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From Transformers to Large Language Models: A systematic review of AI applications in the energy sector towards Agentic Digital Twins

T0 review · 3 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read This review of 99 papers argues that transformers have become the standard for energy forecasting and that embedding large language models throughout digital twins will turn them into Agentic Digital Twins—autonomous, proactive…

desk verdict A useful but methodologically filtered review of transformer/LLM energy applications, with the Agentic Digital Twin explicitly a vision rather than a demonstrated result. read the letter →

arxiv 2506.06359 v1 pith:RK3JEFV5 submitted 2025-06-03 cs.LG cs.AI

Gabriel Antonesi , Tudor Cioara , Ionut Anghel , Vasilis Michalakopoulos , Elissaios Sarmas , Liana Toderean This is my paper
classification cs.LGcs.AI
keywords ArtificialIntelligenceTransformersLargeLanguageModelsGenerativeAIAgenticDigitalTwinsSmartGridEnergyForecastingBuildingModeling
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Drawing on 99 papers published between 2021 and 2025, this systematic review argues that transformer models have become the dominant tool for energy forecasting, consistently outperforming LSTM, GRU, and CNN baselines on demand, thermal load, wind, and solar prediction tasks. It then documents a second, younger wave: large language models are entering energy management not just as forecasters but as integrators of time-series data with text, as generators of synthetic scenarios, and as the engine of multi-agent workflows that automate building-energy modelling and retrofit recommendations. The paper's central, forward-looking claim is that embedding LLMs across every phase of a digital twin will produce 'Agentic Digital Twins'—autonomous, proactive, and communicative models that mirror physical assets, reason about situations, plan energy management actions, and cooperate with other twins. This matters because it would shift AI in smart grids from passive monitoring to active, human-supervised decision support, and it would give grid operators tools for forecasting, balancing, asset onboarding, and workforce training. The authors also identify the obstacles that would have to be overcome for that shift: costly domain fine-tuning, hallucination risk, the need for real-time edge inference, and the absence of standards for LLMs in safety-critical decision loops.

What carries the argument

The load-bearing mechanism is the transformer's self-attention, which computes weighted relationships between all pairs of time steps and lets models capture long-range temporal dependencies; the review shows how domain adaptations—spatial-temporal attention, patch-based decomposition, graph attention, probabilistic decoders, and transfer learning—convert that mechanism into accurate forecasts of electricity demand, thermal load, wind, and solar generation. For the forward-looking half of the paper, the machinery is the fine-tuned LLM inserted into the digital-twin lifecycle: perception, analytics, decision-making, and interaction. The named object that unifies these is the Agentic Digital Twin, an LLM-augmented twin that gains situational awareness, reasoning, proactivity, and social interaction, able to plan and recommend energy actions and negotiate with other twins while remaining subject to human oversight.

What would settle it

Re-run the same Web of Science search without the citation-count thresholds and inspect the papers the original rule excluded: if many low-citation 2024–2025 papers show LLMs already operating in grid-management roles beyond building modelling, the review's 'LLMs are still early' conclusion is contradicted; separately, a live microgrid test in which an LLM-augmented digital twin must produce constraint-respecting, actionable recommendations on demand would test whether the Agentic Digital Twin vision has operational substance.

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Extended reading notes

Core claim

On the paper's own terms, the key discovery is that the energy-AI literature has crossed a threshold. Transformer architectures—above all the Temporal Fusion Transformer, Informer, and patch-based or graph-augmented variants—now define the state of the art in forecasting, while fine-tuned LLMs (GPT-3.5/4, BERT, T5, TimeGPT) and agentic workflows built on them are extending the field beyond prediction into knowledge integration, scenario generation, anomaly detection, and automated simulation. The culminating claim is the Agentic Digital Twin: a next-generation digital twin in which LLMs enhance perception (parsing manuals, reports, and time-series anomalies), analytics (fusing multimodal data with few-shot generalization), decision-making (generating and validating action plans), and interaction (retrieval-augmented knowledge access and cooperation with other twins). In this vision the twin stops being a passive mirror and becomes an active, communicative decision-support agent, with humans remaining in the loop because LLMs cannot enforce actions directly on the grid.

Load-bearing premise

The review's conclusions rest on the assumption that the 99 papers selected through citation-count cutoffs (at least 10 citations for 2021–2022, at least 5 for 2023, at least 3 for 2024, and none for 2025) are representative of the transformer and LLM energy literature; if recent low-citation but significant LLM papers were excluded by those cutoffs, the finding that LLM use is early and concentrated in building modelling could be an artifact of the selection rule rather than the true state of the field.

Editorial extensions

If this is right

  • Transformer-based forecasting will keep displacing recurrent and statistical baselines in grid operations, making architectures like TFT and Informer the reference points for load, wind, and solar prediction.
  • LLM-augmented digital twins will likely mature first in building-energy modelling, where multi-agent workflows already automate simulation-file generation, debugging, and retrofit recommendation.
  • LLMs in grid management will stay advisory rather than directly actuating: hallucination risk and lack of enforcement means humans or dedicated devices execute the recommended actions.
  • Deploying these models at substations or the edge will require compression and distillation to satisfy real-time latency and privacy constraints.
  • Standardization, including validation of LLM outputs against simulation engines, will be a precondition for using them in safety-critical decision loops.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the Agentic Digital Twin vision is right, a likely next bottleneck is multi-twin negotiation: protocols for two LLM-driven twins with conflicting objectives (a building wanting comfort, a grid wanting stability) to reach agreement without a central controller.
  • The citation-cutoff selection rule may have excluded recent low-citation LLM papers, so the 'LLMs are still early' finding should be re-tested on a search without citation thresholds; the field may be further along than the 99-paper sample suggests.
  • The building-energy evidence suggests a concrete benchmark: comparing LLM-generated EnergyPlus models against manually authored ones on a standardized test set would quantify how much autonomy agentic twins can actually take on.
  • A useful refinement of the review's taxonomy would separate 'LLM as forecaster' from 'LLM as orchestrator'—the orchestrator role (agentic workflows, RAG, code generation) is where the promised transformation to Agentic DTs actually lives.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 6 minor

Summary. This manuscript is a PRISMA-guided systematic review of 99 papers (2021-2025, Web of Science, four publishers) applying transformer models and LLMs in the energy sector. It synthesizes transformer-based forecasting of electricity, thermal, wind, solar, and EV loads; reviews LLM applications in building energy modeling, scenario generation, anomaly detection, and decision support; and proposes the Agentic Digital Twin concept, in which LLM integration gives digital twins autonomy, proactivity, and social interaction. The review is a qualitative synthesis rather than a meta-analysis; no fitted equations or code are included. The paper's main empirical conclusions are that transformers dominate forecasting, LLMs extend capabilities beyond prediction, and early multi-agent LLM workflows are emerging; the Agentic DT is presented as the next step.

Significance. The paper has clear value as a structured inventory: the PRISMA flow, keyword combinations, and architecture-level tables (Tables 3-6) make the corpus and model design space transparent, and the authors are candid in Section 6 about hallucination risk, lack of action validation, and the need for human-in-the-loop supervision. If the corpus-level findings hold, the review provides a useful map of where transformer architectures have matured and where LLM work is still nascent. The Agentic DT vision is a plausible research agenda, and the paper's explicit list of open challenges is a constructive contribution. These strengths are real; however, the empirical generalizations and the concluding 'central insight' need to be rebalanced to match the selective corpus and the vision status of the framework.

major comments (3)
  1. [Section 3, Table 1 and Section 7] In Table 1, the citation-count cutoffs (>=10 for 2021-2022, >=5 for 2023, >=3 for 2024, none for 2025) appear under 'Exclusion Criteria' although the text applies them as minimum inclusion thresholds. Regardless of labeling, these cutoffs are arbitrary and post hoc, and they systematically remove recent, low-cited papers from the corpus. Because Section 7 states as finding (a) that 'Transformer models now dominate energy forecasting tasks,' the review should either rerun the PRISMA selection without the citation filter, provide a sensitivity analysis across thresholds, or explicitly restrict the claim to the 99 selected papers. Without this, the dominance claim may be an artifact of the selection rule.
  2. [Table 2, LLM rows, and Section 5] The included corpus is not reconciled with the narrative. Reference [103], a 2021 Prophet-based load forecasting paper, is listed under 'Large Language Model energy prediction' but is neither an LLM nor a transformer study and is never discussed; several other LLM-row entries ([104], [110]-[112]) are also absent from the Section 5 synthesis. This undermines the reproducibility of the screening step and inflates the LLM application counts. The authors should either discuss each included item or document and justify its removal at the synthesis stage.
  3. [Sections 6-7] The paper's central claim about Agentic DTs is presented as a conclusion derived from the review, but the reviewed systems are offline, human-in-the-loop assistants (BEM, retrofit recommendation, forecasting, intrusion detection). None of the 99 studies demonstrates an LLM-driven digital twin executing real-time grid actions or multi-twin negotiation. Moreover, Section 6 itself states that 'LLMs cannot execute or enforce actions directly in the smart grid' and 'humans will remain a key in the decision-making loop,' which directly undercuts the attributes 'autonomous' and 'proactive' attributed to Agentic DTs in Section 7. The conclusion should separate the empirical synthesis (LLMs are at an early, human-supervised stage, concentrated in building energy modeling) from the normative vision (Agentic DT as a research direction with the listed open challenges).
minor comments (6)
  1. [Sections 3 and Figure 3] 'PRISMA 2000' should be 'PRISMA 2020' in both places; the text cites the 2020 statement, so the label is a typo.
  2. [Table 1] The citation-count thresholds should be moved to the inclusion criteria or explicitly labeled as minimum-citation filters; the current placement under 'Exclusion Criteria' makes the procedure ambiguous.
  3. [Section 4.1, Table 3] The row for [73] describes an 'LSTM-based Transformer,' while the text in Section 4.1 describes [73] as a 'deep Transformer seq2seq model'; align the terminology.
  4. [References] Reference formatting is inconsistent: several entries use 'htps://' (missing 's') and some lack complete DOIs (e.g., references [19], [22], [26]); please normalize.
  5. [Figure 7] The text says 'more than 20 authors from Europe, more than 10 from the US' and 'dominated by authors from Asia with more than 70 authors,' but the figure appears to count papers or affiliated authors; clarify the unit of analysis.
  6. [Section 5, Table 7] The distinction between 'Agentic' and 'Non-Agentic' rows is not defined in the text; add a sentence describing the criterion.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the review's empirical synthesis and the Agentic DT proposal are independent of the paper's inputs; the few self-citations are not load-bearing.

full rationale

This is a systematic literature review, not a derivation with fitted parameters or equations. The review's empirical claims—that transformer models dominate energy forecasting and that LLM use is emerging, mainly in building energy modeling and decision support—are summaries of the 99 papers selected through the PRISMA procedure described in Section 3. There is no equation or fitted quantity in the paper, so there is no step where a prediction reduces by construction to its input. The Agentic Digital Twin concept is explicitly introduced as the authors' vision and proposal in Sections 1, 6, and 7, supported by external surveys on agentic LLMs and digital twins rather than by the paper's own results. The paper itself flags the speculative nature of autonomous action by noting that 'LLMs cannot execute or enforce actions directly in the smart grid' and that 'humans will remain a key in the decision-making loop,' which are limitation statements rather than circularity. The only self-citations are reference [14], the authors' hybrid transformer paper, cited as one example among many transformer forecasting works, and reference [126], the authors' edge offloading paper, cited for a generic point about edge-fog-cloud deployment; neither is load-bearing for the review's conclusions. No load-bearing argument reduces to a self-citation, and no known result is merely renamed as a new contribution. Therefore, no significant circularity is present.

Assumptions & free parameters 1 free parameters · 4 assumptions · 1 invented entities

This review introduces no fitted equations, so the main hand-chosen quantities are the screening parameters and the main postulated entity is the Agentic Digital Twin. The review's conclusions are sensitive to these choices.

free parameters (1)
  • Citation count cutoffs by publication year = 2021-2022: >=10; 2023: >=5; 2024: >=3; 2025: none
    Hand-chosen screening thresholds in Table 1 that determine the final 99-paper corpus and therefore shape the review's conclusions.
assumptions (4)
  • ad hoc to paper Citation count is a valid proxy for paper relevance and quality in this domain
    Invoked by the exclusion criteria in Table 1 (Section 3); no evidence is provided that citation count correlates with the review's research questions.
  • domain assumption Searching only Web of Science with Topic fields captures the relevant transformer/LLM energy literature
    Section 3 states WoS was the primary database; many relevant LLM contributions appear as arXiv preprints (e.g., refs [17], [125], [127], [131]), which WoS may not index consistently.
  • ad hoc to paper The 99 included papers are representative of the field
    The final corpus is the output of citation and publisher filters; the review uses it to make field-wide claims such as 'transformers now dominate' in Section 7.
  • domain assumption Reported performance gains in the included papers are accurate and comparable
    The review aggregates error reductions from heterogeneous datasets and baselines without re-running experiments.
invented entities (1)
  • Agentic Digital Twin
    purpose: Proposed next-generation digital twin integrating LLMs to add autonomy, proactivity, and social interaction to energy asset management (Section 6, Figure 8).
    The paper introduces this as a vision and lists open challenges, but provides no implementation, quantitative evaluation, or falsifiable prediction, so no independent evidence exists outside the paper.

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Cite this review

Pith. "Pith review of From Transformers to Large Language Models: A systematic review of AI applications in the energy sector towards Agentic Digital Twins." pith.science (2026). https://pith.science/paper/RK3JEFV5

@misc{pith2026250606359,
  author       = {Pith},
  title        = {Pith review of: From Transformers to Large Language Models: A systematic review of AI applications in the energy sector towards Agentic Digital Twins},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/RK3JEFV5}},
  note         = {Machine review of arXiv:2506.06359}
}
read the original abstract

Artificial intelligence (AI) has long promised to improve energy management in smart grids by enhancing situational awareness and supporting more effective decision-making. While traditional machine learning has demonstrated notable results in forecasting and optimization, it often struggles with generalization, situational awareness, and heterogeneous data integration. Recent advances in foundation models such as Transformer architecture and Large Language Models (LLMs) have demonstrated improved capabilities in modelling complex temporal and contextual relationships, as well as in multi-modal data fusion which is essential for most AI applications in the energy sector. In this review we synthesize the rapid expanding field of AI applications in the energy domain focusing on Transformers and LLMs. We examine the architectural foundations, domain-specific adaptations and practical implementations of transformer models across various forecasting and grid management tasks. We then explore the emerging role of LLMs in the field: adaptation and fine tuning for the energy sector, the type of tasks they are suited for, and the new challenges they introduce. Along the way, we highlight practical implementations, innovations, and areas where the research frontier is rapidly expanding. These recent developments reviewed underscore a broader trend: Generative AI (GenAI) is beginning to augment decision-making not only in high-level planning but also in day-to-day operations, from forecasting and grid balancing to workforce training and asset onboarding. Building on these developments, we introduce the concept of the Agentic Digital Twin, a next-generation model that integrates LLMs to bring autonomy, proactivity, and social interaction into digital twin-based energy management systems.

Figures

Figures reproduced from arXiv: 2506.06359 by the authors.

Figure 2
Figure 2. Finetuning process for LLM adapta�on to smart grid management tasks More specifically, according to the literature reviewed in this paper, LLMs have been applied for tasks related to industrial carbon emissions predic�on, electrical safety incident detec�on, wind power and load forecas�ng and grid management by leveraging their patern recogni�on, reasoning capabili�es and genera�ng decision-relevant informa�on over … view at source ↗

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Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. A Multimodal Dataset for Large Language Model Applications in the Energy Domain

    eess.SY 2026-07 accept novelty 6.5 of 10

    mAIEnergy is a harmonized, FAIR multimodal energy dataset (text, imagery, numerical series, geospatial graphs) purpose-built for LLM pre-training and retrieval-augmented generation.

  2. SoK: How Frontier AI Reshapes System-Level Security Risk Dynamics in Critical Infrastructure

    cs.CR 2026-08 conditional novelty 5.0 of 10

    A new five-dimension framework describes how frontier AI reshapes critical-infrastructure security through capability, infiltration, propagation, control loss, and response limits.

Reference graph

Works this paper leans on

135 extracted references · 64 canonical work pages · cited by 2 Pith papers

  1. [103]

    Parizad, A.; Hatziadoniu, C. J., Using Prophet Algorithm for Patern Recogni�on and Short Term Forecas�ng of Load Demand Based on Seasonality and Exogenous Features , 2020 52ND NORTH AMERICAN POWER SYMPOSIUM (NAPS), 2021

    work page 2020

  2. [104]

    Shahin, Ma�n; Simjoo, Mohammad, Poten�al applica�ons of innova�ve AI-based tools in hydrogen energy development: Leveraging large language model technologies, INTERNATIONAL JOURNAL OF HYDROGEN ENERGY, 2025

    work page 2025

  3. [110]

    Zhao, Tianqiao; Yogarathnam, Amirthagunaraj; Yue, Meng, A Large Language Model for Determining Par�al Tripping of Distributed Energy Resources, IEEE TRANSACTIONS ON SMART GRID, 2025

    work page 2025

  4. [112]

    Liu, Qiang; Mu, Junsheng; Chen, Da; Zhang, Ronghui; Liu, Yijian; Hong, Tao , LLM Enhanced Reconfigurable Intelligent Surface for Energy-Efficient and Reliable 6G IoV, IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2025

    work page 2025

  5. [1]

    P., & Nandi, C

    Baidya, S., Potdar, V., Ray, P. P., & Nandi, C. (2021). Reviewing the opportunities, challenges, and future directions for the digitalization of energy. Energy Research & Social Science, 81, 102243

    2021

  6. [2]

    , Z h e n g , X

    X i e , L . , Z h e n g , X . , S u n , Y . , H u a n g , T . , & B r u t o n , T . ( 2 0 2 2 ) . Massively digitized power grid: Opportunities and challenges of use-inspired AI. Proceedings of the IEEE, 111(7), 762-787

  7. [3]

    Monaco, R., Bergaentzlé, C., Vilaplana, J. A. L., Ackom, E., & Nielsen, P. S. (2024). Digitalization of power distribution grids: Barrier analysis, ranking and policy recommendations. Energy Policy, 188, 114083

    2024

  8. [4]

    P., Yan, H., Gusev, A., Zhang, L., He, Y., & Yang, S

    Filonchyk, M., Peterson, M. P., Yan, H., Gusev, A., Zhang, L., He, Y., & Yang, S. (2024). Greenhouse gas emissions and reduction strategies for the world's largest greenhouse gas emitters. Science of The Total Environment, 944, 173895

    2024

Show all 135 references
  1. [5]

    J., Fischedick, M., Lechtenböhmer, S.,

    Bataille, C., Åhman, M., Neuhoff, K., Nilsson, L. J., Fischedick, M., Lechtenböhmer, S., ... & Rahbar, S. (2018). A review of technology and policy deep decarbonization pathway options for making energy- intensive industry production consistent with the Paris Agreement. Journa...

    2018

  2. [6]

    S., Nathwani, J., & Wen, J

    Li, J., Herdem, M. S., Nathwani, J., & Wen, J. Z. (2023). Methods and applica�ons for Ar�ficial Intelligence, Big Data, Internet of Things, and Blockchain in smart energy management. Energy and AI, 11, 100208

    2023

  3. [7]

    Ar�ficial Intelligence for Energy Systems: Driving Intelligent, Flexible and Op�mal Energy Management

    Sarmas, E. Ar�ficial Intelligence for Energy Systems: Driving Intelligent, Flexible and Op�mal Energy Management. Springer Nature

  4. [8]

    Entezari, A., Aslani, A., Zahedi, R., & Noorollahi, Y. (2023). Artificial intelligence and machine learning in energy systems: A bibliographic perspective. Energy Strategy Reviews, 45, 101017

    2023

  5. [9]

    Liu, Z., Sun, Y., Xing, C., Liu, J., He, Y., Zhou, Y., & Zhang, G. (2022). Artificial intelligence powered large -scale renewable integrations in multi -energy systems for carbon neutrality transition: Challenges and future perspectives. Energy and AI, 10, 100195

    2022

  6. [10]

    , X i a o , A

    H a n , K . , X i a o , A . , W u , E . , G uo , J . , X u , C . , & W a n g , Y . ( 2 0 2 1 ) . T r a n s f o r m e r i n t r a n s f o r m e r . A d v a n c e s i n ne u r a l informa�on processing systems, 34, 15908-15919

  7. [11]

    Nascimento, E. G. S., de Melo, T . A., & Moreira, D. M. (2023). A transformer-based deep neural network with wavelet transform for forecas�ng wind speed and wind energy. Energy, 278, 127678

    2023

  8. [12]

    Xu, C., & Chen, G. (2024). Interpretable transformer -based model for probabilistic short -term forecasting of residential net load. International Journal of Electrical Power & Energy Systems, 155, 109515

    2024

  9. [13]

    Liu, J., & Fu, Y. (2023). Renewable energy forecasting: A self -supervised learning -based transformer variant. Energy, 284, 128730

    2023

  10. [14]

    Antonesi, G., Cioara, T., Anghel, I., Papias, I., Michalakopoulos, V., & Sarmas, E. (2025). Hybrid transformer model with liquid neural networks and learnable encodings for buildings’ energy forecasting. Energy and AI, 20, 100489

    2025

  11. [15]

    Achiam, J., Adler , S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F . L., ... & McGrew , B. (2023). Gpt-4 technical report. arXiv preprint arXiv:2303.08774

    2023 arXiv

  12. [16]

    & Fiedel, N

    Chowdhery , A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., ... & Fiedel, N. (2023). Palm: Scaling language modeling with pathways. Journal of Machine Learning Research, 24(240), 1-113

    2023

  13. [17]

    Garza, A., Challu, C., & Mergenthaler-Canseco, M. (2023). TimeGPT-1. arXiv preprint arXiv:2310.03589

    2023 arXiv

  14. [18]

    , Z h a n g , S

    Y i , H . , Z h a n g , S . , A n , D . , & L i u , Z . ( 2 0 2 4 ) . P a t c h e s N e t : P a t c h T S T-based mul� -scale network security situa�on predic�on. Knowledge-Based Systems, 299, 112037

  15. [19]

    Siyuan Huang, Yepeng Liu, Haoyi Cui, Fan Zhang, Jinjiang Li, Xiaofeng Zhang, Mingli Zhang, Caiming Zhang, MEAformer: An all- MLP transformer with temporal external aten�on for long -term �me series forecas�ng, Informa�on Sciences, Volume 669, 2024, 120605, ISSN 0020-0255, htps...

    2024

  16. [20]

    Zheng Wang, Haowei Ran, Jinchang Ren, Meijun Sun, PWDformer: Deformable transformer for long-term series forecas�ng, Patern Recogni�on, Volume 147, 2024, 110118, ISSN 0031 -3203, htps://doi.org/10.1016/j.patcog.2023.110118

    2024

  17. [21]

    Jun Wei Chan, Chai Kiat Yeo, A Transformer based approach to electricity load forecas�ng, The Electricity Journal, Volume 37, Issue 2, 2024, 107370, ISSN 1040-6190, htps://doi.org/10.1016/j.tej.2024.107370

    2024

  18. [22]

    Pengfei Zhao, Weihao Hu, Di Cao, Zhenyuan Zhang, Wenlong Liao, Zhe Chen, Qi Huang, Enhancing mul�variate, mul�-step residen�al load forecas�ng with spa�otemporal graph aten�on -enabled transformer, Interna�onal Journal of Electrical Power & Energy S ystems, Volume 160, 2024, 1...

    2024

  19. [23]

    Faisal Saeed, Abdul Rehman, Hasnain Ali Shah, Muhammad Diyan, Jie Chen, Jae-Mo Kang, SmartFormer: Graph- based transformer model for energy load forecas�ng, Sustainable Energy Technologies and Assessments, Volume 73, 2025, 104133, ISSN 2213-1388, htps://doi.org/10.1016/j.seta....

    2025

  20. [24]

    Miguel López Santos, Saúl Díaz García, Xela García- San�ago, Ana Ogando- Mar�nez, Fernando Echevarría Camarero, Gonzalo Blázquez Gil, Pablo Carrasco Ortega, Deep learning and transfer learning techniques applied to short-term load forecas�ng of data-poor buildings in local ene...

    2023

  21. [25]

    Wei Zhang, Hongyi Zhan, Hang Sun, Mao Yang, Probabilis�c load forecas�ng for integrated energy systems based on quan�le regression patch �me series Transformer, Energy Reports, Volume 13, 2025, Pages 303 -317, ISSN 2352-4847, htps://doi.org/10.1016/j.egyr.2024.11.057

    2025 doi

  22. [26]

    Jingfei Wang, Danya Xu, Yuanzheng Li, Mohammad Shahidehpour, Tao Yang, Transformer -based probabilis�c demand forecas�ng with adap�ve online learning, Electric Power Systems Research, Volume 240, 2025, 111255, ISSN 0378-7796, htps://doi.org/10.1016/j.epsr.2024.111255

    2025

  23. [27]

    Junlong Tong, Liping Xie, Wankou Yang, Kanjian Zhang, Junsheng Zhao, Enhancing �me series forecas�ng: A hierarchical transformer with probabilis�c decomposi�on representa�on, Informa�on Sciences, Volume 647, 2023, 119410, ISSN 0020-0255, htps://doi.org/10.1016/j.ins.2023.119410

    2023

  24. [28]

    Tangjie Wu, Qiang Ling, STELLM: Spa�o -temporal enhanced pre -trained large language model for wind speed forecas�ng, Applied Energy, Volume 375, 2024, 124034, ISSN 0306- 2619, htps://doi.org/10.1016/j.apenergy.2024.124034

    2024

  25. [29]

    Zefeng Lai, Tangjie Wu, Xihong Fei, Qiang Ling, BERT4ST:: Fine-tuning pre-trained large language model for wind power forecas�ng, Energy Conversion and Management, Volume 307, 2024, 118331, ISSN 0196 -8904, htps://doi.org/10.1016/j.enconman.2024.118331

    2024

  26. [30]

    Cooee: An Ar�ficial Intelligence Chatbot for Complex Energy Environments,

    G. Gamage, N. Mills, P . Rathnayaka, A. Jennings and D. Alahakoon, "Cooee: An Ar�ficial Intelligence Chatbot for Complex Energy Environments," 2022 15th Interna�onal Conference on Human System Interac�on (HSI), Melbourne, Australia, 2022, pp. 1-5, doi: 10.1109/HSI55341.2022.9869464

    2022

  27. [31]

    ChatGPT and Other Large Language Models for Cybersecurity of Smart Grid Applica�ons,

    A. Zaboli, S. L. Choi, T. -J. Song and J. Hong, "ChatGPT and Other Large Language Models for Cybersecurity of Smart Grid Applica�ons," 2024 IEEE Power & Energy Society General Mee�ng (PESGM), Seatle, WA, USA, 2024, pp. 1-5, doi: 10.1109/PESGM51994.2024.10688863

    2024

  28. [32]

    Chaobo Zhang, Jian Zhang, Yang Zhao, Jie Lu, Automated data -driven building energy load predic�on method based on genera�ve pre -trained transformers (GPT), Energy, Volume 318, 2025, 134824, ISSN 0360 -5442, htps://doi.org/10.1016/j.energy.2025.134824

    2025

  29. [33]

    Tong Xiao, Peng Xu, Exploring automated energy op�miza�on with unstructured building data: A mul� -agent based framework leveraging large language models, Energy and Buildings, Volume 322, 2024, 114691, ISSN 0378-7788, htps://doi.org/10.1016/j.enbuild.2024.114691

    2024

  30. [34]

    Page, M.J., McKenzie, J.E., Bossuyt, P .M. et al. The PRISMA 2020 statement: an updated guideline for repor�ng systema�c reviews. Syst Rev 10, 89 (2021). htps://doi.org/10.1186/s13643-021-01626-4

    2021 doi

  31. [35]

    PRISMA 2020 explana�on and elabora�on: updated guidance and exemplars for repor�ng systema�c reviews BMJ 2021; 372 :n160 doi:10.1136/bmj.n160

    P age M J, Moher D , Bossuyt P M, Boutron I, Hoffmann T C, Mulrow C D et al. PRISMA 2020 explana�on and elabora�on: updated guidance and exemplars for repor�ng systema�c reviews BMJ 2021; 372 :n160 doi:10.1136/bmj.n160

    2020 doi

  32. [36]

    & Yan, E

    Li, K., Rollins, J. & Yan, E. Web of Science use in published research and review papers 1997 –2017: a selec�ve, dynamic, cross -domain, content -based analysis. Scientometrics 115, 1– 20 (2018). htps://doi.org/10.1007/s11192-017-2622-5

    2018 doi

  33. [37]

    Spa�al and Temporal Aten�on-Enabled Transformer Network for Mul�variate Short-Term Residen�al Load Forecas�ng,

    H. Zhao, Y. Wu, L. Ma and S. Pan, "Spa�al and Temporal Aten�on-Enabled Transformer Network for Mul�variate Short-Term Residen�al Load Forecas�ng," in IEEE Transac�ons on Instrumenta�on and Measurement, vol. 72, pp. 1-11, 2023, Art no. 2524611, doi: 10.1109/TIM.2023.3305655

    2023

  34. [38]

    Wang, Yun; Xu, Houhua; Song, Mengmeng; Zhang, Fan; Li, Yifen; Zhou, Shengchao; Zhang, Lingjun , A convolu�onal Transformer-based truncated Gaussian density network with data denoising for wind speed forecas�ng, APPLIED ENERGY, Volume 333, 1 March 2023, 120601

    2023

  35. [39]

    Zhang, Hanjin; Li, Bin; Su, Shun- Feng; Yang, Wankou; Xie, Liping, A Novel Hybrid Transformer-Based Framework for Solar Irradiance Forecas�ng Under Incomplete Data Scenarios , IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2024

    2024

  36. [40]

    Li, Long; Su, Xingyu ; Bi, Xian�ng; Lu, Yueliang; Sun, Xuetao , A novel Transformer -based network forecas�ng method for building cooling loads, ENERGY AND BUILDINGS, 2023

    2023

  37. [41]

    Dong, Changyin; Xiong, Zhuozhi; Zhang, Chu; Li, Ni; Li, Ye; Xie, Ning; Zhang, Jiarui; Wang, Hao , A transformer- based approach for deep feature extrac�on and energy consump�on predic�on of electric buses based on driving distances, APPLIED ENERGY, 2025

    2025

  38. [42]

    Zhang, Liwenbo; Wilson, Robin; Sumner, Mark; Wu, Yupeng, Advanced mul�modal fusion method for very short- term solar irradiance forecas�ng using sky images and meteorological data: A gate and transformer mechanism approach, RENEWABLE ENERGY, 2023

    2023

  39. [43]

    Huang, Siyuan; Liu, Yepeng; Zhang, Fan; Li, Yue; Li, Jinjiang; Zhang, Caiming , CrossWaveNet: A dual -channel network with deep cross -decomposi�on for Long -term Time Series Forecas�ng , EXPERT SYSTEMS WITH APPLICATIONS, 2024

    2024

  40. [44]

    Zhou, Chengjie; Che, Chao; Wang, Pengfei; Zhang, Qiang , Diformer: A dynamic self -differen�al transformer for new energy power autoregressive predic�on, KNOWLEDGE-BASED SYSTEMS, 2023

    2023

  41. [45]

    Rong, Jing; Wang, Cong; Zhou, Qiuzhan; He, Yunxue; Wu, Huinan, Enhancing non- intrusive load monitoring through transfer learning with transformer models, ENERGY AND BUILDINGS, 2025

    2025

  42. [46]

    Huang, Siyuan; Liu, Yepeng , FL-Net: A mul� -scale cross -decomposi�on network with frequency external aten�on for long-term �me series forecas�ng, KNOWLEDGE-BASED SYSTEMS, 2024

    2024

  43. [47]

    Wang, Chao-fan; Liu, Kui-xing; Peng, Jieyang; Li, Xiang; Li, Xiu -feng; Zhang, Jia-wan; Niu, Zhi-bin, High-precision energy consump�on forecas�ng for large office building using a signal decomposi�on -based deep learning approach, ENERGY, 2025

    2025

  44. [48]

    Su, Jing; Xie, Dirui; Duan, Yuanzhi; Zhou, Yue; Hu, Xiaofang; Duan, Shukai , MDCNet: Long -term �me series forecas�ng with mode decomposi�on and 2D convolu�on, KNOWLEDGE-BASED SYSTEMS, 2024

    2024

  45. [49]

    Yuan, Liang; Wang, Xiang�ng; Sun, Yao; Liu, Xubin; Dong, Zhao Yang , Mul�step photovoltaic power forecas�ng based on mul� -�mescale fluctua�on aggrega�on aten�on mechanism and contras�ve learning , INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2025

    2025

  46. [50]

    Han, Yongming; Han, Longkun; Shi, Xinwei; Li, Jun; Huang, Xiaoyi; Hu, Xuan; Chu, Chong; Geng, Zhiqiang , Novel CNN-based transformer integra�ng Boruta algorithm for produc�on predic�on modeling and energy saving of industrial processes, EXPERT SYSTEMS WITH APPLICATIONS, 2024

    2024

  47. [51]

    Mo, Site; Wang, Haoxin; Li, Bixiong; Xue, Zhe; Fan, Songhai; Liu, Xianggen , Powerformer: A temporal -based transformer model for wind power forecas�ng, ENERGY REPORTS, 2024

    2024

  48. [52]

    Wu, Qiang; Zheng, Hongling; Guo, Xiaozhu; Liu, Guangqiang , Promo�ng wind energy for sustainable development by precise wind speed predic�on based on graph neural networks, RENEWABLE ENERGY, 2022

    2022

  49. [53]

    Yu, Yang; Ma, Ruizhe; Ma, Zongmin, Robformer: A robust decomposi�on transformer for long-term �me series forecas�ng, PATTERN RECOGNITION, 2024

    2024

  50. [54]

    Zhang, Qingyong; Zhou, Shiyang; Xu, Bingrong; Li, Xinran, TCAMS-Trans: Efficient temporal-channel aten�on mul�-scale transformer for net load forecas�ng, COMPUTERS & ELECTRICAL ENGINEERING, 2024

    2024

  51. [55]

    Yu, Die; Liu, Tong; Wang, Kai; Li, Kang; Mercangoz, Mehmet; Zhao, Jian; Lei, Yu; Zhao, Ruofan, Transformer based day-ahead cooling load forecas�ng of hub airport air-condi�oning systems with thermal energy storage, ENERGY AND BUILDINGS, 2024

    2024

  52. [56]

    Yuan, Jun; Chen, Si -Zhe; Yu, Samson S.; Zhang, Guidong; Chen, Zhe; Zhang, Yun, A Kernel -Based Real -Time Adap�ve Dynamic Programming Method for Economic Household Energy Systems , IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2023

    2023

  53. [57]

    Wang, Chen; Wang, Ying; Ding, Zhetong; Zheng, Tao; Hu, Jiangyi; Zhang, Kaifeng , A Transformer-Based Method of Mul�energy Load Forecas�ng in Integrated Energy System, IEEE TRANSACTIONS ON SMART GRID, 2022

    2022

  54. [58]

    Gao, Jiaxin; Chen, Yun�an; Hu, Wenbo; Zhang, Dongxiao, An adap�ve deep-learning load forecas�ng framework by integra�ng transformer and domain knowledge, ADVANCES IN APPLIED ENERGY, 2023

    2023

  55. [59]

    Yan, Qin; Lu, Zhiying; Liu, Hong; He, Xingtang; Zhang, Xihai; Guo, Jianlin , An improved feature-�me Transformer encoder-Bi-LSTM for short-term forecas�ng of user-level integrated energy loads, ENERGY AND BUILDINGS, 2023

    2023

  56. [60]

    Ozen, Serkan; Yazici, Adnan; Atalay, Volkan, Hybrid deep learning models with data fusion approach for electricity load forecas�ng, EXPERT SYSTEMS, 2025

    2025

  57. [61]

    Cui, Yunlong; Li, Zhao; Wang, Yusong; Dong, Danhuang; Gu, Chenlin; Lou, Xiaowei; Zhang, Peng, Informer model with season -aware block for efficient long -term power �me series forecas�ng , COMPUTERS & ELECTRICAL ENGINEERING, 2024

    2024

  58. [62]

    Cen, Senfeng; Lim, Chang Gyoon, Mul�-Task Learning of the PatchTCN -TST Model for Short -Term Mul�-Load Energy Forecas�ng Considering Indoor Environments in a Smart Building, IEEE ACCESS, 2024

    2024

  59. [63]

    Ferreira, Jonatas B

    Andréia B.A. Ferreira, Jonatas B. Leite, Denis H.P. Salvadeo, Power substa�on load forecas�ng using interpretable transformer-based temporal fusion neural networks, Electric Power Systems Research, Volume 238, 2025, 111169, ISSN 0378-7796, htps://doi.org/10.1016/j.epsr.2024.111169

    2025

  60. [64]

    Pham Canh Huy; Nguyen Quoc Minh; Nguyen Dang Tien; Tao Thi Quynh Anh, Short-Term Electricity Load Forecas�ng Based on Temporal Fusion Transformer Model, IEEE ACCESS, 2022

    2022

  61. [65]

    Giacomazzi, Elena; Haag, Felix; Hopf, Konstan�n , Short-Term Electricity Load Forecas�ng Using the Temporal Fusion Transformer: Effect of Grid Hierarchies and Data Sources , PROCEEDINGS OF THE 2023 THE 14TH ACM INTERNATIONAL CONFERENCE ON FUTURE ENERGY SYSTEMS, E-ENERGY 2023, 2023

    2023

  62. [66]

    Alorf, Abdulaziz; Khan, Muhammad Usman Ghani , Solar Irradiance Forecas�ng Using Temporal Fusion Transformers, INTERNATIONAL JOURNAL OF ENERGY RESEARCH, 2025

    2025

  63. [67]

    Du, Pei; Ye, Yuxin; Wu, Han; Wang, Jianzhou, Study on determinis�c and interval forecas�ng of electricity load based on mul� -objec�ve whale op�miza�on algorithm and transformer model , EXPERT SYSTEMS WITH APPLICATIONS, 2025

    2025

  64. [68]

    Semmelmann, Leo; Hertel, Mathias; Kircher, Kevin J.; Mikut, Ralf; Hagenmeyer, Veit; Weinhardt, Christof , The impact of heat pumps on day-ahead energy community load forecas�ng, APPLIED ENERGY, 2024

    2024

  65. [69]

    Zhang, Qingyong; Chen, Jiahua; Xiao, Gang; He, Shangyang; Deng, Kunxiang , TransformGraph: A novel short - term electricity net load forecas�ng model, ENERGY REPORTS, 2023

    2023

  66. [70]

    Lin, Zijie; Xie, Linbo; Zhang, Siyuan, A compound framework for short-term gas load forecas�ng combining �me- enhanced percep�on transformer and two-stage feature extrac�on, ENERGY, 2024

    2024

  67. [71]

    Iqbal, Muhammad Sajid; Adnan, Muhammad, Edge compu�ng and transfer learning -based short -term load forecas�ng for residen�al and commercial buildings, ENERGY AND BUILDINGS, 2025

    2025

  68. [72]

    Zheng, Peijun; Zhou, Heng; Liu, Jiang; Nakanishi, Yosuke, Interpretable building energy consump�on forecas�ng using spectral clustering algorithm and temporal fusion transformers architecture, APPLIED ENERGY, 2023

    2023

  69. [73]

    Dong, Hanjiang; Zhu, Jizhong; Li, Shenglin; Wu, Wanli; Zhu, Haohao; Fan, Junwei , Short-term residen�al household reac�ve power forecas�ng considering ac�ve power demand via deep Transformer sequence -to- sequence networks, APPLIED ENERGY, 2023

    2023

  70. [74]

    Liu, Jingxuan; Zang, Haixiang; Cheng, Lilin; Ding, Tao; Wei, Zhinong; Sun, Guoqiang , A Transformer-based mul�modal-learning framework using sky images for ultra -short-term solar irradiance forecas�ng , APPLIED ENERGY, 2023

    2023

  71. [75]

    Faiz, Muhammad Faizan; Sajid, Muhammad; Ali, Sara; Javed, Kashif; Ayaz, Yasar, Energy modeling and predic�ve control of environmental quality for building energy management using machine learning , ENERGY FOR SUSTAINABLE DEVELOPMENT, 2023

    2023

  72. [76]

    Gao, Shuhua; Liu, Yuanbin; Jing, Wang; Wang, Zhengfang; Xu, Wenjun; Yue, Renfeng; Cui, Ruipeng; Yong, Liu; Fan, Xuezhong, Short-term residen�al load forecas�ng via transfer learning and mul� -aten�on fusion for EVs' coordinated charging, INTERNATIONAL JOURNAL OF ELECTRICAL POW...

    2025

  73. [77]

    Tiwari, Shamik; Jain, Anurag; Ahmed, Nada Mohamed Osman Sid; Charu; Alkwai, Lulwah M.; Da�alla, Alaa Kamal Yousif; Hamad, Sawsan Ali Saad Machine learning -based model for predic�on of power consump�on in smart grid- smart way towards smart city

  74. [78]

    Abdel-Basset, Mohamed; Hawash, Hossam; Chakraborty, Ripon K.; Ryan, Michael Energy-Net: A Deep Learning Approach for Smart Energy Management in IoT-Based Smart Ci�es

  75. [79]

    Nazir, Amril; Shaikh, Abdul Khalique; Shah, Abdul Salam; Khalil, Ashraf, Forecas�ng energy consump�on demand of customers in smart grid using Temporal Fusion Transformer (TFT)

  76. [80]

    Lee, Jooseung; Kang, Jimyung; Lee, Soonwoo; Oh, Hui -Myoung, Ultra-Short Term Photovoltaic genera�on Forecas�ng Based on Data Decomposi�on and Customized Hybrid Model Architecture

  77. [81]

    Ma, Qing; Zhang, Deyuan; Wu, Hao; Zhang, Yusen; Yang, Qingrong; Yang, Ning, Shape-Aware Nonintrusive Load Disaggrega�on via Prior-Guided State-Reused Mul�branch Cross-Transformer

  78. [82]

    Bao, Gengyi; Liu, Xinhua; Zou, Bosong; Yang, Kaiyi; Zhao, Junwei; Zhang, Lisheng; Chen, Muyang; Qiao, Yuan�ng; Wang, Wentao; Tan, Rui; Wang, Xiangwen , Collabora�ve framework of Transformer and LSTM for enhanced state-of-charge es�ma�on in lithium-ion bateries

  79. [83]

    Zhang, Zongbin; Huang, Xiaoqiao; Li, Chengli; Cheng, Feiyan; Tai, Yonghang , CRAformer: A cross -residual aten�on transformer for solar irradia�on mul�step forecas�ng

  80. [84]

    Yin, Linfei; Zheng, Da , Hybrid modeling with data enhanced driven learning algorithm for smart genera�on control in mul�-area integrated energy systems with high propor�on renewable energy

  81. [85]

    Shan, Zihan; Si, Gangquan; Qu, Kai; Wang, Qianyue; Kong, Xiangguang; Tang, Yu; Yang, ChenMul�scale Self - Aten�on Architecture in Temporal Neural Network for Nonintrusive Load Monitoring

  82. [86]

    Bashir, Tasarruf; Wang, Huifang; Tahir, Mustafa; Zhang, Yixiang , Wind and solar power forecas�ng based on hybrid CNN-ABiLSTM, CNN-transformer-MLP models

  83. [87]

    Song, Dongran; Rehman, Muhammad Shams Ur; Deng, Xiaofei; Xiao, Zhao; Noor, Javeria; Yang, Jian; Dong, Mi , Accurate solar power predic�on with advanced hybrid deep learning approach

  84. [88]

    Liang, Xuefeng; Hu, Ze�an; Zhang, Jun; Chen, Han; Gu, Qingshui; You, Xiaochuan, Developing a robust wind power forecas�ng method: Integra�ng data repair, feature screening, and economic impact analysis for prac�cal applica�ons

  85. [89]

    , Short-term solar irradiance forecas�ng in streaming with deep learning

    Lara-Benitez, Pedro; Carranza -Garcia, Manuel; Luna -Romera, Jose Maria; Riquelme, Jose C. , Short-term solar irradiance forecas�ng in streaming with deep learning

  86. [90]

    Wang, Sicheng; Huang, Yan, Spa�o-temporal photovoltaic predic�on via a convolu�onal based hybrid network

  87. [91]

    Cao, Yizhi; Liao, Yilin; Liu, Zhaoran; Ma, Xiang; Liu, Xinggao , SWAformer: A novel shi�ed window aten�on Transformer model for accurate power distribu�on predic�on

  88. [92]

    Sri; Suresh, Vishnu, A Deep Learning Model Using Transformer Network and Expert Op�mizer for an Hour Ahead Wind Power Forecas�ng

    Thiyagarajan, Anushalini; Revathi, B. Sri; Suresh, Vishnu, A Deep Learning Model Using Transformer Network and Expert Op�mizer for an Hour Ahead Wind Power Forecas�ng

  89. [93]

    Mirza, Adeel Feroz; Shu, Zhaokun; Usman, Muhammad; Mansoor, Majad; Ling, Qiang , Quan�le-transformed mul�-aten�on residual framework (QT-MARF) for medium-term PV and wind power predic�on

  90. [94]

    Zhu, Leyang; Huang, Xiaoqiao; Zhang, Zongbin; Li, Chengli; Tai, Yonghang, A novel U-LSTM-AFT model for hourly solar irradiance forecas�ng

  91. [95]

    Mo, Fan; Jiao, Xuan; Li, Xingshuo; Du, Yang; Yao, Yun�ng; Meng, Yuxiang; Ding, Shuye, A novel mul�-step ahead solar power predic�on scheme by deep learning on transformer structure

  92. [96]

    Jiang, Canchen; Liebman, Ariel; Jie, Bo; Wang, Hao , Dynamic rolling horizon op�miza�on for network - constrained V2X value stacking of electric vehicles under uncertain�es

  93. [97]

    Liao, Xuan; Wong, Man Sing; Zhu, Rui , Dual-gate Temporal Fusion Transformer for es�ma�ng large -scale land surface solar irradia�on

  94. [98]

    Wang, Boyu; Dabbaghjamanesh, Morteza; Kavousi -Fard, Abdollah; Yue, Yuntao , AI-enhanced mul� -stage learning-to-learning approach for secure smart ci�es load management in IoT networks

  95. [99]

    Kim, Jimin; Obregon, Josue; Park, Hoonseok; Jung, Jae -Yoon, Mul�-step photovoltaic power forecas�ng using transformer and recurrent neural networks

  96. [100]

    Feng, Zhengyuan; Sun, Yuheng; Ning, Jun; Tang, Shoujuan; Liu, Guangxin; Liu, Fangtao; Li, Yang; Shi, Lei , Implemen�ng a provincial -level universal daily industrial carbon emissions predic�on by fine -tuning the large language model, APPLIED ENERGY, 2025

    2025

  97. [101]

    Wang, Ru-Guan; Chuang, Mei-Ling; Ke, Chi-Yun; Chien, Yi-Fan; Ho, Wen-Jen; Chiang, Kuei-Chun; Hung, Yung- Chieh; Tsai, Chu Hsien; Chou, Chien- Cheng, Predic�ng Imminent Electrical Safety Incidents Using Smart Meter Big Data With Large Language Models, IEEE ACCESS, 2024

    2024

  98. [102]

    Choi, Sebin; Yoon, Sungmin, GPT-based data-driven urban building energy modeling (GPT-UBEM): Concept, methodology, and case studies, ENERGY AND BUILDINGS, 2024

    2024

  99. [105]

    Liao, Wenlong; Wang, Shouxiang; Yang, Dechang; Yang, Zhe; Fang, Jiannong; Rehtanz, Chris�an; Porte-Agel, Fernando, TimeGPT in load forecas�ng: A large �me series model perspec�ve, APPLIED ENERGY, 2025

    2025

  100. [106]

    Zhang, Liang; Ford, Vitaly; Chen, Zhelun; Chen, Jianli , Automa�c building energy model development and debugging using large language models agen�c workflow, ENERGY AND BUILDINGS, 2025

    2025

  101. [107]

    Qaisar, Irfan; Liang, Wei; Sun, Kailai; Xing, Tian; Zhao, Qianchuan , An experimental compara�ve study of energy saving based on occupancy-centric control in smart buildings, BUILDING AND ENVIRONMENT, 2025

    2025

  102. [108]

    Zhang, Liang; Chen, Zhelun; Ford, Vitaly , Advancing building energy modeling with large language models: Explora�on and case studies, ENERGY AND BUILDINGS, 2024

    2024

  103. [109]

    Liu, Changchun; Nie, Qingwei , A blockchain-based LLM-driven energy-efficient scheduling system towards distributed mul� -agent manufacturing scenario of new energy vehicles within the circular economy , COMPUTERS & INDUSTRIAL ENGINEERING, 2025

    2025

  104. [111]

    Buster, Grant; Pinchuk, Pavlo; Barrons, Jacob; McKeever, Ryan; Levine, Aaron; Lopez, Anthony , Suppor�ng energy policy research with large language models: A case study in wind energy si�ng ordinances, ENERGY AND AI, 2024

    2024

  105. [113]

    Xu, Yifang; Zhu, Siyao; Cai, Jiannan; Chen, Jianli; Li, Shuai , A large language model-based pla�orm for real- �me building monitoring and occupant interac�on, JOURNAL OF BUILDING ENGINEERING, 2025

    2025

  106. [114]

    Ma, Yichuan X.; Yeung, Lawrence K., BEForeGAN: An image-based deep genera�ve approach for day-ahead forecas�ng of building HVAC energy consump�on, APPLIED ENERGY, 2024

    2024

  107. [115]

    Asimopoulos, Dimitrios Christos; Radoglou -Gramma�kis, Panagio�s; Makris, Ioannis; Mladenov, Valeri; Psannis, Konstan�nos E.; Goudos, So�rios; Sarigiannidis, Panagio�s, Breaching the Defense: Inves�ga�ng FGSM and CTGAN Adversarial Atacks on IEC 60870- 5-104 AI -enabled Intrusi...

    2023

  108. [116]

    Nie, Yuhao; Zelikman, Eric; Scot, Andea; Paleta, Quen�n; Brandt, Adam , SkyGPT: Probabilis�c ultra-short- term solar forecas�ng using synthe�c sky images from physics -constrained VideoGPT, ADVANCES IN APPLIED ENERGY, 2024

    2024

  109. [117]

    Heo, SungKu; Ko, Jaerak; Kim, SangYoun; Jeong, Chanhyeok; Hwangbo, Soonho; Yoo, ChangKyoo, Explainable AI-driven net -zero carbon roadmap for petrochemical industry considering stochas�c scenarios of remotely sensed offshore wind energy, JOURNAL OF CLEANER PRODUCTION, 2022

    2022

  110. [118]

    Zhang, L., & Chen, Z. (2025). Opportuni�es of applying Large Language Models in building energy sector. Renewable and Sustainable Energy Reviews, 214, 115558

    2025

  111. [119]

    Ullah, A., Qi, G., Hussain, S., Ullah, I., & Ali, Z. (2024). The role of llms in sustainable smart ci�es: Applica�ons, challenges, and future direc�ons. arXiv preprint arXiv:2402.14596

    2024 arXiv

  112. [120]

    & Liu, P

    Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., ... & Liu, P . J. (2020). Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of machine learning research, 21(140), 1-67

    2020

  113. [121]

    & Dong, Z

    Ruan, J., Liang, G., Zhao, H., Liu, G., Sun, X., Qiu, J., ... & Dong, Z. Y . (2024). Applying large language models to power systems: poten�al security threats. IEEE transac�ons on smart grid

    2024

  114. [122]

    , C h a n g , M

    D e v l i n , J . , C h a n g , M . W . , L e e , K . , & T o u t a n o v a , K . ( 2 0 1 9 , J u n e ) . B e r t : P r e-training of deep bidirec�onal transformers for language understanding. In Proceedings of the 2019 conference of the North American chapter of the associa�o...

    2019

  115. [123]

    Jiang, G., Ma, Z., Zhang, L., & Chen, J. (2024). EPlus-LLM: A large language model-based compu�ng pla�orm for automated building energy modeling. Applied Energy, 367, 123431

    2024

  116. [124]

    B., Kuppan, K., & Divya, B

    Acharya, D. B., Kuppan, K., & Divya, B. (2025). Agen�c AI: Autonomous Intelligence for Complex Goals –A Comprehensive Survey. IEEE Access

    2025

  117. [125]

    Zihang Qiu, Chaojie Li, Zhongyang Wang, Huadong Mo, Renyou Xie, Guo Chen, Zhaoyang Dong, FPE-LLM: Highly Intelligent Time-Series Forecas�ng and Language Interac�on LLM in Energy Systems, arXiv:2411.00852v1 [cs.LG] 30 Oct 2024

    2024 arXiv

  118. [126]

    Edge Offloading in Smart Grid

    Arcas, G.I.; Cioara, T .; Anghel, I.; Lazea, D.; Hangan, A. Edge Offloading in Smart Grid. Smart Ci�es 2024, 7, 680-711

    2024

  119. [127]

    Seyyedreza Madani, Ahmadreza Tavasoli, Zahra Khoshtarash Astaneh, Pierre-Olivier Pineau, Large Language Models integra�on in Smart Grids, arXiv:2504.09059v1 [cs.CY] 12 Apr 2025

    2025 arXiv

  120. [128]

    Gao, C., Lan, X., Li, N. et al. Large language models empowered agent -based modeling and simula�on: a survey and perspec�ves. Humanit Soc Sci Commun 11, 1259 (2024). htps://doi.org/10.1057/s41599-024- 03611-3

    2024 doi

  121. [129]

    Opy Das, Muhammad Hamza Zafar, Filippo Sanfilippo, Souman Rudra, Mohan Lal Kolhe, Advancements in digital twin technology and machine learning for energy systems: A comprehensive review of applica�ons in smart grids, renewable energy, and electric vehicle op�misa�on, Energy Con...

    2024

  122. [130]

    Muhammad Arslan, Lamine Mahdjoubi, Saba Munawar, Driving sustainable energy transi�ons with a mul�- source RAG -LLM system, Energy and Buildings, Volume 324, 2024, 114827, ISSN 0378 -7788, htps://doi.org/10.1016/j.enbuild.2024.114827

    2024

  123. [131]

    Aske Plaat, Max van Duijn, Niki van Stein, Mike Preuss, Peter van der Puten, Kees Joost Batenburg, Agen�c Large Language Models, a survey, arXiv:2503.23037v2 [cs.AI] 03 Apr 2025

    2025

  124. [132]

    Andrea Matarazzo, Riccardo Torlone, A Survey on Large Language Models with some Insights on their Capabili�es and Limita�ons, arXiv:2501.04040v1 [cs.CL] 03 Jan 2025

    2025 arXiv

  125. [133]

    , S h i w a k o � , S

    T h a p a , S . , S h i w a k o � , S . , S h a h , S . B . e t a l . L a r g e l a n g u a g e m o d e l s ( L L M ) i n c o m p u t a � o n a l s o c i a l s c i e n c e : prospects, current state, and challenges. Soc. Netw. Anal. Min. 15, 4 (2025). htps://doi.org/10.1007/s1...

    2025 doi

  126. [134]

    Liu, M., Zhang, L., Chen, J. et al. Large language models for building energy applica�ons: Opportuni�es and challenges. Build. Simul. 18, 225–234 (2025)

    2025

  127. [135]

    Linyao Yang, Shi Luo, Xi Cheng, Lei Yu, Leveraging Large Language Models for Enhanced Digital Twin Modeling: Trends, Methods, and Challenges, arXiv:2503.02167v1 [cs.ET] 04 Mar 2025

    2025 arXiv

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.