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A new idea is proposed that preprocessing is the key to improving the precision of short-term load forecasting (STLF). This paper presents a new model of STLF which is using support vector regression (SVR) based on pattern-base. Our model can be described as follows: firstly, it recognizes the different patterns of daily load according such features as weather and date type by means of data mining technology of classification and regression tree (CART); secondly, it sets up pattern-bases which are composed of daily load data sequence with highly similar features; thirdly, it establishes SVR forecasting model based on the pattern-base which matches to the forecasting day. Since the patterns of daily load are treated beforehand, the rule of the historical data sequence is more obvious. The model has many advantages: first, since the training data has similar pattern to the forecasting day, the model reflects the rule of daily load accurately and improves forecasting precision accordingly; second, as the pattern variables need not to be input into model, the mapping of the categorical variables is solved; third, as inputs are reduced, the model is simplified and the runtime is lessened. The simulation indicates that the new method is feasible and the forecasting precision is greatly improved.

The paper presents a new model of Short-term load forecasting based on pattern-base. It can be described as follows: firstly, it recognizes the different patterns of daily load according such features as weather and date type by means of data mining technology of classification and regression tree; secondly, it sets up pattern-bases which are composed of daily load data sequence with highly similar features; thirdly, it establishes support vector regression forecasting model based on the pattern-base which matches to the forecasting day. The model has many advantages: first, since the training data has similar pattern to the forecasting day, the model reflects the rule of daily load accurately and improves forecasting precision accordingly; second, as the pattern variables need not to be input into model, the mapping of the categorical variables is solved; third, as inputs are reduced, the model is simplified and the runtime is lessened.

A new idea is proposed that preprocessing is the key to improving the precision of short-term load forecasting (STLF). This paper presents a new model of STLF which is based on pattern-base. Our model can be described as follows: firstly, it recognizes the different patterns of daily load according such features as weather and date type by means of data mining technology of classification and regression tree (CART); secondly, it sets up pattern-bases which are composed of daily load data sequence with highly similar features; thirdly, it establishes ANN forecasting model based on the pattern-base which matches to the forecasting day. Since the patterns of daily load are treated precedingly, the rule of the historical data sequence is more obvious. Accordingly, we need not input pattern characters when establishing ANN load forecasting model. The model has many advantages: first, since the training data has similar pattern to the forecasting day, the odel reflects the rule of daily load accurately and improves forecasting precision accordingly; second, as the pattern variables need not to be input into model, the mapping of the categorical variables is solved; third, as inputs are reduced, the model is simplified and the runtime is lessened. The simulation indicates that the new method is feasible and the forecasting precision is greatly improved.

In many industries there's a move from paper forms towards electronic forms (eForms) - speed and direction varies within and between industries. Within insurance, ACORD forms is such a standardized effort for properties & casualty and life & annuity. However, ACORD has not effectively linked their forms to the data models they've produced, ACORD forms are still used as static forms - this inhibits the move to SOA and effective straight through processing. This article will introduce how to leverage XForms W3C standardized technique into ACORD Forms and combine it with ACORD Message model in a SOA solution. It illustrates the necessary steps needed for initiating an end to end service invocation through a secure electronic forms channel, and how to utilize a feasible mechanism to makes the forms dynamic, thus the form content change with backend business rules adjustment automatically.

In this paper, a knowledge-based intelligent system for power customer service management is proposed. As the value of knowledge management and customer relationship management has been well recognized, it is essential to make optimal reuse of knowledge of customer among various functional units of the enterprise. As the power company is the industry that provides standard service, it is important to ensure that the customer service staff can access and be trained up with dynamically updated knowledge that adjusts to the changing environment of the enterprise in customer services. However, conventional way of customer service management is inadequate to achieve knowledge acquisition and diffusion, service automation and satisfaction measurement, and thus unfavorable for the continuous improvement of the customer service quality. This paper discusses the frame and process of the knowledge-based intelligent service system for power customer, and puts forward some aspects that should be noted during the period of implementation. It is verified that the proposed model provides high quality customer services with fast and efficient responses to customer demands.

With the development of electronic industry, accurate load forecasting of the future electricity demand plays an important role in regional or national power system strategy management. Electricity load forecasting is difficult due to the nonlinearity of its influencing factors. Support vector machine (SVM) have been successfully applied to solve nonlinear regression and time series problems. However, the application to load forecasting is rare. In this study, a model of support vector machine is proposed to forecast electricity load. The model overcomes the disadvantages of general artificial neural network (ANN), such as it is not easy to converge, liable to trap in partial minimum and unable to optimize globally, and the generalization of the model is not good, etc. The SVM model ensured the forecasting is optimized globally. Subsequently, examples of electricity load data from Hebei Province of China are used to illustrate the performance of the proposed model. The empirical results reveal that the proposed model outperforms the general artificial neural network model, and the forecasting accuracy improved effectively. Therefore, the model provides a promising arithmetic to forecasting electricity load in power industry.

In this paper, a simple and applied approach was presented, which can automatically locate pupils. On the basis of skin detection of human face, it combines such techniques as edge extraction and geometrical features to confirm the coarse eye regions with ways of gray integration projection, region combination and LAT to fix on the precise location of pupils. By using this method, one could attain the accurate localization of eyes in color images, so it is suitable for processing identification photos.