本論文旨在研究以負載預測為基礎之電網重構分析技術，藉由類神經網路技術計算出標的系統每個月之平均負載量與發電量，並利用三相相別配接及饋線重組技術，以粒子群演算法找出未來目標系統運轉時段之最適相別配接及系統饋線結構，以提高系統整體運轉效能。

This thesis aims to study the technology of distribution network reconfiguration analysis based on load forecasting. To obtain the optimal operational performance of a distribution network, this thesis using neural network technology to assess the average load and power generation for every month in a target year, and adopt the optimal phase arrangement and system reconfiguration techniques, and particle swarm optimization (PSO) algorithm as well.

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