CoChat
Sign Up
Cited by 22Building Energy and Comfort OptimizationEnergy Load and Power ForecastingRefrigeration and Air Conditioning Technologies

The DOI matches its Crossref record.

Adaptive corrected parameters algorithm applied in cooling load prediction based on black-box model: A case study for subway station

Hu, Yuanyang, Qin, Luwen, Li, Shuhong, Li, Xiaohuan, Zhou, Runfa, Li, Yanjun, Sheng, Wei·2023·Energy and Buildings
DOI 10.1016/j.enbuild.2023.113429
Open this paper in CoChat — free account

Overview

Integrity signals

Registry

Crossref · 10.1016/j.enbuild.2023.113429

Cites

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cites

No references could be resolved yet.

Research works printed but not linked: 50

  1. Kaeriyama, 2023, Warming climate impacts on production dynamics of southern populations of Pacific salmon in the North Pacific Ocean, Fish. Oceanogr., 32, 121, 10.1111/fog.12598 · DOI
  2. Phillips, 2022, Biocrusts mediate a new mechanism for land degradation under a changing climate, Nat. Clim. Chang., 12, 71, 10.1038/s41558-021-01249-6 · DOI
  3. Shao, 2022, Intraspecific responses of plant productivity and crop yield to experimental warming: a global synthesis, Sci. Total Environ., 840, 10.1016/j.scitotenv.2022.156685 · DOI
  4. Ji, 2023, Energy consumption and carbon emissions: measurement and analysis—the case of Shanghai in China, Waste Biomass Valori., 14, 365, 10.1007/s12649-022-01876-w · DOI
  5. Röck, 2020, Embodied GHG emissions of buildings – The hidden challenge for effective climate change mitigation, Appl. Energy, 258, 10.1016/j.apenergy.2019.114107 · DOI
  6. Sun, 2017, A solar heating and cooling system in a nearly zero-energy building: a case study in China, Int. J. Photoenergy, 2017, 1
  7. Song, 2019, Development of an energy saving strategy model for retrofitting existing buildings: a Korean case study, Energies, 12, 1626, 10.3390/en12091626 · DOI
  8. Akram, 2022, Global Prospects, Advance technologies and policies of energy-saving and sustainable building systems: A review, Sustainability-Basel, 14, 1316, 10.3390/su14031316 · DOI
  9. Li, 2021, Modelling heating and cooling energy demand for building stock using a hybrid approach, Energ. Build., 235, 10.1016/j.enbuild.2021.110740 · DOI
  10. Li, 2020, Integration of daytime radiative cooling and solar heating for year-round energy saving in buildings, Nat. Commun., 11, 6101, 10.1038/s41467-020-19790-x · DOI
  11. Sharif, 2019, Simulation-based multi-objective optimization of institutional building renovation considering energy consumption, life-cycle cost and life-cycle assessment, J Build Eng., 21, 429, 10.1016/j.jobe.2018.11.006 · DOI
  12. Mehregan, 2022, Building energy model validation and estimation using heating and cooling degree days (HDD–CDD) based on accurate base temperature, Energy Sci. Eng., 10, 3638, 10.1002/ese3.1246 · DOI
  13. Hou, 2022, Prediction and optimization of thermal comfort, IAQ and energy consumption of typical air-conditioned rooms based on a hybrid prediction model, Build. Environ., 225, 10.1016/j.buildenv.2022.109576 · DOI
  14. Royapoor, 2015, Building model calibration using energy and environmental data, Energ. Build., 94, 109, 10.1016/j.enbuild.2015.02.050 · DOI
  15. Kaushik, 2014, Thermal performance prediction and energy conservation potential of earth air tunnel heat exchanger for thermal comfort in building, J. Renew. Sustain. Ener., 6, 13107, 10.1063/1.4861782 · DOI
  16. Wang, 2021, Numerical study on performance optimization of an energy-saving insulated window, Sustainability-Basel, 13, 935, 10.3390/su13020935 · DOI
  17. Braun, 2002, An inverse gray-box model for transient building load prediction, Hvac&R Res., 8, 73, 10.1080/10789669.2002.10391290 · DOI
  18. Foucquier, 2013, State of the art in building modelling and energy performances prediction: a review, Renew. Sustain. Energy Rev., 23, 272, 10.1016/j.rser.2013.03.004 · DOI
  19. Zhang, 2021, A review of machine learning in building load prediction, Appl. Energy, 285, 10.1016/j.apenergy.2021.116452 · DOI
  20. Chen, 2022, Physical energy and data-driven models in building energy prediction: a review, Energy Rep., 8, 2656, 10.1016/j.egyr.2022.01.162 · DOI
  21. Lee, 2021, Prediction performance analysis of artificial neural network model by input variable combination for residential heating loads, Energies, 14, 756, 10.3390/en14030756 · DOI
  22. Eseye, 2020, Short-term forecasting of heat demand of buildings for efficient and optimal energy management based on integrated machine learning models, IEEE T Ind. Inform., 16, 7743, 10.1109/TII.2020.2970165 · DOI
  23. Jang, 2019, Development of an improved model to predict building thermal energy consumption by utilizing feature selection, Energies, 12, 4187, 10.3390/en12214187 · DOI
  24. Kapetanakis, 2017, Input variable selection for thermal load predictive models of commercial buildings, Energ. Build., 137, 13, 10.1016/j.enbuild.2016.12.016 · DOI
  25. Ling, 2021, An improved input variable selection method of the data-driven model for building heating load prediction, J. Build. Eng., 44
  26. Ding, 2018, Model input selection for building heating load prediction: A case study for an office building in Tianjin, Energ. Build., 159, 254, 10.1016/j.enbuild.2017.11.002 · DOI
  27. Fan, 2019, Cooling load prediction and optimal operation of HVAC systems using a multiple nonlinear regression model, Energ. Build., 197, 7, 10.1016/j.enbuild.2019.05.043 · DOI
  28. Al-Shargabi, 2021, Tuning deep neural networks for predicting energy consumption in arid climate based on buildings characteristics, Sustainability (Basel, Switzerland), 13, 12442
  29. Guo, 2023, Prediction of heating and cooling loads based on light gradient boosting machine algorithms, Build. Environ., 236, 10.1016/j.buildenv.2023.110252 · DOI
  30. Islam, 2017, Development of chaotically improved meta-heuristics and modified BP neural network-based model for electrical energy demand prediction in smart grid, Neural Comput. & Applic., 28, 877, 10.1007/s00521-016-2408-3 · DOI
  31. Moayedi, 2021, Suggesting a stochastic fractal search paradigm in combination with artificial neural network for early prediction of cooling load in residential buildings, Energies, 14, 1649, 10.3390/en14061649 · DOI
  32. Moayedi, 2021, Double-target based neural networks in predicting energy consumption in residential buildings, Energies, 14, 1331, 10.3390/en14051331 · DOI
  33. Liu, 2016, A hybrid model of AR and PNN method for building thermal load forecasting, Asian Simulation Conference, 643, 146
  34. Tan, 2022, Room thermal load prediction based on analytic hierarchy process and back-propagation neural networks, Build. Simul.-China, 15, 1989, 10.1007/s12273-022-0905-0 · DOI
  35. Chen, 2022, A training pattern recognition algorithm based on weight clustering for improving cooling load prediction accuracy of HVAC system, J. Build. Eng., 52
  36. Himmetoğlu, 2021, PSACONN mining algorithm for multi-factor thermal energy-efficient public building design, J Build Eng., 34
  37. Kim, 2019, Cooling load forecasting via predictive optimization of a Nonlinear Autoregressive Exogenous (NARX) neural network model, Sustainability-Basel, 6535, 1
  38. Hyndman, 1996, Sample quantiles in statistical packages, Am. Statistician, 50, 361, 10.1080/00031305.1996.10473566 · DOI
  39. Zhu, 2019, Multiple ant colony optimization based on Pearson correlation coefficient, IEEE Access, 7, 61628, 10.1109/ACCESS.2019.2915673 · DOI
  40. Kim, 2020, Impact of correlation of plug load data, occupancy rates and local weather conditions on electricity consumption in a building using four back-propagation neural network models, Sustain. Cities Soc., 62, 10.1016/j.scs.2020.102321 · DOI
  41. Bolandnazar, 2020, Energy consumption forecasting in agriculture by artificial intelligence and mathematical models, Energy Sources. Part a, Recovery, Utilization, and Environmental Effects, 42, 1618, 10.1080/15567036.2019.1604872 · DOI
  42. Wang, 2018, Random Forest based hourly building energy prediction, Energ. Build., 171, 11, 10.1016/j.enbuild.2018.04.008 · DOI
  43. Ding, 2018, Effect of input variables on cooling load prediction accuracy of an office building, Appl. Therm. Eng., 128, 225, 10.1016/j.applthermaleng.2017.09.007 · DOI
  44. Liu, 2022, Short-term load forecasting of multi-energy in integrated energy system based on multivariate phase space reconstruction and support vector regression mode, Electr. Pow. Syst. Res., 210, 10.1016/j.epsr.2022.108066 · DOI
  45. Zhang, 2021, Predictive model of cooling load for ice storage air-conditioning system by using GBDT, Energy Rep., 7, 1588, 10.1016/j.egyr.2021.03.017 · DOI
  46. Yang, 2014, Flower pollination algorithm: a novel approach for multiobjective optimization, Eng. Optimization, 46, 1222, 10.1080/0305215X.2013.832237 · DOI
  47. Fan, 2020, Hybrid support vector machines with heuristic algorithms for prediction of daily diffuse solar radiation in air-polluted regions, Renew. Energy, 145, 2034, 10.1016/j.renene.2019.07.104 · DOI
  48. Mergos, 2021, Flower pollination algorithm parameters tuning, Soft. Comput., 25, 14429, 10.1007/s00500-021-06230-1 · DOI
  49. Li, 2022, Artificial neural network model for predicting the local compression capacity of stirrups-confined concrete, Structures, 41, 943, 10.1016/j.istruc.2022.05.055 · DOI
  50. Al-Mukhtar, 2019, Random Forest, support vector machine, and neural networks to modelling suspended sediment in Tigris River-Baghdad, Environ. Monit. Assess., 191, 673, 10.1007/s10661-019-7821-5 · DOI

Cited by

Sorted by citations

See all 22

Metadata

Authors
Hu, Y., Qin, L., Li, S., Li, X., Zhou, R., Li, Y., & Sheng, W.
Affiliations
  • Southeast University (CN)
  • Nanjing Product Quality Supervision and Inspection Institute (CN)
Corresponding author
Li, Shuhong
ORCID
Venue
Energy and Buildings
Publisher
Elsevier BV
Published
2023-10
Volume
297
Pages
113429
ISSN
0378-7788 (ISSN-L)
DOI
10.1016/j.enbuild.2023.113429
OpenAlex
W4385633477
Cited by
22
References
0
Citations by year
2026: 5 · 2025: 8 · 2024: 9
FWCI
2.65
Type
journal article
Language
en
Open access
closed
Licence
Locations
Energy and Buildings (publishedVersion)
Topics
  • Physical Sciences › Engineering › Building and Construction › Building Energy and Comfort Optimization
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Energy Load and Power Forecasting
  • Physical Sciences › Engineering › Mechanical Engineering › Refrigeration and Air Conditioning Technologies
Keywords
cooling load prediction, black-box models, subway stations, feature selection, building load forecasting, BP neural network, radial basis function neural network, random forest, air conditioning

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review