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首页> 外文会议>2017 IEEE URUCON Conference >Short term load forecast method using artificial neural network with artificial immune systems
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Short term load forecast method using artificial neural network with artificial immune systems

机译：人工免疫系统的人工神经网络短期负荷预测方法

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Although there are widely used methods as Genetic Algorithms, Fuzzy Logic and Artificial Neural Network, the Optimization Based Tools are considered the future of the systems of information. This issue is about Artificial Neural Network (ANN) used in Short Term Load Forecast (STLF). It proposes that the method is valid to predict STLF and how important it is on demand scheduling, contingency analysis, power flow analysis, planning and maintenance of power systems or distribution networks. It also uses and shows Genetic Algorithms as Artificial Immune System (AIS) as a valid method of optimization of the gain assigned to the neural components of an ANN. The adjusted ANN considers information as temperature and related humidity associated to the demand of the electric network analyzed. The comparison of the results obtained using ANN with the proceedings normally used in distribution centers, looks for significant improvement in short term electric demand prediction and the capacity of response in managing of electric load.
机译：尽管有广泛使用的方法，如遗传算法，模糊逻辑和人工神经网络，但基于优化的工具被认为是信息系统的未来。此问题与短期负荷预测（STLF）中使用的人工神经网络（ANN）有关。它建议该方法可有效预测STLF以及它对按需调度，应急分析，潮流分析，电力系统或配电网络的计划和维护的重要性。它还使用遗传算法作为人工免疫系统（AIS），并将其显示为优化分配给ANN神经成分的增益的有效方法。调整后的人工神经网络将信息视为与所分析的电网需求相关的温度和相关湿度。将使用人工神经网络获得的结果与配电中心通常使用的程序进行比较，可以发现短期电力需求预测和电力负荷管理响应能力的显着提高。

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