ENHANCED CUSTOMER DEMAND LOAD PROFILES ESTIMATION ALGORITHMS FOR FIELD APPLICATION
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Due to the deregulation of the power system, the electric power industry is undergoing a transformation in terms of its planning and operation strategies. Because of the importance in reducing financial and operational risk, improving load forecasting accuracy is paramount. In some load forecasting applications, K-means clustering is used to group customers prior to forecasting. This method has been shown to improve the accuracy of load predictions. However, there are situations where K-means clustering reduces load forecasting accuracy. This dissertation studies the factors that affect the performance of K-means clustering. The data used for validating the proposed strategies associated with the factors is from Consolidated Edison Company of New York, Inc. (Con Edison). The mean absolute percent error (MAPE) and relative mean square error (RMSE) are utilized to evaluate the forecasting results of K-means based least squares support vector machines (LS-SVM) and preprocessed K-means based LS-SVM. Additionally, the outperformance of preprocessed K-means based LS-SVM is demonstrated via the data results. As the improving of life quality, electricity consumption is also increased. Though elements that affect electricity consumption are various, the overall influence is more important than individual one. This dissertation proposes the load growth factor, which focuses on the overall pattern of consumption changes. The procedures for load growth factor application is conducted by different groups of customers, and help to improve the load profile estimation accuracies. Peak demand forecasting accuracy is affected by the temperature, this dissertation comes up with a method that including temperature variables to the estimation process. The related calculation equations are shown in this dissertation. This dissertation aims to develop the load profile forecast algorithm and increase the forecast accuracy for coincident peak demand.