Prediction of Building Power Consumption Based on GAWNN

2019 International Conference on Advanced Electronic Materials, Computers and Materials Engineering

无线电电子学;计算机科学;材料科学

Yaqing, Tian^1

Qingshuihe Campus, University of Electronic Science and Technology, Gaoxin West District, Chengdu, Sichuan, China^1

关键词: Building energy consumption;    Continuous development;    Electricity-consumption;    Energy saving and consumption reductions;    National policies;    Prediction methods;    Scientific basis;    Wavelet neural networks;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/563/4/042081/pdf
DOI  :  10.1088/1757-899X/563/4/042081

来源: IOP

【 摘 要 】

With the continuous development of urban modernization, the power consumption of building is increasing rapidly. In the context of the basic national policy of sustainable development, energy conservation and emission reduction of building energy consumption is imperative. In this paper, the relevant influencing factors of building electricity consumption are considered comprehensively. The wavelet neural network combined with genetic algorithm is (GA-WNN) used to predict the power consumption of buildings. The experimental results show that the prediction effect and relative error of GA-WNN algorithm are better than other prediction methods. The unique advantages of the algorithm in the field of building power consumption prediction can provide a scientific basis for energy saving and consumption reduction. It can facilitate managers to formulate efficient energy management solutions to optimize resource allocation.