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首页> 外文期刊>IEEE transactions on industrial informatics >Load Forecasting Using Interval Type-2 Fuzzy Logic Systems: Optimal Type Reduction
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Load Forecasting Using Interval Type-2 Fuzzy Logic Systems: Optimal Type Reduction

机译：使用区间2型模糊逻辑系统进行负荷预测：最佳类型减少

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摘要

This paper aims at using interval type-2 fuzzy logic systems (IT2FLSs) for one-day ahead load forecasting task. It introduces an optimal type reduction (TR) algorithm for IT2FLSs to improve their approximation capability. Flexibility and adaptiveness are the key features of the proposed nonparametric optimal TR algorithm. Lower and upper firing strengths of rules as well as their consequent coefficients are fed into a neural network (NN). NN output is a crisp value that corresponds to the optimal defuzzified output of IT2FLSs. The NN type reducer is trained through minimization of an error-based cost function with the purpose of improving forecasting performance of IT2FLS models. Once the optimal NN-based type reducer is trained, IT2FLS models can be straightforwardly forecast the next-day load demand. Numerical testing using real load datasets indicate IT2FLS models equipped with the new optimal TR algorithm outperform IT2FLS models using traditional TR algorithms in terms of forecast accuracies. This benefit is achieved in no cost, as the computational requirement of the proposed optimal TR algorithm is the same as for traditional TR algorithms.
机译：本文旨在将区间2型模糊逻辑系统（IT2FLS）用于提前一天的负荷预测任务。它为IT2FLS引入了最佳类型缩减（TR）算法，以提高其逼近能力。灵活性和自适应性是所提出的非参数最优TR算法的关键特征。规则的较高和较低的发射强度及其相应系数被馈入神经网络（NN）。 NN输出是一个清晰的值，对应于IT2FLS的最佳去模糊输出。通过最小化基于错误的成本函数来训练NN型减速器，其目的是提高IT2FLS模型的预测性能。一旦训练了最佳的基于NN的减速机，IT2FLS模型就可以直接预测第二天的负载需求。使用实际载荷数据集进行的数值测试表明，在预测准确性方面，配备了新的最佳TR算法的IT2FLS模型优于使用传统TR算法的IT2FLS模型。由于所建议的最佳TR算法的计算要求与传统TR算法的计算要求相同，因此可以免费获得此收益。

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