An adaptive neural network based short-term electric load forecasting system is presented. The system is developed and implemented for Florida Power and Light Company(FPL). Practical experiences with the system are discussed. The system accounts for seasonal and daily characteristics, as well as abnormal conditions such as cold fronts, heat waves, holidays and other conditions. It is capable of forecasting load with a lead time of one hour to seven days. The adaptive mechanism is used to train the neural networks when on-line. The results indicate that the load forecasting system presented gives robust and more accurate forecasts and allows greater adaptability to sudden climatic changes compared with statistical methods. The system is portable and can be modified to suit the requirements of other utility companies.
The paper being presented describes how an Artificial Neural Network can be utilized for improving the shape of an electrical power load forecast.It is shown that the application of this method to make the shape of the forecast load curve conform to the shape of the typical seasonal load curve results in improvement in the overall accuracy of the electrical power load forecast.
A new method for the design optimization of electromagnetic devices is presented. Improved Simulated Annealing (SA) is used along With the Hopfield neural network (€IN) to optimize a solution obtained from a trained multilayered perceptron (MLP). This results in an overall increase in the speed of optimization. Results are presented for two examples which show the effectiveness of the proposed method in obtaining a near optimal solution.In this paper, we present results obtained for a c-core magnetic circuit and a 2-pole dc motor using the proposed method. The simulated annealing algorithm has been modified by separating the search and state updating procedure, of the shortcomings of using with in design optimization problems. The results show the effectiveness of using the proposed method for design optimization.
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