Research on building energy consumption prediction model based on fractal theory

ABSTRACT

Nowadays, the energy problem is becoming more and more serious, and the actual energy consumption of the building is one of the important links in the field of building energy conservation. At present, most prediction algorithms fail to fully consider the complex characteristics of building energy consumption, resulting in unsatisfactory prediction results. Fractal theory can directly analyze some rules of abstract composite complex nonlinear things and then analyze and predict them correctly. Therefore, it is also a new way to analyze fractal theory and solve the problem of large-scale public construction energy consumption prediction. Taking a building as the object, an energy consumption prediction model using the fractal collage principle and fractal interpolation algorithm is proposed. In order to verify the validity of the model, a prediction model of traditional mature BP neural network is established, and the experimental results of the two models were compared. Mean relative error (MRE) and root mean square error (RMSE) basis are used to evaluate the performance of the model on the daily. The results show that the fractal prediction model has good prediction effect and accuracy. The energy prediction data provided by the model can provide a scientific basis for energy management and energy conservation control of such buildings.

KEYWORDS:

• Energy consumption
• fractal theory
• fractal interpolation
• energy consumption prediction

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes on contributors

Junqi Yu received his Ph.D. in Control Science and Engineering from Xi’an Jiaotong University, China, in 2001. He was Associate Dean of the Graduate School, Xi’an university of architecture and technology, China. Currently, he is Dean of the School Building Services Science and Engineering, Xi’an university of architecture and technology, China. His research interests include Smart Buildings & Smart Controls and Building Energy Saving Technology.

Sen Jiao received his B.S. degrees in Building Electricity and Intelligentization from Zhejiang University of Science and Technology, China, in 2017. He is currently pursuing M.S. in the School of Building Services Science and Engineering, Xi’an University of Architecture and Technology, China. His research interests include Building Energy Saving Technology and Building Intelligence Technology.

Yue Zhang received her B.S. and M.S. degrees in Intelligent Building from Nanjing Institute of Engineering and Xi’an University of Architecture and Technology, China, in 2016 and 2019, respectively. Her research interests include Building Energy Consumption Monitoring and Energy-saving Control.

Xisheng Ding received his B.S. and M.S. degrees in Control Theory and Control Engineering from Yanshan University, China, in 2004 and 2008, He is currently pursuing Ph.D. in the School of Civil engineering, Xi’an University of Architecture and Technology, China. His research interests include Smart Buildings & Smart Controls and Smart LED Lighting Systems.

Jiali Wang received her B.S. degrees in Building Electricity and Intelligentization from Xi’an University of Architecture and Technology, China, in 2018. She is currently pursuing M.S. in the School of Building Services Science and Engineering, Xi’an University of Architecture and Technology, China. Her research interests include Building Energy Consumption Monitoring and Energy-saving Control.

Tong Ran received her B.S. degrees in Building Electricity and Intelligentization from Xi’an University of Architecture and Technology, China, in 2018. She is currently pursuing M.S. in the School of Building Services Science and Engineering, Xi’an University of Architecture and Technology, China. Her research interests include Building Intelligence Technology.

Additional information

Funding

This work was supported by The key research and development plan of Shaanxi Province, “Research on Energy Consumption Monitoring and Management System for Large Public Buildings” [Grant Number 2017ZDCXL-SF-03-02].