Hong Ouyang

Short-term load forecasting based on parallel frameworks

In this paper, a short-term load forecasting method which considers the different load characteristics in different periods is proposed. Firstly, we use parallel K-Means algorithm to cluster the daily load curves with 96 points of electricity customer to obtain some date groups with different load characteristics. Then for each date group, we use the daily load curves in the group to build load forecasting model of every point by the random vector functional-link net. Finally, we find the similar historical day of the forecast day by dynamic time warping method and use the forecasting models of the date group which contains the similar date to forecast the customers load in the forecast day. Empirical study shows that the method is suitable for the short-term load forecasting of massive customers and has satisfactory forecasting accuracy.

Effective and Efficient Feature Description of the Electric Customer Service Staffs Based on the Visual Clustering Algorithm

Effective and efficient description of the features of different staffs is the key problem to achieve scientific and meticulous management in customer service centers, because the features are essential for decision-making works, e.g., the formulation of the scheduling strategy, the placement strategy and the performance evaluation standard. In this paper, by introducing the visual clustering algorithm, the electric customer service staffs are divided into four types, i.e., the quality type, the efficiency type, the medium type and the problem type, on the basis of the workload, work quality and work efficiency of the staffs. The clustering results are helpful for the formulation of the scheduling strategy, the placement strategy and the performance evaluation standard objectively. The visual clustering algorithm is now regularly applied to divide electric customer service staffs into different groups so that related strategies and standards may be tuned timely and properly. The successful application of our proposed method in State Grid Customer Service Center shows the effectiveness and the efficiency of the proposed visual clustering algorithm.

An Evaluation Method for Charging Facilities Siting

Due to the important roles in relieving energy crisis and environmental pollution, electric vehicles have been developed greatly. Charging facilities are linked to electric vehicles closely, and the accuracy of the charging area and scale affect their applications to a great extent. Based on the transaction data of the charging facilities in a city of northern China, this paper calculates the occupancy rate of these charging facilities and divides them by box plot. Then, we choose the data such as the surrounding environment of the charging facilities, the parking charging standard and the spatial location as well as the proportion of the own pile within 3km for analysis of variance. We found that the surrounding environment and the proportion of the own pile have significant effect on occupancy rate of charging facilities. Finally, according to the results of analysis of variance, the decision tree classification model of charging facilities is established. The results demonstrate that the model can identify the idle facilities.

Research on building an innovative electric power marketing business application system based on cloud computing and microservices architecture technologies

The paper proposes the method of building the electric power marketing business application system based on the cloud computing and microservices architecture technologies to solve the issues of the current system with the concentrated infrastructure and the monolithic architecture which are difficult to support the business processing in very large scale and the evolution of the electric power marketing business. The paper first introduces the current electric power marketing business application system and highlights its main drawbacks. Then it proposes the method to build the innovative electric power marketing business application system with the main technical characteristics as follows: the IaaS part of the application system has the distributed infrastructures with the IT resource elastic management and powerful horizontal scalability brought by the cloud computing technology; its PaaS part is the service platform with the components of the data processing, information integration, application building, and cloud service center to provide the several cloud services, such as one-click deployment, flexible scaling, fault self-healing, gray distribution, full-link monitoring and other cloud service capabilities; and its SaaS part has the microservices architecture with the features of servitization, componentization, decentralization, independent deployment etc. Finally, the suggestions are given to design the microservices for the electric power marketing business application.

A novel photovoltaic power output forecasting method based on weather type clustering and wavelet support vector machines regression

Due to the strong randomness and intermittency of photovoltaic (PV) power output, accurate PV power output forecast becomes more and more important for system reliability, meanwhile it can promote large-scale PV deployment. In this paper, a novel PV power output forecast model based upon weather type clustering and support vector machines (SVM) regression is proposed. Firstly, on the basis of calculated average historical PV power output of each weather type, expectation maximization (EM) algorithm is adopted to cluster weather types into some categories. Secondly, based on clustering results and weather information collected from authoritative meteorological administration, the input samples are selected to better reflect weather characteristics of the forecasting day. Finally, for certain weather type, a wavelet SVM regression approach is adopted to forecast PV power output. Extensive experimental results demonstrate that the proposed model for PV power output forecasting has a high forecasting accuracy.

Comprehensive evaluation of the staffs of state grid customer service center

A scientific, objective and comprehensive evaluation of the works of the customer service staffs is essential for improving the customer service ability and qualities, and especially for improving the satisfactory rate. In this paper, we propose a scientific and objective method for the comprehensive evaluation of the staffs of State Grid Customer Service Center which consists of two steps. In the first step, the L1/2 sparse classification model is used to distinguish the indexes which will be used for the evaluation from all indexes. In the second step, the Analytic Hierarchy Process is used for the comprehensive evaluation of the staffs over the selected indexes. Extensive experimental study shows the priority of our proposed method.