Short term load forecasting using hybrid adaptive fuzzy neural system: The performance evaluation

Daud Mustafa Minhas, Raja Rehan Khalid, Georg Frey
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引用次数: 9

Abstract

In this paper, an evaluation theory of hybrid model for short-term electricity load forecasting is presented using simple soft-technique of predicting data. A model that integrates fuzzy system with neural network database is demonstrated and eventually compared with a traditional statistical method of linear regression. Power load forecasting errors especially for weekends, which is much higher than that of weekdays, is reduced using the probabilistic and stochastic natured Hybrid Adaptive Fuzzy Neural System (HAFNS) method. Neural network database uses temperature and power loads as predictors to train the data sets and then use fuzzy system to develop membership functions, forecasting future power load demands for subsequent hours. HAFNS model is made using power load and temperature data of 2015. The training and testing set of HAFNS is composed of yearly data, which may be decomposed on monthly, daily and hourly basis for comparison. The simulation results of the forecasted data including error distribution graphs are demonstrated.
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混合自适应模糊神经系统短期负荷预测:性能评价
本文利用简单的预测数据软技术,提出了短期负荷预测混合模型的评价理论。提出了一种将模糊系统与神经网络数据库相结合的模型,并与传统的线性回归统计方法进行了比较。采用概率与随机混合自适应模糊神经系统(HAFNS)方法,降低了电力负荷预测误差,特别是周末负荷预测误差远高于工作日。神经网络数据库以温度和电力负荷作为预测因子对数据集进行训练,然后利用模糊系统建立隶属函数,预测未来数小时的电力负荷需求。HAFNS模型采用2015年的电力负荷和温度数据。HAFNS的训练和测试集由年数据组成,可以按月、日、时进行分解,以便进行比较。给出了预测数据的仿真结果,包括误差分布图。
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