Artificial Intelligence for Power Systems with Renewable Energy

Luolin Xiong; Yang Tang; Kankar Bhattacharya; Feng Qian

doi:10.1016/j.eng.2025.03.021

Engineering ›› 2025, Vol. 52 ›› Issue (9) :25 -28. DOI: 10.1016/j.eng.2025.03.021

Views & Comments

Artificial Intelligence for Power Systems with Renewable Energy

Author information +

History +

PDF (1005KB)

Cite this article

Download citation ▾

Luolin Xiong, Yang Tang, Kankar Bhattacharya, Feng Qian. Artificial Intelligence for Power Systems with Renewable Energy. Engineering, 2025, 52 (9) : 25-28 DOI:10.1016/j.eng.2025.03.021

登录浏览全文

4963

注册一个新账户忘记密码

References

Publishing order | Descend order by publishing year | Descend order by cited within

[1]	Gidden MJ, Gasser T, Grassi G, Forsell N, Janssens I, Lamb WF, et al. Aligning climate scenarios to emissions inventories shifts global benchmarks. Nature 2023; 624(7990):102-8.

[2]	Wei YM, Chen K, Kang JN, Chen W, Wang XY, Zhang X. Policy and management of carbon peaking and carbon neutrality: a literature review. Engineering 2022; 14:52-63.

[3]	Qian F. Smart process manufacturing toward carbon neutrality: digital transformation in process manufacturing for achieving the goals of carbon peak and carbon neutrality. Engineering 2023; 27:1-2.

[4]	Information Office of the State Council of the People’s Republic of China. Energy transition of China [Internet]. Beijing: National Energy Administration; 2024 Aug 29 [cited 2024Sep 17]. Available from: https://www.nea.gov.cn/2024-08/29/c_1310785406.htmChinese

[5]	Xiong LL, Tang Y, Liu CS, Mao S, Meng K, Dong ZY, et al. Interpretable deep reinforcement learning for optimizing heterogeneous energy storage systems. IEEE Trans Circuits Syst I Regul Pap 2024; 71(2):910-21.

[6]	Xiong LL, Tang Y, Liu CS, Mao S, Meng K, Dong ZY, et al. Meta-reinforcement learning-based transferable scheduling strategy for energy management. IEEE Trans Circuits Syst I Regul Pap 2023; 70(4):1685-95.

[7]	Gehrig D, Scaramuzza D. Low-latency automotive vision with event cameras. Nature 2024; 629(8014):1034-40.

[8]	Qian F, Tang Y, Yu XH. The future of process industry: a cyber-physical-social system perspective. IEEE Trans Cybern 2024; 54(7):3878-89.

[9]	Szymanski NJ, Rendy B, Fei Y, Kumar RE, He T, Milsted D, et al. An autonomous laboratory for the accelerated synthesis of novel materials. Nature 2023; 624 (7990):86-91.

[10]	Qian F. Editorial for special issue "artificial intelligence energizes process manufacturing". Engineering 2021; 7(9):1193-4.

[11]	Zhang K, Sun Q, Zhao C, Tang Y. Causal reasoning in typical computer vision tasks. Science China Technological Sciences 2024; 67(1):105-20.

[12]	Nobel Prizes 2024 [Internet]. Stockholm: The Nobel Foundation; [cited 2024Oct 17]. Available from: https://www.nobelprize.org/all-nobel-prizes-2024/

[13]	Wu T, He S, Liu J, Sun S, Liu K, Han QL, et al. A brief overview of ChatGPT: the history, status quo and potential future development. IEEE/CAA J Autom Sin 2023; 10(5):1122-36.

[14]	State Grid Corporation of China releases the first hundred-billion-level multimodal power industry large model] [Internet]. Beijing: Xinhuanet; 2024 Dec 24 [cited 2024 Dec 27]. Available from: http://www.news.cn/power/20241224/b37c81368b674fc3978c0703848fddcf/c.htmlChinese.

[15]	Xiong LL, Tang Y, Mao S, Liu HY, Meng K, Dong ZY, et al. A two-level energy management strategy for multi-microgrid systems with interval prediction and reinforcement learning. IEEE Trans Circuits Syst I Regul Pap 2022; 69 (4):1788-99.

[16]	Ryu S, Yu Y. Quantile-mixer: a novel deep learning approach for probabilistic short-term load forecasting. IEEE Trans Smart Grid 2024; 15 (2):2237-50.

[17]	Kong WC, Dong ZY, Jia YW, Hill DJ, Xu Y, Zhang Y. Short-term residential load forecasting based on LSTM recurrent neural network. IEEE Trans Smart Grid 2019; 10(1):841-51.

[18]	Gruver N, Finzi M, Qiu S, Wilson AG. Large language models are zero-shot time series forecasters. In: Oh A, Naumann T, Globerson A, Saenko K, Hardt M, Levine S, editors. Proceedings of the 37th Conference on Neural Information Processing Systems; 2023 Dec 10-16; New Orleans, LA, USA; 2023.

[19]	Zhao Y, Liu J, Liu XM, Nie YX, Liu JC, Chen C. LDM: a generic data-driven large distribution network operation model. IEEE Trans Smart Grid 2024; 15 (4):4284-7.

[20]	Zhao J, Wen F, Huang J, Liu J, Zhao H, Cheng Y, et al. Prospect of artificial general intelligence for power systems based on large language model: theory and applications. Autom Electr Power Syst 2024; 48(6):13-28. Chinese.

[21]	Li JH, Wang CL, Zhang YR, Wang H. Temporal-aware deep reinforcement learning for energy storage bidding in energy and contingency reserve markets. IEEE Trans Energy Mark Policy Regul 2024; 2(3):392-406.

[22]

Park JS, O’Brien J, Cai CJ, Morris MR, Liang P, Bernstein MS. Generative agents:interactive simulacra of human behavior. In: Follmer S, Han J, Steimle J, Riche NH, editors. Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology; 2023 Oct 29-Nov 1; San Francisco, CA, USA. New York City: Association for Computing Machinery; 2023.