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Electric load forecasting plays a critical role for the reliable and efficient operation of power grids. In this paper we propose a load forecasting model using parallel radial basis function neural networks (RBFNN). The proposed implementation of RBFNN allows parallel computation therefore expedites the convergence of training process. The proposed model also employs a new hybrid chaotic genetic...
Long-term demand forecasting presents the first step in planning and developing future generation, transmission and distribution facilities. One of the primary tasks of an electric utility accurately predicts load demand requirements at all times, especially for long-term. Based on the outcome of such forecasts, utilities coordinate their resources to meet the forecasted demand using a least-cost...
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