By the load definition of cluster, the request is regarded as granularity to compute load and implement the load balancing in cache cluster. First, the processing power of cache-node is studied from four aspects: netw...By the load definition of cluster, the request is regarded as granularity to compute load and implement the load balancing in cache cluster. First, the processing power of cache-node is studied from four aspects: network bandwidth, memory capacity, disk access rate and CPU usage. Then, the weighted load of cache-node is customized. Based on this, a load-balancing algorithm that can be applied to the cache cluster is proposed. Finally, Polygraph is used as a benchmarking tool to test the cache cluster possessing the load-balancing algorithm and the cache cluster with cache array routing protocol respectively. The results show the load-balancing algorithm can improve the performance of the cache cluster.展开更多
Since the variation pattern of load during holidays is different than that of non-holidays, forecasting holiday load is a challenging task. With a focus on this problem, we propose a learning framework based on weight...Since the variation pattern of load during holidays is different than that of non-holidays, forecasting holiday load is a challenging task. With a focus on this problem, we propose a learning framework based on weighted knowledge transfer for daily peak load forecasting during holidays.First,we select source cities which can provide extra hidden knowledge to improve the forecast accuracy of the load of the target city. Then, all the instances which are from source cities and the target city will be weighted and trained by the improved weighted transfer learning algorithm which is based on the TrAdaBoost algorithm and can decrease negative transfer. We evaluate our method with the classical support vector machine method and a method based on knowledge transfer on a real data set, which includes eleven cities in Guangdong province of China,to illustrate the performance of the method. To solve the problem of limited historical holiday load data, we transfer the data from nearby cities based on the fact that nearby cities in Guangdong province of China have a similar economic development level and similar load variation pattern. The results of comparative experiments show that the framework proposed by this paper outperforms these methods in terms of mean absolute percent error and mean absolute scaled error.展开更多
Based on small-deflection buckling equation, a weighted solution for critical load is presented. Usually, it is very difficult to solve the equation for general problems, especially those with complicated boundary con...Based on small-deflection buckling equation, a weighted solution for critical load is presented. Usually, it is very difficult to solve the equation for general problems, especially those with complicated boundary conditions, Axisymmetric problem was studied as an example. Influencing factors were found from the equation and averaged as the buckling load by introducing weights. To determine those weights, some special known results were applied. This method solves general complicated problems by using the solutions of special simple problems, simplifies the solving procedure and expands the scope of solvable problem. Compared with numerical solution, it also has fine precision.展开更多
Short-term load forecast plays an important role in the day-to-day operation and scheduling of generating units. Season and temperature are the most important factors that affect the load change, but random factors su...Short-term load forecast plays an important role in the day-to-day operation and scheduling of generating units. Season and temperature are the most important factors that affect the load change, but random factors such as big sport events or popular TV shows can change demand consumption in particular hours, which will lead to sudden load changes. A weighted time-variant slide fuzzy time-series model (WTVS) for short-term load forecasting is proposed to improve forecasting accuracy. The WTVS model is divided into three parts, including the data preprocessing, the trend training and the load forecasting. In the data preprocessing phase, the impact of random factors will be weakened by smoothing the historical data. In the trend training and load forecasting phase, the seasonal factor and the weighted historical data are introduced into the Time-variant Slide Fuzzy Time-series Models (TVS) for short-term load forecasting. The WTVS model is tested on the load of the National Electric Power Company in Jordan. Results show that the proposed WTVS model achieves a significant improvement in load forecasting accuracy as compared to TVS models.展开更多
文摘By the load definition of cluster, the request is regarded as granularity to compute load and implement the load balancing in cache cluster. First, the processing power of cache-node is studied from four aspects: network bandwidth, memory capacity, disk access rate and CPU usage. Then, the weighted load of cache-node is customized. Based on this, a load-balancing algorithm that can be applied to the cache cluster is proposed. Finally, Polygraph is used as a benchmarking tool to test the cache cluster possessing the load-balancing algorithm and the cache cluster with cache array routing protocol respectively. The results show the load-balancing algorithm can improve the performance of the cache cluster.
基金supported by the National Natural Science Foundation of China (No. 61773157)in part by the National Scientific and Technological Achievement Transformation Project of China (No. 201255)
文摘Since the variation pattern of load during holidays is different than that of non-holidays, forecasting holiday load is a challenging task. With a focus on this problem, we propose a learning framework based on weighted knowledge transfer for daily peak load forecasting during holidays.First,we select source cities which can provide extra hidden knowledge to improve the forecast accuracy of the load of the target city. Then, all the instances which are from source cities and the target city will be weighted and trained by the improved weighted transfer learning algorithm which is based on the TrAdaBoost algorithm and can decrease negative transfer. We evaluate our method with the classical support vector machine method and a method based on knowledge transfer on a real data set, which includes eleven cities in Guangdong province of China,to illustrate the performance of the method. To solve the problem of limited historical holiday load data, we transfer the data from nearby cities based on the fact that nearby cities in Guangdong province of China have a similar economic development level and similar load variation pattern. The results of comparative experiments show that the framework proposed by this paper outperforms these methods in terms of mean absolute percent error and mean absolute scaled error.
文摘Based on small-deflection buckling equation, a weighted solution for critical load is presented. Usually, it is very difficult to solve the equation for general problems, especially those with complicated boundary conditions, Axisymmetric problem was studied as an example. Influencing factors were found from the equation and averaged as the buckling load by introducing weights. To determine those weights, some special known results were applied. This method solves general complicated problems by using the solutions of special simple problems, simplifies the solving procedure and expands the scope of solvable problem. Compared with numerical solution, it also has fine precision.
文摘Short-term load forecast plays an important role in the day-to-day operation and scheduling of generating units. Season and temperature are the most important factors that affect the load change, but random factors such as big sport events or popular TV shows can change demand consumption in particular hours, which will lead to sudden load changes. A weighted time-variant slide fuzzy time-series model (WTVS) for short-term load forecasting is proposed to improve forecasting accuracy. The WTVS model is divided into three parts, including the data preprocessing, the trend training and the load forecasting. In the data preprocessing phase, the impact of random factors will be weakened by smoothing the historical data. In the trend training and load forecasting phase, the seasonal factor and the weighted historical data are introduced into the Time-variant Slide Fuzzy Time-series Models (TVS) for short-term load forecasting. The WTVS model is tested on the load of the National Electric Power Company in Jordan. Results show that the proposed WTVS model achieves a significant improvement in load forecasting accuracy as compared to TVS models.