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Load forecasting is very essential to the operation of electric utility. It is a pre-requisite to economic dispatch of electrical power and enhances the efficiency besides ensuring reliable operation of a power system. Electrical energy demand is highly dependent on various independent variables such as the weather, temperature, holidays, and days in a week. The accuracy of the forecast is important...
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...
Accurate forecasting of short-term electricity load has been one of the most important issues in the electricity industry. And the forecasting accuracy is influenced by many unpredicted factors. Artificial neural network is a novel type of learning method, which has been successfully employed to solve nonlinear regression and time series problems. In this paper, it is proposed a new optimal model...
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,...
This paper proposes practical predictions of hospital air-conditioner electricity using the artificial neural network, owing to its excellent predict ability. The influence variables of hospital air-conditioner electricity are included temperature, relative humidity, the previous one hour electricity, the time in day, and some uncontrolled variables, e.g. the number of surgical operations, the number...
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