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Wavelet-based short-term load forecasting using optimized anfis

机译:使用优化的anfis的基于小波的短期负荷预测

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摘要

This paper focuses on forecasting electric load consumption using optimized Adaptive Neuro-Fuzzy inference System (ANFIS). It employs the use of Particle Swarm Optimization (PSO) to optimize ANFIS, with aim of improving its speed and accuracy. It determines the minimum error from the ANFIS error function and thus propagates it to the premise part. Wavelet transform was used to decompose the input variables using Daubechies 2 (db2). The purpose is to reduce outliers as small as possible in the forecasting data. The data was decomposed in to one approximation coefficients and three details coefficients. The combined Wavelet-PSO-ANFIS model was tested using weather and load data of Nova Scotia province. It was found that the model can perform more than Genetic Algorithm (GA) optimized ANFIS and traditional ANFIS, which is been optimized by Gradient Decent (GD). Mean Absolute Percentage Error (MAPE) was used to measure the accuracy of the model. The model gives lower MAPE than the other two models, and is faster in terms of speed of convergence.
机译:本文着重于使用优化的自适应神经模糊推理系统(ANFIS)预测电力消耗。它使用粒子群优化(PSO)来优化ANFIS,以提高其速度和准确性。它根据ANFIS误差函数确定最小误差,并将其传播到前提部分。小波变换用于使用Daubechies 2(db2)分解输入变量。目的是将预测数据中的离群值尽可能小。将数据分解为一个近似系数和三个细节系数。使用新斯科舍省的天气和负荷数据测试了组合的Wavelet-PSO-ANFIS模型。发现该模型的性能比遗传算法(GA)优化的ANFIS和传统的ANFIS更好,传统的ANFIS由Gradient Decent(GD)优化。平均绝对百分比误差(MAPE)用于测量模型的准确性。与其他两个模型相比,该模型提供的MAPE较低,并且在收敛速度方面更快。

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