Method for Determining Design Heating Load of Rural Residential Buildings Considering Indoor Temperature Uncertainty
Source abstract
Abstract
In rural locations, the application of clean heating technologies requires an appropriate design heating load. However, the variation characteristics of indoor temperatures in rural residential buildings are rarely taken into consideration by traditional techniques of calculating the design heating load, which may result in over- or under-design. As a result, a new method that took the uncertainty of the indoor temperature into account was presented to calculate the design heating load for rural residential buildings. First, for the “part-time, part-space” heating mode in rural residential buildings, an indoor temperature stochastic model was established to generate multiple indoor temperature scenarios; on the basis of this, heating loads under these scenarios were simulated and their probability distributions were counted; lastly, the design heating load was selected from the load probability distribution based on a predetermined confidence level. When the new method and the traditional method were compared, it was discovered that the new method can offer a more thorough guide to the determination of the design load value for the design of heating systems in rural residential buildings, while the traditional method’s result might not satisfy the reliability requirements.
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Research context
OpenAlex topics: Building Energy and Comfort Optimization · Urban Heat Island Mitigation
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Environmental Science and Engineering, Tianjin University, Tianjin 300072, China
Tianjin Key Lab of Building Environment and Energy, Tianjin 300072, China
Cecep Wind Power Co., Ltd., Beijing 100082, China
School of Energy and Environmental Engineering, Hebei University of Technology, Tianjin 300401, China
Funding
the National Natural Science Foundation of China
References (30)
Show source-provided references
1. Occupants’ on-demand control of individual heating devices in rural residential buildings: An experimental scheme and on-site study10.1016/j.enbuild.2022.111862 ↗
2. Exploring pathways of phasing out clean heating subsidies for rural residential buildings in China10.1016/j.eneco.2022.106411 ↗
3. Techno-economic performances of clean heating solutions to replace raw coal for heating in Northern rural China10.1016/j.enbuild.2021.110881 ↗
4. Optimization design generation of the rural residential building towards energy saving by using “Digital intelligence” technology10.1016/j.jclepro.2023.138137 ↗
5. Study on the Heating Methods of Rural Residential Building in Different Thermal Zones in Northern China10.1016/j.proeng.2016.06.411 ↗
6. Method to elucidate the spatial–temporal characteristics of the intermittent heating demand of rural houses in northern China10.1016/j.enbuild.2023.113008 ↗
7. (2012). Design Code for Heating Ventilation and Air Conditioning of Civil of Buildings (Standard No. GB 50736-2012). (In Chinese).
8. Hygrothermal behaviour of a room with different occupancy scenarios10.1016/j.jobe.2023.105928 ↗
9. Impact of the occupancy scenario on the hygrothermal performance of a room10.1016/j.buildenv.2019.106178 ↗
10. New perspectives on temporal changes in occupancy characteristics of residential buildings10.1016/j.jobe.2022.105590 ↗
11. Clustering and prediction of space cooling and heating energy consumption in high-rise resi-dential buildings with the influence of occupant behaviour: Evidence from a survey in Changsha. China10.1016/j.jobe.2023.107418 ↗
12. Indoor temperature preference setting control method for thermal comfort and energy saving based on reinforcement learning10.1016/j.jobe.2023.106805 ↗
13. Temperature-preference learning with neural networks for occupant-centric building indoor climate controls10.1016/j.buildenv.2019.01.036 ↗
14. The effects of indoor temperature and exercise behavior on thermal comfort in cold region: A field study on Xi’an, China10.1016/j.energy.2023.127258 ↗
15. Exploring HVAC system sizing under uncertainty10.1016/j.enbuild.2014.06.026 ↗
16. An uncertainty-based design optimization method for district cooling systems10.1016/j.energy.2016.02.107 ↗
17. HVAC system design under peak load prediction uncertainty using multiple-criterion decision making technique10.1016/j.enbuild.2015.01.026 ↗
18. A review of uncertainty characterisation approaches for the optimal design of distributed energy systems10.1016/j.rser.2018.02.021 ↗
19. Robust optimal design of building cooling sources considering the uncertainty and cross-correlation of demand and source10.1016/j.apenergy.2020.114793 ↗
20. Uncertainty in peak cooling load calculations10.1016/j.enbuild.2010.01.013 ↗
21. A novel stochastic modeling method to simulate cooling loads in residential districts10.1016/j.apenergy.2017.08.038 ↗
22. Method of constructing stochastic near-extreme daily weather data for efficient calculation of probabilistic load in air-conditioning system design10.1016/j.buildenv.2022.109278 ↗
23. Grimmett, G., and Stirzaker, D. (2001). Probability and Random Processes, Oxford University Press.10.1093/oso/9780198572237.001.0001 ↗
24. Simulation and optimization of the key parameters of combined floor and Kang heating terminal based on differentiated thermal demands10.1016/j.enbuild.2021.111624 ↗
25. A study on thermal characteristic and sleeping comfort of a hybrid solar heating system applied in cold rural areas10.1016/j.enbuild.2018.10.027 ↗
26. EnergyPlus (2024, February 20). EnergyPlus Simulation Software. Available online: https://www.energyplus.net.
27. (2013). Design Standard for Energy Efficiency of Rural Residential Buildings (Standard No. GB/T 50824-2013). (In Chinese).
28. Calculation and Analysis of Heat Load Indicators for Residential Heating in Villages and Towns in China
29. Li, J., Zou, Y., and Liu, J. (2012). Study on Building Envelope Optimization Parameters and Energy-saving Rate of Rural Residential Buildings in Severe Cold and Cold Zones. Build. Sci., 1002–8528.
30. Li, T. (2020). Study on Differentiated Thermal Requirements in Temporal and Spatial Dimensions and the Optimization of Heating System in Rural Residential Buildings in Northwest China, Xi’an University of Architecture and Technology.