Electricity Consumption Forecasting of Buildings Using Hierarchical ANFIS and GRA

Han-Yun Chen, Ching-Hung Le, Baolian Huang
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Abstract

Because of the rise of environmental awareness, controlling and monitoring the electricity consumption become significant. The accuracy of the prediction of electricity consumption can directly influence the efficiency of power management. If the usage status of electricity can be predicted, it will be easy to discover if there is any unusual electricity consumption. The choice of suitable models or mathematic methods will be the essential of all. Adaptive network-based fuzzy inference system combines the concept of fuzzy and neural networks. It reserves the interpretability of fuzzy inference system and the learning ability of neural networks. We applied adaptive network-based fuzzy inference system (ANFIS) with hierarchical structure on electricity consumption prediction and grey relational analysis (GRA) on the influence of each input factors. The result showed that hierarchical ANFIS did achieve the purpose we set and GRA can effectively evaluate the magnitude of relation between factors and specific output.
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基于分层ANFIS和GRA的建筑用电量预测
由于环保意识的提高,对电力消耗的控制和监测变得非常重要。电力消耗预测的准确性直接影响到电力管理的效率。如果可以预测电力的使用状况,就很容易发现是否有异常的用电量。选择合适的模型或数学方法将是最重要的。基于自适应网络的模糊推理系统结合了模糊和神经网络的概念。它保留了模糊推理系统的可解释性和神经网络的学习能力。将基于自适应网络的层次结构模糊推理系统(ANFIS)应用于电力消费预测,并对各输入因素的影响进行灰色关联分析(GRA)。结果表明,分层ANFIS确实达到了我们设定的目的,GRA可以有效地评价各因素与具体产出之间的关系程度。
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