Intelligent Question-Answering System and Inference Mechanism for Grid Dispatching with Energy Storage Access Scenarios Based on Semantic Models

Authors

  • Lin Yang China Southern Power Dispatching & Control Center
  • Fan Yang China Southern Power Dispatching & Control Center
  • Yongqing Feng China Southern Power Dispatching & Control Center
  • Cheng Zeng China Southern Power Dispatching & Control Center
  • Peng Zhou China Southern Power Dispatching & Control Center
  • Hongliang Gao China Southern Power Dispatching & Control Center

DOI:

https://doi.org/10.4108/ew.13834

Keywords:

Power grid dispatching, Reasoning mechanism, Intelligent question answering, Robustly optimized BERT pretraining approach, Translation embedding, Energy storage and grid connection

Abstract

INTRODUCTION: In the power grid energy storage dispatch system, after the large-scale integration of new energy storage systems, the demand for inquiries regarding the operation and maintenance of storage equipment as well as the coordinated power regulation has significantly increased. The intelligent question-answering function is of vital importance in enhancing the efficiency of dispatchers.

OBJECTIVES:The research focuses on the field of scheduling, integrating expertise in power dispatching and energy storage grid connection operation with natural language processing technology, and has constructed an efficient question-answering generation framework. A semantic model optimized by dynamic masking and large-batch training is combined with a dispatching knowledge graph to enhance semantic understanding.

METHODS: The model achieves an intent classification accuracy of 0.97 and an F1 score of 98.1%. The accuracy of terminology disambiguation and complex sentence analysis reaches 92.5% and 84.8%, respectively. Based on this framework and a reasoning mechanism, an intelligent question answering system is developed using multi-algorithm collaborative reasoning to support answer generation and optimization.

RESULTS: Experimental results show that the system maintains an accuracy of 91.3% even at difficulty level 5. The violation rate of dispatching procedures is as low as 22.5%, with a maximum memory usage of 805 MB and a response time of 63.2 seconds.

CONCLUSION: This system can automatically generate effective responses, optimize the accuracy and rationality of question-and-answer interactions, and precisely output compliant control plans for abnormal handling issues related to energy storage grid connection. It demonstrates significant advantages in enhancing response efficiency and accuracy.

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References

[1] Ugboke P, Ebimaro J, Olanrewaju P, et al. The impact of digitization towards technological revolution in the power industry[J]. NIPES-Journal of Energy Technology and Environment, 2024, 6(2): 193-200.

[2] Biancofiore G M, Deldjoo Y, Noia T D, et al. Interactive question answering systems: Literature review[J]. ACM Computing Surveys, 2024, 56(9): 32-38.

[3] Kim K, Park S. Aobert: All-modalities-in-one BERT for multimodal sentiment analysis[J]. Information Fusion, 2023, 92: 37-45.

[4] Laurer M, Van Atteveldt W, Casas A, Welbers K. Less annotating, more classifying: Addressing the data scarcity issue of supervised machine learning with deep transfer learning and BERT-NLI[J]. Political Analysis, 2024, 32(1): 84-100.

[5] Xu J, Guo Y. TransE-KCB: An improved knowledge graph representation method for negative sample sampling[J]. Computer Applications and Software, 2024, 41(8): 345-350.

[6] Jiang T, Shen D, Zhang Z, Liu H, Zhao G, Wang Y, Chen W. Battery technologies for grid-scale energy storage[J]. Nature Reviews Clean Technology, 2025, 1(7): 474-492.

[7] Chang X, Zhao Y M, Yuan B, Fan M, Meng Q, Guo Y G, Wan L J. Solid-state lithium-ion batteries for grid energy storage: opportunities and challenges. Science China Chemistry, 2024, 67(1): 43-66.

[8] Sobhanam H, Prakash J. Analysis of fine tuning the hyper parameters in RoBERTa model using genetic algorithm for text classification[J]. International Journal of Information Technology, 2023, 15(7): 3669-3677.

[9] Özkurt C. Comparative analysis of state-of-the-art Q&A models: BERT, RoBERTa, DistilBERT, and ALBERT on SQuAD v2 dataset[J]. Chaos and Fractals, 2024, 1(1): 19-30.

[10] Cheruku R, Hussain K, Kavati I, et al. Sentiment classification with modified RoBERTa and recurrent neural networks[J]. Multimedia Tools and Applications, 2024, 83(10): 29399-29417.

[11] Madichetty S, M S, Madisetty S. A RoBERTa based model for identifying the multi-modal informative tweets during disaster[J]. Multimedia Tools and Applications, 2023, 82(24): 37615-37633.

[12] Kumar B V, Sadanandam M. A fusion architecture of BERT and RoBERTa for enhanced performance of sentiment analysis of social media platforms[J]. International Journal of Computing and Digital Systems, 2024, 15(1): 51-66.

[13] Yang L, Lv C, Wang X, et al. Collective entity alignment for knowledge fusion of power grid dispatching knowledge graphs[J]. IEEE/CAA Journal of Automatica Sinica, 2022, 9(11): 1990-2004.

[14] Li X, Yang N, Li Z, et al. Confidence estimation transformer for long-term renewable energy forecasting in reinforcement learning-based power grid dispatching[J]. CSEE Journal of Power and Energy Systems, 2022, 10(4): 1502-1513.

[15] Fan S, Guo J, Ma S, et al. Framework and key technologies of human-machine hybrid-augmented intelligence system for large-scale power grid dispatching and control[J]. CSEE Journal of Power and Energy Systems, 2023, 10(1): 5-12.

[16] Ji Z, Wang X, Zhang J, Wu D. Construction and application of knowledge graph for grid dispatch fault handling based on pre-trained model[J]. Global Energy Interconnection, 2023, 6(4): 493-504.

[17] Garrido-Merchan E C, Gozalo-Brizuela R, Gonzalez-Carvajal S. Comparing BERT against traditional machine learning models in text classification[J]. Journal of Computational and Cognitive Engineering, 2023, 2(4): 352-356.

[18] Kim J H, Park S W, Kim J Y, et al. RoBERTa-CoA: RoBERTa-based effective finetuning method using co-attention[J]. IEEE Access, 2023, 11: 120292-120303.

[19] Meng D, Ma B, Shang Z. A knowledge set recommendation method for online education in universities based on DV-TransE model and social networks[J]. International Journal of Networking and Virtual Organisations, 2024, 30(1): 44-56.

[20] Chen L, Zhou Y, Wei Q, et al. ELPGPT: Large language models enhancing link prediction in electrical knowledge graph[J]. International Journal of High Speed Electronics and Systems, 2025, 34(01): 2540115.

[21] Kang M, Lee S, Baek J, et al. Knowledge-augmented reasoning distillation for small language models in knowledge-intensive tasks[C]//Advances in Neural Information Processing Systems. 2023, 36: 48573-48602.

[22] Irham A, Roslan M F, Jern K P, et al. Hydrogen energy storage integrated grid: A bibliometric analysis for sustainable energy production[J]. International Journal of Hydrogen Energy, 2024, 63: 1044-1087.

[23] Zhou N, Xu Y, Cho S, Wee C T. A systematic review for switchgear asset management in power grids: Condition monitoring, health assessment, and maintenance strategy[J]. IEEE Transactions on Power Delivery, 2023, 38(5): 3296-3311.

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Published

21-09-2026

Issue

Section

AI-Powered Hybrid Energy Storage Optimization for Grid Cost-Efficiency and Stability

How to Cite

1.
Yang L, Yang F, Feng Y, Zeng C, Zhou P, Gao H. Intelligent Question-Answering System and Inference Mechanism for Grid Dispatching with Energy Storage Access Scenarios Based on Semantic Models. EAI Endorsed Trans Energy Web [Internet]. 2026 Sep. 21 [cited 2026 Sep. 21];13. Available from: https://publications.eai.eu/index.php/ew/article/view/13834

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