Forecasting building energy consumption using neural networks and hybrid neuro-fuzzy system: A comparative study: Fid-Bau Portal

Forecasting building energy consumption using neural networks and hybrid neuro-fuzzy system: A comparative study

Li, Kangji / Su, Hongye / Chu, Jian

Highlights ► Present a hybrid genetic algorithm-adaptive network-based fuzzy inference system (GA-ANFIS) in building energy prediction. ► A hierarchical structure of ANFIS is suggested to solve the probably curse-of-dimensionality problem. ► Time consuming problem of the proposed model is overcomed by appropriate configuration of the model parameters. ► Better performances are obtained compared with Artificial Neural Networks using two different kinds of data sets.

Abstract As a regular data-driven method, Artificial Neural Networks (ANNs) are popular in building energy prediction. In this paper, an alternative approach, namely, hybrid genetic algorithm-adaptive network-based fuzzy inference system (GA-ANFIS) is presented. In this model, GA optimizes the subtractive clustering’s radiuses which help form the rule base, and ANFIS adjusts the premise and consequent parameters to optimize the forecasting performance. a hierarchical structure of ANFIS is also suggested to solve the probably curse-of-dimensionality problem. The performance of the proposed model is compared with ANN using two different data sets, which are collected from the Energy Prediction Shootout I contest and a library building located in Zhejiang University, China. Results show that the hybrid GA-ANFIS model has better performance than ANN in term of prediction accuracy. The proposed model also has the same scale of modeling time as ANN if parameters in GA procedure are carefully selected. It can be regarded as an alternative method in building energy prediction.