The load saturation estimation helps to quantify the final load consumption of a given area, and avoid unnecessary investment to the transmission and distribution facilities. There are generally two methods to estimate the saturated load, based on the load curve and the spacial load distribution respectively. With the historical load instead of load classification data, the Logistic curve, i.e. the S curve, is more suitable to extrapolate the load curve, and forecast the saturated load consumption. In the existing literatures, the parametric estimation of the Logistic curve is based on randomly selected 3 or 4 load data with equal intervals, and can not avoid abnormal or ill data. In this paper, improved parametric estimation methods are proposed. With the average value or the largest correlation index is applied to find the parameters of Logistic curve. The numerical results among the proposed and existing methods are presented, and the forecast feasibility for different load increase stages are discussed.
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