Understanding the Impact of Sensing Flexibility and Strategies on HVAC Energy Consumption Modeling
Publication: Computing in Civil Engineering 2023
ABSTRACT
Data-driven HVAC energy consumption modeling could play an important role in operating energy-efficient buildings. However, (1) existing data-driven approaches rely on rigid sensor networks to collect indoor physical parameter data, often deployed in an ad-hoc manner or based on engineering judgment; and (2) the impact of this practice on model performance is often poorly understood. To address this gap, this paper aims to assess the impact of sensor deployment configurations on HVAC energy consumption modeling, where a configuration is defined in terms of the number and locations of sensors and their flexibility (i.e., fixed or can change over time). Indoor temperature and humidity data were collected from an office building. Several configurations were defined and evaluated in predicting the HVAC consumption, using an XGBoost-based model. The results showed that sensor configurations could significantly impact the performance of HVAC consumption modeling, and that periodic changes in sensor locations could improve the performance of traditional rigid methods. The findings from this work could help define improved sensor configurations for enhanced HVAC management and operation.
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REFERENCES
Amasyali, K., and El-Gohary, N. (2018). “A review of data-driven building energy consumption prediction studies”. Renew. Sustain. Energy Rev., 81, pp. 1192–1205.
Kusiak, A., Li, M., and Zhang, Z. (2010). “A data-driven approach for steam load prediction in buildings”. Appl. Energy, 87(3), pp. 925–933.
Bae, Y., et al. (2021). “Sensor impacts on building and HVAC controls: A critical review for building energy performance”. Adv. in Appl. Energy, 4.
Antonucci, D., Oberegger, U. F., Pasut, W., and Gasparella, A. (2017). “Building performance evaluation through a novel feature selection algorithm for automated model identification procedures”. Energy and Build., 150, pp. 432–446.
Dahl, M., Brun, A., and Andresen, G. B. (2017). “Using ensemble weather predictions in district heating operation and load forecasting”. Appl. Energy, 193, pp. 445–465.
Do, H., and Cetin, K. S. (2019). “Data-driven evaluation of residential HVAC system efficiency using energy and environmental data”. Energies, 12(188).
Wang, F., Xuan, Z., Zhen, Z., Li, K., Wang, T., and Shi, M. (2020). “A day-ahead PV power forecasting method based on LSTM-RNN model and time correlation modification under partial daily pattern prediction framework”. Energy Conv. and Mgmt., 212.
Fan, C., Liao, Y., Zhou, G., Zhou, X., and Ding, Y. (2020). “Improving cooling load prediction reliability for HVAC systems using Monte-Carlo simulation to deal with uncertainties in input variables”. Energy and Build., 226.
He, D. S., Zhang, Y., Fan, X. W., and Hou, C. (2014). “Support vector machine for hourly cooling load prediction of commercial building”. Proc., 6th Intl. Conf. on Energy and Environment of Residential Buildings (ICEERB 2014).
Zhang, L., and Wen, J. (2019). “A systematic feature selection procedure for short-term data-driven building energy forecasting model development”. Energy and Build., 183, pp. 428–442.
Zhang, L., Wen, J., Li, Y., Chen, J., Ye, Y., Fu, Y., and Livingood, W. (2021). “A review of machine learning in building load prediction”. Appl. Energy, 285.
Lillstrang, M., Harju, M., Del Campo, G., Calderon, G., Röning, J., and Tamminen, S. (2022). “Implications of properties and quality of indoor sensor data for building machine learning applications: Two case studies in smart campuses”. Build. and Env., 207.
Jinhu, L., Xuemei, L., Lixing, D., and Liangzhong, J. (2010). “Applying principal component analysis and weighted support vector machine in building cooling load forecasting”. Proc., Intl. Conf. on Computer and Communication Technologies in Agriculture Engineering, Chengdu, China, pp. 434–437.
Al-Rakhami, M., Gumaei, A., Alsanad, A., Alamri, A., and Hassan, M. M. (2019). “An ensemble learning approach for accurate energy load prediction in residential buildings”. IEEE Access, 7, pp. 48328–48338.
Moradzadeh, A., Mansour-Saatloo, A., Mohammadi-Ivatloo, B., and Anvari-Moghaddam, A. (2020). “Performance evaluation of two machine learning techniques in heating and cooling loads forecasting of residential buildings”. Appl. Sci., 10.
Ortiz, M., Itard, L., and Bluyssen, P. M. (2020). “Indoor environmental quality related risk factors with energy-efficient retrofitting of housing: A literature review”. Energy and Build., 221.
Jovanović, R. Z., Sretenović, A. A., and Živković, B. D. (2015). “Ensemble of various neural networks for prediction of heating energy consumption”. Energy and Build., pp.189–199.
Sholahudin, S., and Han, H. (2016). “Simplified dynamic neural network model to predict heating load of a building using Taguchi method”. Energy, 115, pp.1672–1678.
Teh, H. Y., Kempa-Liehr, A. W., and Wang, K. I. K. (2020). “Sensor data quality: A systematic review”. J. of Big Data, 7(11).
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Published In
Computing in Civil Engineering 2023
Pages: 996 - 1004
History
Published online: Jan 25, 2024
ASCE Technical Topics:
- Architectural engineering
- Building systems
- Buildings
- Computing in civil engineering
- Data collection
- Energy consumption
- Energy efficiency
- Energy engineering
- Engineering fundamentals
- HVAC
- Mathematics
- Measurement (by type)
- Methodology (by type)
- Parameters (statistics)
- Research methods (by type)
- Sensors and sensing
- Statistics
- Structural engineering
- Structures (by type)
Authors
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