Ventilation System Heating Demand Forecasting Based on Long Short-Term Memory Network

doi:10.1007/s12204-021-2277-5

J Shanghai Jiaotong Univ Sci ›› 2021, Vol. 26 ›› Issue (2): 129-137.doi: 10.1007/s12204-021-2277-5

• • 下一篇

Ventilation System Heating Demand Forecasting Based on Long Short-Term Memory Network

ZHANG Zhanluo (张战罗), ZHANG Zhinan (张执南), EIKEVIK Trygve Magne, SMITT Silje Marie

(1. School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China; 2. Student Innovation
Center, Shanghai Jiao Tong University, Shanghai 200240, China; 3. Department of Energy and Process Engineering,
Norwegian University of Science and Technology, Trondheim 7491, Norway)

出版日期:2021-04-28 发布日期:2021-03-24
通讯作者: ZHANG Zhinan (张执南) E-mail: zhinanz@sjtu.edu.cn

Ventilation System Heating Demand Forecasting Based on Long Short-Term Memory Network

ZHANG Zhanluo (张战罗), ZHANG Zhinan (张执南), EIKEVIK Trygve Magne, SMITT Silje Marie

(1. School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China; 2. Student Innovation
Center, Shanghai Jiao Tong University, Shanghai 200240, China; 3. Department of Energy and Process Engineering,
Norwegian University of Science and Technology, Trondheim 7491, Norway)

Online:2021-04-28 Published:2021-03-24
Contact: ZHANG Zhinan (张执南) E-mail: zhinanz@sjtu.edu.cn

摘要/Abstract

摘要： Load forecasting can increase the efficiency of modern energy systems with built-in measuring systems by providing a more accurate peak power shaving performance and thus more reliable control. An analysis of an integrated CO2 heat pump and chiller system with a hot water storage system is presented in this paper. Drastic power fluctuations, which can be reduced with load forecasting, are found in historical operation records. A model that aims to forecast the ventilation system heating demand is thus established on the basis of a long short-term memory (LSTM) network. The model can successfully forecast the one-hour-ahead power using records of the past 48 h of the system operation data and the ambient temperature. The mean absolute percentage error (MAPE) of the forecast results of the LSTM-based model is 10.70%, which is respectively 2.2% and 7.25% better than the MAPEs of the forecast results of the support vector regression based and persistence method based models.

关键词: ventilation system, load forecasting, long short-term memory (LSTM), walk-forward forecasting

Abstract: Load forecasting can increase the efficiency of modern energy systems with built-in measuring systems by providing a more accurate peak power shaving performance and thus more reliable control. An analysis of an integrated CO2 heat pump and chiller system with a hot water storage system is presented in this paper. Drastic power fluctuations, which can be reduced with load forecasting, are found in historical operation records. A model that aims to forecast the ventilation system heating demand is thus established on the basis of a long short-term memory (LSTM) network. The model can successfully forecast the one-hour-ahead power using records of the past 48 h of the system operation data and the ambient temperature. The mean absolute percentage error (MAPE) of the forecast results of the LSTM-based model is 10.70%, which is respectively 2.2% and 7.25% better than the MAPEs of the forecast results of the support vector regression based and persistence method based models.

Key words: ventilation system, load forecasting, long short-term memory (LSTM), walk-forward forecasting

中图分类号:

TU 833

ZHANG Zhanluo (张战罗), ZHANG Zhinan (张执南), EIKEVIK Trygve Magne, SMITT Silje Marie. Ventilation System Heating Demand Forecasting Based on Long Short-Term Memory Network[J]. J Shanghai Jiaotong Univ Sci, 2021, 26(2): 129-137.

参考文献 19

[1]	SHAHNAZARI H, MHASKAR P, HOUSE J M, et al.Heating, ventilation and air conditioning systems:Fault detection and isolation and safe parking [J].Computers & Chemical Engineering, 2018, 108: 139-151.
[2]	HUANG Y, NIU J L. A review of the advance of HVAC technologies as witnessed in ENB publications in the period from 1987 to 2014 [J]. Energy and Buildings,2016, 130: 33-45.
[3]	SONG M J, MAO N, XU Y J, et al. Challenges in, and the development of, building energy saving techniques,illustrated with the example of an air source heat pump[J]. Thermal Science and Engineering Progress, 2019,10: 337-356.
[4]	MARUYAMA K, YOON G, WATANABE T. Simple thermal energy storage tank for improving the energy efficiency of an existing air-conditioning system [J].IOP Conference Series: Earth and Environmental Science,2019, 238: 012057.
[5]	BEGHI A, CECCHINATO L, RAMPAZZO M, et al.Load forecasting for the efficient energy management of HVAC systems [C]//Proceedings of the 2010 IEEE International Conference on Sustainable Energy Technologies.Piscataway: IEEE, 2010: 1-6.
[6]	PAPALEXOPOULOS A D, HESTERBERG T C. A regression-based approach to short-term system load forecasting [J]. IEEE Transactions on Power Systems,1990, 5(4): 1535-1547.
[7]	VU D H, MUTTAQI K M, AGALGAONKAR A P. Short-term load forecasting using regression based moving windows with adjustable window-sizes[C]//Proceedings of the 2014 IEEE Industry Application Society Annual Meeting. Piscataway: IEEE, 2014:1-8.
[8]	CHO M Y, HWANG J C, CHEN C S. Customer short term load forecasting by using ARIMA transfer function model [C]//Proceedings of the 1995 International Conference on Energy Management and Power Delivery.Piscataway: IEEE, 1995: 317-322.
[9]	LEE C M, KO C N. Short-term load forecasting using lifting scheme and ARIMA models [J]. Expert Systems with Applications, 2011, 38(5): 5902-5911.
[10]	JAIN R K, SMITH K M, CULLIGAN P J, et al. Forecasting energy consumption of multi-family residential buildings using support vector regression: Investigating the impact of temporal and spatial monitoring granularity on performance accuracy [J]. Applied Energy, 2014, 123: 168-178.
[11]	LI Q, MENG Q L, CAI J J, et al. Predicting hourly cooling load in the building: A comparison of support vector machine and different artificial neural networks[J]. Energy Conversion and Management, 2009, 50(1):90-96.
[12]	FAN C, XIAO F, WANG S W. Development of prediction models for next-day building energy consumption and peak power demand using data mining techniques[J]. Applied Energy, 2014, 127: 1-10.
[13]	ZHAO D Y, ZHONG M, ZHANG X, et al. Energy consumption predicting model of VRV (variable refrigerant volume) system in office buildings based on data mining [J]. Energy, 2016, 102: 660-668.
[14]	LECUN Y, BENGIO Y, HINTON G. Deep learning[J]. Nature, 2015, 521(7553): 436-444.
[15]	KONG W C, DONG Z Y, JIA Y W, et al. Short-term residential load forecasting based on LSTM recurrent neural network [J]. IEEE Transactions on Smart Grid,2019, 10(1): 841-851.
[16]	CAI M M, PIPATTANASOMPORN M, RAHMAN S. Day-ahead building-level load forecasts using deep learning vs. traditional time-series techniques [J]. Applied Energy, 2019, 236: 1078-1088.
[17]	MOHAMED A R, DAHL G E, HINTON G. Acoustic modeling using deep belief networks [J]. IEEE Transactions on Audio, Speech, and Language Processing,2012, 20(1): 14-22.
[18]	HINTON G, DENG L, YU D, et al. Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups [J]. IEEE Signal Processing Magazine, 2012, 29(6): 82-97.
[19]	KINGMA D P, BA J L. Adam: A method for stochastic optimization [EB/OL]. [2019-12-31].https://arxiv.org/pdf/1412.6980.pdf.

Ventilation System Heating Demand Forecasting Based on Long Short-Term Memory Network

Ventilation System Heating Demand Forecasting Based on Long Short-Term Memory Network

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献 19

相关文章 0

编辑推荐

Metrics

本文评价