International Scientific Journal

Authors of this Paper

External Links


Real time prediction of energy consumption is the basis of energy conservation and emission reduction. Aiming at the problems of large prediction error and poor effect, a real-time prediction method of energy consumption of geothermal system of public buildings based on wavelet neural network is proposed. Firstly, the energy consumption of geothermal system in public buildings is analyzed, the wavelet neural network is designed, the neural network is optimized and solved by genetic algorithm, and the necessity of constructing the real-time prediction model of energy consumption based on wavelet neural network is established. Then it introduces the basic principle of model establishment, wavelet analysis, and shows the role of wavelet analysis in prediction model. Finally, based on the distribution structure of public buildings, this paper analyzes the energy consumption system of geothermal system, constructs the energy consumption prediction method, analyzes the over­all temperature regulation energy consumption prediction principle of building geothermal system, and realizes the real-time prediction of energy consumption of geothermal system of public buildings. The experimental results show that the energy consumption real-time prediction results of the designed method are basically similar to the actual prediction values, and the prediction efficiency is high, which can effectively reduce the energy consumption of the geothermal system of public buildings.
PAPER REVISED: 2021-11-29
PAPER ACCEPTED: 2022-02-03
CITATION EXPORT: view in browser or download as text file
THERMAL SCIENCE YEAR 2022, VOLUME 26, ISSUE Issue 3, PAGES [2373 - 2384]
  1. Bevilacqua, P., The Effectiveness of Green Roofs in Reducing Building Energy Consumptions Across Different Climates, A Summary of Literature Results, Renewable and Sustainable Energy Reviews, 151 (2021), 11, 111523
  2. Fonseca, J. A., et al., Quantifying the Uncertain Effects of Climate Change on Building Energy Consumption Across the United States, Applied Energy, 277 (2020), 1, 115556
  3. Pan, Y., Zhang, L., Data-Driven Estimation of Building Energy Consumption with Multi-Source Heterogeneous Data, Applied Energy, 268 (2020), 15, 114965
  4. Neto, A., et al., Building Energy Consumption Models Based on Smartphone User's Usage Patterns, Knowledge-Based Systems, 213 (2021), 1, 106680
  5. Zhang, G., et al., Accurate Forecasting of Building Energy Consumption Via a Novel Ensembled Deep Learning Method Considering the Cyclic Feature, Energy, 201 (2020), 15, 117531
  6. Somu, N., A Deep Learning Framework for Building Energy Consumption Forecast, Renewable and Sustainable Energy Reviews, 137 (2021), 3, 110591
  7. Wang, R., et al., A Novel Improved Model for Building Energy Consumption Prediction Based on Model Integration, Applied Energy, 262 (2020), 15, 114561
  8. Ahmed, S. F., et al., Physical and Hybrid Modelling Techniques for Earth-Air Heat Exchangers in Reducing Building Energy Consumption: Performance, Applications, Progress, and Challenges, Solar Energy, 216 (2021), 2, pp. 274-294
  9. Ghoushchi, S. J., et al., An Extended New Approach for Forecasting Short-Term Wind Power Using Modified Fuzzy Wavelet Neural Network: A Case Study in Wind Power Plant, Energy, 223 (2021), 5, 120052
  10. Tabaraki, R., Khodabakhshi, M., Performance Comparison of Wavelet Neural Network and Adaptive Neuro-Fuzzy Inference System with Small Data Sets, Journal of Molecular Graphics and Modelling, 100 (2020), 11, 107698
  11. Nanda, T., et al., Enhancing Real-Time Streamflow Forecasts with Wavelet-Neural Network-Based Error-Updating Schemes and ECMWF Meteorological Predictions in Variable Infiltration Capacity Model, Journal of Hydrology, 575 (2019), 8, pp. 890-910
  12. Sabir, Z., et al., Evolutionary Computing for Non-Linear Singular Boundary Value Problems Using Neural Network, Genetic Algorithm and Active-Set Algorithm, European Physical Journal Plus, 136 (2021), 2, pp. 1-10
  13. Luan, Y. Y., et al., Rough Set Attribute Reduction Algorithm Based on Chaotic Discrete Particle Swarm Optimization, Computer Simulation, 38 (2021), 7, pp. 271-275
  14. Cordeiro, N., et al., Fixed-Point Time Series, Repeat Survey and High-Resolution Modelling Reveal event Scale Responses of the Northwestern Iberian Upwelling, Progress In Oceanography, 190 (2021), 4, 102480
  15. La, A., et al., Bio Accessibility-Based Monitoring and Risk Assessment of Indoor Dust-Bound PAH Collected from Housing and Public Buildings: Effect of Influencing Factors, Environmental Research, 204 (2021), 3, 112039

© 2023 Society of Thermal Engineers of Serbia. Published by the Vinča Institute of Nuclear Sciences, National Institute of the Republic of Serbia, Belgrade, Serbia. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International licence