅能够促进系统低碳运行,而且能够满足各主体的经济性需求。
基金浙江省“尖兵”“领雁”研发攻关计划(2024C01058)浙江省“十四五”第二批本科省级教学改革备案项目(JGBA2024014)+2 种基金2025年01月批次教育部产学合作协同育人项目(2501270945)2024年度浙江大学本科“AI赋能”示范课程建设项目(24)浙江大学第一批AI For Education系列实证教学研究项目(202402)。
