CoChat
Sign Up
Cited by 180Integrated Energy Systems OptimizationSmart Grid Energy ManagementElectric Power System Optimization

Digital twins based day-ahead integrated energy system scheduling under load and renewable energy uncertainties

Open access
You, Minglei, Wang, Qian, Sun, Hongjian, Castro, Iván, Jiang, Jing·2022·Applied Energy
DOI 10.1016/j.apenergy.2021.117899
Open this paper in CoChat — free account

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.apenergy.2021.117899

Cites

Statistics

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

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?

Self-check

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

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

Cites

No references could be resolved yet.

Research works printed but not linked: 22

  1. [1][1] Skarvelis-Kazakos S, Papadopoulos P, Grau Unda I, Gorman T, Belaidi A, Zigan S. Multiple energy carrier optimisation with intelligent agents. Applied Energy 2016; 167:323-35. doi: 10.1016/j.apenergy.2015.10.130 . - · DOI
  2. [2][2] Wang H, Gu C, Zhang X, Li F. Optimal CHP planning in integrated energy systems considering network charges. IEEE Systems Journal 2020; 14(2):2684-93. doi: 10.1109/JSYST.2019.2921218 . - · DOI
  3. [3][3] Moeini-Aghtaie M, Dehghanian P, Fotuhi-Firuzabad M, Abbaspour A. Multiagent genetic algorithm: an online probabilistic view on economic dispatch of energy hubs constrained by wind availability. IEEE Transactions on Sustainable Energy 2014; 5(2):699-708. doi: 10.1109/TSTE.2013.2271517 . - · DOI
  4. [4][4] Darbali-Zamora R, Johnson J, Summers A, Jones CB, Hansen C, Showalter C. State estimation-based distributed energy resource optimization for distribution voltage regulation in telemetry-sparse environments using a real-time digital twin. Energies 2021; 14(3). doi: 10.3390/en14030774 . - · DOI
  5. [5][5] Barricelli BR, Casiraghi E, Fogli D. A survey on digital twin: Definitions, characteristics, applications, and design implications. IEEE Access 2019; 7:167653-7. doi: 10.1109/ACCESS.2019.2953499 . - · DOI · Paper page
  6. [7][7] Xiang Y, Cai H, Gu C, Shen X. Cost-benefit analysis of integrated energy system planning considering demand response. Energy 2020; 192:116632. doi: https://doi.org/10.1016/j.energy.2019.116632 . - · DOI
  7. [9][9] Ge S, Xu Z, Liu H, Gu C, Li F. Flexibility evaluation of active distribution networks considering probabilistic characteristics of uncertain variables. IET Generation, Transmission & Distribution 2019; 13(14):314857. doi: 10.1049/iet-gtd.2019.0181 . - · DOI
  8. [10][10] Liu F, Bie Z, Wang X. Day-ahead dispatch of integrated electricity and natural gas system considering reserve scheduling and renewable uncertainties. IEEE Transactions on Sustainable Energy 2018; 10(2):646-58. doi: 10.1109/TSTE.2018.2843121 . - · DOI
  9. [11][11] Salkuti SR. Day-ahead thermal and renewable power generation scheduling considering uncertainty. Renewable energy 2019; 131:95665. doi: 10.1016/j.renene.2018.07.106 . - · DOI
  10. [12][12] Fan C, Wang J, Gang W, Li S. Assessment of deep recurrent neural network-based strategies for short-term building energy predictions. Applied Energy 2019; 236:700-10. doi: 10.1016/j.apenergy.2018.12.004 . - · DOI
  11. [13][13] Theocharides S, Makrides G, Livera A, Theristis M, Kaimakis P, Georghiou GE. Day-ahead photovoltaic power production forecasting methodology based on machine learning and statistical post-processing. Applied Energy 2020; 268:115023. doi: 10.1016/j.apenergy.2020.115023 . - · DOI
  12. [14][14] Zhu C, Tian W, Yin B, Li Z, Shi J. Uncertainty calibration of building energy models by combining approximate bayesian computation and machine learning algorithms. Applied Energy 2020; 268:115025. doi: 10.1016/j.apenergy.2020.115025 . - · DOI
  13. [15][15] Zhang L, Wen J, Li Y, Chen J, Ye Y, Fu Y, et al. A review of machine learning in building load prediction. Applied Energy 2021; 285:116452. doi: 10.1016/j.apenergy.2021.116452 . - · DOI
  14. [16][16] Teo T, Logenthiran T, Woo WL, Abidi K. Near-optimal day-ahead scheduling of energy storage system in grid-connected microgrid. In: Proc. IEEE Innovative Smart Grid Technologies-Asia. Singapore; 2018, p. 1257-61. doi: 10.1109/ISGT-Asia.2018.8467921 . - · DOI
  15. [17][17] Zhou M, Wang B, Watada J. Deep learning-based rolling horizon unit commitment under hybrid uncertainties. Energy 2019; 186:115843. doi: 10.1016/j.energy.2019.07.173 . - · DOI
  16. [18][18] Zhang C, Li R, Shi H, Li F. Deep learning for day-ahead electricity price forecasting. IET Smart Grid 2020; 3(4):462-9. doi: 10.1049/iet-stg.2019.0258 . - · DOI
  17. [19][19] Agostinelli S, Cumo F, Guidi G, Tomazzoli C. Cyber-Physical Systems Improving Building Energy Management: Digital Twin and Artificial Intelligence. Energies 2021; 14(8):2338. doi: 10.3390/en14082338 . - · DOI
  18. [20][20] Edward O'D, Indranil P, Richard C, Sarah B, Nilay S. Integration of an energy management tool and digital twin for coordination and control of multi-vector smart energy systems. Sustainable Cities and Society 2020; 62:102412. doi: 10.1016/j.scs.2020.102412 . - · DOI
  19. [21][21] Sutskever I, Martens J, Dahl G, Hinton G. On the importance of initialization and momentum in deep learning. In: Proceedings of the 30th International Conference on Machine Learning. Atlanta, USA; 2013, p. 1139-47. -
  20. [22][22] Gelaˇ zanskas L, Gamage KA. Forecasting hot water consumption in residential houses. Energies 2015; 8(11):12702-17. doi: 10.3390/en81112336 . - · DOI
  21. [23][23] Balint A, Kazmi H. Determinants of energy flexibility in residential hot water systems. Energy and Buildings 2019; 188:286-96. doi: 10.1016/j.enbuild.2019.02.016 . - · DOI
  22. [25][25] Kingma DP, Ba J. Adam: A method for stochastic optimization. In: Proc. 3rd International Conference on Learning Representations, San Diego, CA, USA. 2015, p. 1-15.

Cited by

Sorted by citations

Authors
You, M., Wang, Q., Sun, H., Castro, I., & Jiang, J.
Affiliations
  • Durham University (GB)
  • Northumbria University (GB)
Corresponding author
Sun, Hongjian
ORCID
Venue
Applied Energy
Publisher
Elsevier BV
Published
2022-01
Volume
305
Pages
117899
ISSN
0306-2619 (ISSN-L)
DOI
10.1016/j.apenergy.2021.117899
OpenAlex
W3201716575
Cited by
180
References
0
Citations by year
2026: 38 · 2025: 48 · 2024: 35 · 2023: 40 · 2022: 18 · 2021: 1
FWCI
6.91
Type
journal article
Language
en
Open access
Licence
Locations
  • Applied Energy (publishedVersion)
  • Northumbria Research Link (Northumbria University) (acceptedVersion)
  • Durham Research Online (Durham University) (acceptedVersion, cc-by-nc-nd)
  • arXiv (Cornell University) (submittedVersion)
  • Durham Research Online (Durham University) (submittedVersion, other-oa)
  • Repository@Nottingham (University of Nottingham) (submittedVersion, cc-by-nc-nd)
  • RePEc: Research Papers in Economics (submittedVersion)
  • Durham Research Online (Durham University) (submittedVersion, cc-by-nc-nd)
  • arXiv (Cornell University)
Funders
Department for Business, Energy and Industrial Strategy, UK Government
Topics
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Integrated Energy Systems Optimization
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid Energy Management
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Electric Power System Optimization
Keywords
digital twin, integrated energy systems, day-ahead scheduling, load uncertainty, renewable energy uncertainty, multi-vector energy systems, deep neural network, operational cost reduction, electric vehicles, thermal energy storage, forecast error, decarbonization

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