Dagushan Concentrator Energy Information Collection And Short-term Load Forecasting

Reduce energy is based on exactly measuring of the energy that wasting in the course of production. As an important part of plant information and automation system, effective energy management can reduce energy consumption and advance labour productivity. Modern energy management depends on some advanced technology such as automatization and information technology, unmanned technology and short-term load forecasting based on impersonality information。It is helpful to reduce energy consumption and improve the level of energy management. The power load forecast is an important research problem in power field. It has an important application value in ensuring systems working in safe mode and realizing the economy of power an the maximum benefit on this condition. Its precision directly influences system's security, profit and quality. Therefore, how to improve the forecasting precision is the emphasis on the study of short—term load forecasting.Based on the project of electrical consume of Dagushan, by the way of sensor-technology, cyber-technology and communication technology, it realize the function of Real-time electrical collection,exchange, and transmission. An accurate STLF results is based on analyzing the specialty of the real load and applying the knowledge of mathematic and computer to forecasting model. It greatly improves the forecasting precision that the wavelet transform theory is applied into the area of power short-term load forecasting. The main researches in this thesis are as follows:Firstly, the principle, features, current status and development of STLF are generalized in this thesis. And then it makes a summary of many traditional and modern load-forecastion technologies, introduces the application of Wavelet Analysis in short-term load forecasting.Secondly, via vertical and horizontal pretreatment, the historical load data can furthermore show the load changing trend, which establishes the basis for short-term load forecasting model using these historical data. In load forecasting, firstly obtains load hefts at different frequencies by wavelet transforming, then forecasts every heft by data regression multi-analysis, in the end fits every forecasting result together to get the final result. In the example, the author puts forward her own opinions on the retreatment to the original data and designs corresponding methods. On some problems about application in practice, this paper puts forward the feasible method about the complementarity of the values of load on the day before being forecasted week. Simulation results demonstrate that the proposed method can offer higher forecast precision.Thirdly, according to the characteristics of the enterprise product and energy requirment, Introduced the hardware, software and advanced software development ideas of STLF system. More important, the flexibility and openness of the application is expanded by advanced software development ideas such as design patterns and so on..Finally, the conclusion of the paper is done, and the development trend is predicted.