Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 678

The DOI matches its Crossref record; 64 of the 64 references it cites were found in a registry or an index.

A review of artificial intelligence based building energy use prediction: Contrasting the capabilities of single and ensemble prediction models

Wang, Zeyu, Srinivasan, Ravi S.·2017·Renewable and Sustainable Energy Reviews
DOI 10.1016/j.rser.2016.10.079
Open this paper in CoChat — free account

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.rser.2016.10.079

Cites

64 of 64 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 64 references

Research works printed but not linked: 16

  1. Korolijaa, 2013, Regression models for predicting UK office building energy consumption from heating and cooling demands, Energy Build, 59, 214, 10.1016/j.enbuild.2012.12.005 · doi:10.1016/j.enbuild.2012.12.005
  2. Roldán-Blay, 2013, Upgrade of an artificial neural network prediction method for electrical consumption forecasting using an hourly temperature curve model, Energy Build, 60, 38, 10.1016/j.enbuild.2012.12.009 · doi:10.1016/j.enbuild.2012.12.009
  3. Borges, 2013, Assessing tolerance-based robust short-term load forecasting in buildings, Energies, 6, 2110, 10.3390/en6042110 · doi:10.3390/en6042110
  4. Wu, 2013, Short term load forecasting technique based on the seasonal exponential adjustment method and the regression model, Energy Convers Manag, 70, 1, 10.1016/j.enconman.2013.02.010 · doi:10.1016/j.enconman.2013.02.010
  5. Lv, 2014, A novel dynamic modeling approach for predicting building energy performance, Appl Energy, 114, 91, 10.1016/j.apenergy.2013.08.093 · doi:10.1016/j.apenergy.2013.08.093
  6. Jung, 2015, Prediction of building energy consumption using an improved realcoded genetic algorithm based least squares support vector machine approach, Energy Build, 90, 76, 10.1016/j.enbuild.2014.12.029 · doi:10.1016/j.enbuild.2014.12.029
  7. Naji, 2016, Estimating building energy consumption using extreme learning machine method, Energy, 97, 506, 10.1016/j.energy.2015.11.037 · doi:10.1016/j.energy.2015.11.037
  8. Massana, 2016, Short-term load forecasting for non-residential buildings contrasting artificial occupancy attributes, Energy Build, 130, 519, 10.1016/j.enbuild.2016.08.081 · doi:10.1016/j.enbuild.2016.08.081
  9. Jacob, 2010, Black-box models for fault detection and performance monitoring of buildings, J Build Perform Simul, 3, 53, 10.1080/19401490903414454 · doi:10.1080/19401490903414454
  10. Wang, 2016, Artificial Intelligent Models for Improved Prediction of Residential Space Heating, J Energy Eng, 10.1061/(ASCE)EY.1943-7897.0000342 · doi:10.1061/(asce)ey.1943-7897.0000342
  11. Reid, 2007, A Rev Heterog Ensemble Methods
  12. Dietterich, 1997, Machine learning research: four current direction, AI Mag, 18, 97
  13. Siwek, 2009, Ensemble Neural Network Approach for Accurate Load Forecasting in a Power System, Int J Appl Math Comput Sci, 19, 303, 10.2478/v10006-009-0026-2 · doi:10.2478/v10006-009-0026-2
  14. Melin, 2012, A new approach for time series prediction using ensembles of ANFIS models, Expert Syst Appl, 39, 3494, 10.1016/j.eswa.2011.09.040 · doi:10.1016/j.eswa.2011.09.040
  15. Chou, 2014, Modeling heating and cooling loads by artificial intelligence for energy-efficient building design, Energy Build, 437, 10.1016/j.enbuild.2014.07.036 · DOI · Paper page
  16. Vinay K, Rao A, Kumar GH. Computerized analysis of classification of lung nodules and comparison between homogeneous and heterogeneous ensemble of classifier model In: Proceedings of the third national conference on computer vision, pattern recognition, image processing and graphics (NCVPRIPG), Hubli; 2011. · doi:10.1109/ncvpripg.2011.56

Cited by

See all 678

Metadata

Authors
Wang, Z., & Srinivasan, R. S.
Venue
Renewable and Sustainable Energy Reviews
Publisher
Elsevier BV
Published
2017-08
Volume
75
Pages
796-808
ISSN
1364-0321 (ISSN-L)
DOI
10.1016/j.rser.2016.10.079
OpenAlex
W2549906944
Cited by
678
References
64
FWCI
34.26
Type
journal article
Language
en
Open access
closed
Licence
www.elsevier.com/tdm/userlicense/1.0/ (tdm)

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

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: A review of artificial intelligence based building energy use prediction: Contrasting the capabilities of single and ensemble prediction models. https://app.cochat.ai/papers/10.1016/j.rser.2016.10.079
The paper
Wang, Z., & Srinivasan, R. S. (2017). A review of artificial intelligence based building energy use prediction: Contrasting the capabilities of single and ensemble prediction models. Renewable and Sustainable Energy Reviews. https://doi.org/10.1016/j.rser.2016.10.079

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.