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Nowadays, many researches are made to estimate some of socio-economic variables in which methods such as regression, time series (ARIMA, AR and etc.), Artificial Neural Networks (ANN) and so on are used. In this paper integrated System Approach and ANN are applied for estimating affects of subsidy on electricity consumption and social welfare. Actual electricity price is estimated by ANN, which has...
Short-term load forecasting is an essential instrument in power system planning, operation and control. Many operating decisions are based on load forecasts, such as dispatch scheduling of generating capacity, reliability analysis, and maintenance planning for the generators. Overestimation of electricity demand will cause a conservative operation, which leads to the start-up of too many units or...
The problem of power load market forecasting is studied and analyzed, and a new method of power load market forecasting is advanced in this paper. Counter to the characteristic of high nonlinear and high noise of stock time series, the noise is efficiently filtered and the reduction in the data performed by means of. LS-SVM model and BP neural network model are studied and analyzed, and the fuzz change...
Electricity market players operating in a liberalized environment require adequate decision support tools, allowing them to consider all the business opportunities and take strategic decisions. Ancillary services represent a good negotiation opportunity that must be considered by market players. This paper deals with short-term predication of day-ahead spinning reserve (SR) requirement that helps...
With the deterioration of primary energy market supply, it is important to optimize the raw material buying and dispatching. The annual electric power consumption is one of the most important decision making basis to realize this. Because of the characters of observations, OLS method and neural network model are all not suit for this. PLS extract variables one by one from few historical data. Under...
With the development of power markets, forecasting is becoming more and more important in such new competitive markets since the electricity demand forecasting is the basis of decision making for participants in electricity market. The aim of this project is to develop an electricity demand predictor. In this paper, we present an Grey-based prediction algorithm to forecast a long-term electric power...
Long-term load forecasting has a vital role in generation, transmission and distribution network planning. Traditional studies for long-term load forecasting were based on regression method, which could not provide a true representation of power system behavior in a volatile electricity market. The purpose of this paper is to introduce two approaches based regression method and artificial neural network...
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