skip to main content

Building energy consumption prediction method based on Bayesian regression and thermal inertia correction

1Marketing Service Center, State Grid Jiangsu Electric Power Co., Ltd., Nanjing, China

2Marketing Department, State Grid Anhui Electric Power Co., Ltd., Hefei, China

Received: 7 Sep 2023; Revised: 28 Oct 2023; Accepted: 11 Nov 2023; Available online: 23 Nov 2023; Published: 1 Jan 2024.
Editor(s): H Hadiyanto
Open Access Copyright (c) 2024 The Author(s). Published by Centre of Biomass and Renewable Energy (CBIORE)
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Citation Format:
Abstract
The accurate prediction of building energy consumption is a crucial prerequisite for demand response (DR) and energy efficiency management of buildings. Nevertheless, the thermal inertia and probability distribution characteristics of energy consumption are frequently ignored by traditional prediction methods. This paper proposes a building energy consumption prediction method based on Bayesian regression and thermal inertia correction. The thermal inertia correction model is established by introducing an equivalent temperature variable to characterize the influence of thermal inertia on temperature. The equivalent temperature is described as a linear function of the actual temperature, and the key parameters of the function are optimized through genetic algorithm (GA). Using historical energy usage, temperature, and date type as inputs and future building energy comsuption as output, a Bayesian regression prediction model is established. Through Bayesian inference, combined with prior information on building energy usage data, the posterior probability distribution of building energy usage is inferred, thereby achieving accurate forecast of building energy consumption.  The case study is conducted using energy consumption data from a commercial building in Nanjing. The results of the case study indicate that the proposed thermal inertia correction method is effective in narrowing the distribution of temperature data from a range of 24.5°C to 36.5°C to a more concentrated range of 26.5°C to 34°C, thereby facilitating a more focused and advantageous data distribution for predictions. Upon applying the thermal inertia correction method, the relative errors of the Radial Basis Function (RBF) and Deep Belief Network (DBN) decreases by 2.0% and 3.1% respectively, reaching 10.9% and 7.0% correspondingly. Moreover, with the utilization of Bayesian regression, the relative error further decreases to 4.4%. Notably, the Bayesian regression method not only achieves reduced errors but also provides probability distribution, demonstrating superiority over traditional methods.
Fulltext View|Download
Keywords: Building energy consumption; thermal inertia correction; Bayesian regression
Funding: Science and Technology Project of State Grid Corporation of China(5400-202320224A-1-1-ZN)

Article Metrics:

  1. Almalaq A. & Zhang, J. (2019). Evolutionary Deep Learning-Based Energy Consumption Prediction for Buildings. IEEE Access, 7, 1520-1531. https://doi.org/10.1109/ACCESS.2018.2887023
  2. Bedi, G., Venayagamoorthy, G., & Singh, R. (2020). Development of an IoT-Driven Building Environment for Prediction of Electric Energy Consumption. IEEE Internet of Things Journal, 7(6), 4912-4921. https://doi.org/10.1109/jiot.2020.2975847
  3. Cai, M., Pipattanasomporn, M. and Rahman, S. (2019). Day-ahead building-level load forecasts using deep learning vs. traditional time-series techniques. Applied Energy, 236, 1078–1088. https://doi.org/10.1016/j.apenergy.2018.12.042
  4. Cao, W., Yu, J., Chao, M., Wang, J., Yang, S., Zhou, M. & Wang, M. (2023). Short-term energy consumption prediction method for educational buildings based on model integration. Energy, 283, 128580. https://doi.org/10.1016/j.energy.2023.128580
  5. Chae, Y., Horesh, R., Hwang, Y., Lee, Y.M. (2016). An artificial neural network model for forecasting sub-hourly electricity usage in commercial buildings. Energy Build, 111, 184–94. https://doi.org/10.1016/j.enbuild.2015.11.045
  6. Chang, G.W. and Lu, H.J. (2020) Integrating GRAY Data Preprocessor and Deep Belief Network for Day-Ahead PV Power Output Forecast. IEEE Transactions on Sustainable Energy, 11(1), 185–194. https://doi.org/10.1109/tste.2018.2888548
  7. Chen, Y., Luo, F., Dong, Z., Meng, K., Ranzi, G., & Wong K. P. (2018). A day-ahead scheduling framework for thermostatically controlled loads with thermal inertia and thermal comfort model. Journal of Modern Power Systems and Clean Energy, 7(3), 568–578. https://doi.org/10.1007/s40565-018-0431-3
  8. Cheng, L., Zang, H., Xu, Y., Wei, Z. & Sun, G. (2021). Probabilistic Residential Load Forecasting Based on Micrometeorological Data and Customer Consumption Pattern. IEEE Transactions on Power Systems, 36(4), 3762-3775. https://doi.org/10.1109/tpwrs.2021.3051684
  9. Dakir, S., Boukas, I., Lemort, V., & Cornélusse B. (2020). Sizing and operation of an isolated microgrid with building thermal dynamics and cold storage. IEEE Transactions on Industry Applications, 56(5), 5375–5384. https://doi.org/10.1109/tia.2020.3005370
  10. De Angelis, F., Boaro, M., Fuselli, D., Squartini, S., Piazza, F., & Wei Q. (2013). Optimal home energy management under dynamic electrical and thermal constraints. IEEE Trans. Ind. Informat., 9(3), 518–1527. https://doi.org/10.1109/tii.2012.2230637
  11. Esmaeili Shayan, M., Najafi, G., Ghobadian, B., Gorjian, S., & Mazlan, M. (2022). Sustainable Design of a Near-Zero-Emissions Building Assisted by a Smart Hybrid Renewable Microgrid. International Journal of Renewable Energy Development, 11(2), 471-480. https://doi.org/10.14710/ijred.2022.43838
  12. Fan S. (2019) Research on Deep Learning Energy Consumption Prediction Based on Generating Confrontation Network. IEEE Access, 7, 165143-165154. https://doi.org/10.1109/ACCESS.2019.2949030
  13. Fang, H., Tan, H., Dai, N. & Yuan, X. (2021). Day-ahead Prediction Method of Hourly Building Energy Consumption in Transition Season. 2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS), Xi'an, China, 376-380. https://doi.org/10.1109/ccis53392.2021.9754671
  14. Jiang, R., Zeng, S., Song, Q. & Wu, Z. (2023). Deep-Chain Echo State Network With Explainable Temporal Dependence for Complex Building Energy Prediction. IEEE Transactions on Industrial Informatics, 19(1), 426-435. https://doi.org/10.1109/tii.2022.3194842
  15. Lauricella, M., & Fagiano, L., (2023). Day-Ahead and Intra-Day Building Load Forecast With Uncertainty Bounds Using Small Data Batches. IEEE Transactions on Control Systems Technology, 31(6), 2584-2595. https://doi.org/10.1109/TCST.2023.3274955
  16. Lee, Z., Lin, Y., Chen, Z., Yang, Z., Fang, W, & Lee, C. (2023). Ensemble Deep Learning Applied to Predict Building Energy Consumption. 2023 IEEE 6th Eurasian Conference on Educational Innovation (ECEI), Singapore, Singapore, 339-341. https://doi.org/10.1109/ecei57668.2023.10105266
  17. Li, T., Fong, S., Li, X., Lu, Z. & Gandomi, A. (2020). Swarm Decision Table and Ensemble Search Methods in Fog Computing Environment: Case of Day-Ahead Prediction of Building Energy Demands Using IoT Sensors. IEEE Internet of Things Journal, 7(3), 2321-2342. https://doi.org/10.1109/JIOT.2019.2958523
  18. Li. W. (2023) Energy consumption prediction of public buildings based on PCA-RF-AdaBoost. 2023 IEEE 2nd International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA), Changchun, China, 1193-1197. https://doi.org/10.1109/eebda56825.2023.10090762
  19. Lin, X., Zamora, R., Baguley, C. A., & Srivastava, A. K. (2023). A hybrid Short-Term load Forecasting approach for individual residential customer. IEEE Transactions on Power Delivery, 38(1), 26–37. https://doi.org/10.1109/tpwrd.2022.3178822
  20. Liu, Y., Liang, Z. & Li, X. (2023). Enhancing Short-Term Power Load Forecasting for Industrial and Commercial Buildings: A Hybrid Approach Using TimeGAN, CNN, and LSTM. IEEE Open Journal of the Industrial Electronics Society, 4, 451-462. https://doi.org/10.1109/OJIES.2023.3319040
  21. Löschenbrand, M., Gros, S. and Lakshmanan, V. (2021). Generating scenarios from probabilistic short-term load forecasts via non-linear Bayesian regression. 2021 International Conference on Smart Energy Systems and Technologies (SEST). https://doi.org/10.1109/sest50973.2021.9543288
  22. Lu, S., Gu, W., Ding, S., Yao, S., Lu, H., & Yuan, X. (2022). Data-Driven Aggregate Thermal Dynamic Model for Buildings: A Regression approach. IEEE Transactions on Smart Grid, 13(1), 227–242. https://doi.org/10.1109/tsg.2021.3101357
  23. Luo, X., Oyedele, L., Ajayi, A., et al. (2020). Feature extraction and genetic algorithm enhanced adaptive deep neural network for energy consumption prediction in buildings. Renew Sustain Energy Rev, 131, 109980. https://doi.org/10.1016/j.rser.2020.109980
  24. Mathankumar, M., Thirumoorthi, P., & Viswanathan, T. (2021). An Improved Clustering Scheme for Underwater Sensor Network using Bayesian Linear Regression. 2021 International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), Coimbatore, India, 1-4. https://doi.org/10.1109/ICAECA52838.2021.9675767
  25. Miller, C., Picchetti, B., Fu, C., & Pantelic J. (2022). Limitations of machine learning for building energy prediction: ASHRAE Great Energy Predictor III Kaggle competition error analysis. Science and Technology for the Built Environment, 1, 1-18. https://doi.org/10.1080/23744731.2022.2067466
  26. Mohapatra, S., Mishra, S., & Tripathy, H. (2022). Energy Consumption Prediction in Electrical Appliances of Commercial Buildings Using LSTM-GRU Model. 2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC), Bhubaneswar, India, 1-5. https://doi.org/10.1109/assic55218.2022.10088334
  27. Moradzadeh, A., Moayyed, H., Mohammadi-Ivatloo, B., Aguiar, A.P. (2022). A secure federated Deep Learning-Based approach for heating load demand forecasting in building environment. IEEE Access, 10, 5037–5050. https://doi.org/10.1109/access.2021.3139529
  28. Moradzadeh, A., Mohammadi-Ivatloo, B., Abapour. M., Anvari-Moghaddam, A., & Roy, S. S. (2022). Heating and cooling loads forecasting for residential buildings Based on Hybrid Machine Learning Applications: A Comprehensive review and Comparative analysis. IEEE Access, 10, 2196–2215. https://doi.org/10.1109/access.2021.3136091
  29. Narayanan, M. (2017). Techno-Economic Analysis of Solar Absorption Cooling for Commercial Buildings in India. International Journal of Renewable Energy Development, 6(3), 253-262. https://doi.org/10.14710/ijred.6.3.253-262
  30. Phyo, P.P. and Jeenanunta, C. (2021) Daily load forecasting based on a combination of classification and regression tree and deep belief network. IEEE Access, 9, 152226–152242. https://doi.org/10.1109/access.2021.3127211
  31. Qin, D., Wang, C., Wen, Q., Chen, W., Sun, L. & Wang, Y. (2023). Personalized Federated DARTS for Electricity Load Forecasting of Individual Buildings. IEEE Transactions on Smart Grid, 14(6), 4888-4901. https://doi.org/10.1109/TSG.2023.3253855
  32. Ramos, D., Faria, P., Gomes, L. & Vale, Z. (2022). A Contextual Reinforcement Learning Approach for Electricity Consumption Forecasting in Buildings. IEEE Access, 10, 61366-61374. https://doi.org/10.1109/ACCESS.2022.3180754
  33. Syed, D., Abu-Rub, H., Ghrayeb, A., & Refaat, S. S. (2021). Household-Level energy forecasting in smart buildings using a novel hybrid deep learning model. IEEE Access, 9, 33498–33511. https://doi.org/10.1109/access.2021.3061370
  34. Tang, Y., Liu, H., Xie, Y., Zhai, J. & Wu, X., (2019) Short-Term forecasting of electricity and gas demand in Multi-Energy system based on RBF-NN model. 2019 IEEE International Conference on Energy Internet (ICEI). https://doi.org/10.1109/icei.2019.00102
  35. Vijayan, P. (2022). Energy Consumption Prediction in Low Energy Buildings using Machine learning and Artificial Intelligence for Energy Efficiency. 2022 8th International Youth Conference on Energy (IYCE), Hungary, 1-6. https://doi.org/10.1109/iyce54153.2022.9857548
  36. Wang, C., Qin, D., Wen, Q., Zhou, T., Sun, L. & Wang, Y. (2022). Adaptive probabilistic load forecasting for individual buildings. iEnergy, 1(3), 341-350. https://doi.org/10.23919/IEN.2022.0041
  37. Wang, J., Chen, X., Zhang, F., Chen, F., & Xin, Y. (2021) Building Load Forecasting Using Deep Neural Network with Efficient Feature Fusion. Journal of Modern Power Systems and Clean Energy, 9(1), 160–169. https://doi.org/10.35833/mpce.2020.000321
  38. Wang, L., Xie, D., Zhou, L., & Zhang, Z. (2023). Application of the hybrid neural network model for energy consumption prediction of office buildings. Journal of Building Engineering, 72, 106503. https://doi.org/10.1016/j.jobe.2023.106503
  39. Wu, J., Huang, L., & Pan, X. (2010). A Novel Bayesian Additive Regression Trees Ensemble Model Based on Linear Regression and Nonlinear Regression for Torrential Rain Forecasting. 2010 Third International Joint Conference on Computational Science and Optimization, Huangshan, China, 466-470. https://doi.org/10.1109/CSO.2010.15
  40. Xu, Y., Yao, L., Xu, P., Cui, W., Zhang, Z., Liu, F., Mao, B., & Wen, Z. (2021). Load Forecasting Method for Building Energy Systems Based On Modified Two-Layer LSTM. 2021 3rd Asia Energy and Electrical Engineering Symposium (AEEES). https://doi.org/10.1109/aeees51875.2021.9403131
  41. Yu, J., & Ge, L. (2022). Application of Internet of Things Technology in Building Energy Consumption Intelligent Monitoring and Prediction System. 2022 International Conference on Applied Physics and Computing (ICAPC), Ottawa, ON, Canada, 301-305. https://doi.org/10.1109/icapc57304.2022.00063
  42. Zhang, X., Zhong, M., Dou, Z., Chen, H., & Liu, K. (2021). Energy Consumption Forecast of Building Models in College Town——A Case study in China. 2021 IEEE 5th Conference on Energy Internet and Energy System Integration (EI2), Taiyuan, China, 4061-4065. https://doi.org/10.1109/ei252483.2021.9713421
  43. Zhou, X., Lin, W., Kumar, R., Cui, P., Ma, Z. (2022). A data-driven strategy using long short term memory models and reinforcement learning to predict building electricity consumption. Applied Energy, 306, 118078. https://doi.org/10.1016/j.apenergy.2021.118078

Last update:

No citation recorded.

Last update: 2024-03-03 12:38:25

No citation recorded.