基于GA-PSO混合规划算法的电力客户信用评价模型

W. Xinli
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引用次数: 4

摘要

供电企业面临着因用电客户违反供电合同约定而造成的经营风险。为了规避电力客户信用风险,对电力客户进行综合评价,本文建立了基于GPSO混合算法的电力客户信用风险评价模型,克服了传统线性ECCR评价方法的不足。该模型综合了遗传算法(GA)和粒子群算法(PSO)的优点,在收敛性能和预测精度方面优于传统的多元回归方法和GP方法。仿真结果表明,混合模型简单可行,能提高评估效率和准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The credit evaluation model of electricity customer based on GA-PSO hybrid programming algorithm
Power supply enterprises face the business risk caused by electricity clients who break their promise on supply contracts. In order to avoid credit risk and conduct comprehensive evaluation on electricity clients, this paper builds an electricity client credit risk evaluation model based on GPSO hybrid algorithm, overcoming the shortcomings of traditional linear ECCR evaluation method. This new model integrates advantages of GA (genetic algorithm) and PSO, better than traditional multiple regression method and GP method regarding convergence performance and forecast accuracy. Simulation results indicate that hybrid model is simple and feasible, and it can improve efficiency and accuracy of evaluation.
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