Abstract: 

人工智能助力碳中和：开创未来能源系统研究

[1]Abdar M, Pourpanah F, Hussain S, et al., 2021. A review of uncertainty quantification in deep learning: techniques, applications and challenges. Information Fusion, 76:243-297.

[2]Aliramezani M, Koch CR, Shahbakhti M, 2022. Modeling, diagnostics, optimization, and control of internal combustion engines via modern machine learning techniques: a review and future directions. Progress in Energy and Combustion Science, 88:100967.

[3]Amiri MM, Shadman M, Estefen SF, 2024. A review of physical and numerical modeling techniques for horizontal-axis wind turbine wakes. Renewable and Sustainable Energy Reviews, 193:114279.

[4]Atkinson C, McCane B, Szymanski L, et al., 2021. Pseudo-rehearsal: achieving deep reinforcement learning without catastrophic forgetting. Neurocomputing, 428:291-307.

[5]Badra JA, Khaled F, Tang M, et al., 2021. Engine combustion system optimization using computational fluid dynamics and machine learning: a methodological approach. Journal of Energy Resources Technology, 143(2):022306.

[6]Bai ZL, Gao Q, Zhang HZ, et al., 2026. An optimization scheduling strategy for electric-heat-hydrogen integrated energy systems based on memory-enhanced deep reinforcement learning. Journal of Zhejiang University-SCIENCE A, 27(8):809-824.

[7]Boussaid T, Rousset F, Scuturici VM, et al., 2024. Enabling fast prediction of district heating networks transients via a physics-guided graph neural network. Applied Energy, 370:123634.

[8]Ghate A, Sundar A, Zhu QL, et al., 2024. Development of an integrated energy and thermal planner for a series hybrid off-road autonomous tracked vehicle. Energy Conversion and Management, 322:119163.

[9]He ZX, Yu QH, Ye JD, et al., 2024. Optimization of plate-fin heat exchanger performance for heat dissipation of thermoelectric cooler. Case Studies in Thermal Engineering, 53:103953.

[10]Huang C, Huang XB, Guo JM, et al., 2026. Physics-informed deep learning for data-efficient and robust photovoltaic power forecasting. Journal of Zhejiang University-SCIENCE A, 27(8):885-896.

[11]Li XH, Huang ZH, Shao SS, et al., 2024. Machine learning prediction of physical properties and nitrogen content of porous carbon from agricultural wastes: effects of activation and doping process. Fuel, 356:129623.

[12]Liu SY, Li QS, Lu B, et al., 2025. Prediction of offshore wind turbine wake and output power using large eddy simulation and convolutional neural network. Energy Conversion and Management, 324:119326.

[13]Lu S, Gao ZH, Sun Y, et al., 2024. Aggregate model of district heating network for integrated energy dispatch: a physically informed data-driven approach. IEEE Transactions on Sustainable Energy, 15(3):1859-1871.

[14]Lund H, Østergaard PA, Sorknæs P, et al., 2025. District heating in clean energy systems. Nature Reviews Clean Technology, 1(8):532-546.

[15]Mayer MJ, 2022. Benefits of physical and machine learning hybridization for photovoltaic power forecasting. Renewable and Sustainable Energy Reviews, 168:112772.

[16]Niccolai A, Dolara A, Ogliari E, 2021. Hybrid PV power forecasting methods: a comparison of different approaches. Energies, 14(2):451.

[17]Sadeghian O, Shotorbani AM, Ghassemzadeh S, et al., 2025. Energy management of hybrid fuel cell and renewable energy based systems–a review. International Journal of Hydrogen Energy, 107:135-163.

[18]Shi MS, Vasquez JC, Guerrero JM, et al., 2023. Smart communities–design of integrated energy packages considering incentive integrated demand response and optimization of coupled electricity-gas-cooling-heat and hydrogen systems. International Journal of Hydrogen Energy, 48(80):31063-31077.

[19]Sun SX, Xu ZJ, Wang ZH, et al., 2026. Hierarchical learning method for array flow field prediction integrated with a deep neural network. Journal of Zhejiang University-SCIENCE A, 27(8):837-851.

[20]Wen ZX, Wu JL, Cao XW, et al., 2024. Machine learning and prediction study on heat transfer of supercritical CO₂ in pseudo-critical zone. Applied Thermal Engineering, 243:122630.

[21]Yan YC, Huang Q, Xie TF, et al., 2026. Data-driven neural surrogates for ReaxFF molecular dynamics simulations in engine-relevant combustion chemistry. Journal of Zhejiang University-SCIENCE A, 27(8):825-836.

[22]Yuan XZ, Suvarna M, Low S, et al., 2021. Applied machine learning for prediction of CO₂ adsorption on biomass waste-derived porous carbons. Environmental Science & Technology, 55(17):11925-11936.

[23]Zhang YC, Johansson P, Kalagasidis AS, 2022. Assessment of district heating and cooling systems transition with respect to future changes in demand profiles and renewable energy supplies. Energy Conversion and Management, 268:116038.

[24]Zhao K, Shao CM, Sun XX, et al., 2026. Performance analysis and optimization of staggered fin heat exchangers under varying altitudes using machine learning. Journal of Zhejiang University-SCIENCE A, 27(8):871-884.

[25]Zhao P, Zhou JH, Li GY, et al., 2026. Mechanism-enhanced multitask distillation for predictive, interpretable design of biomass-based activated carbons. Journal of Zhejiang University-SCIENCE A, 27(8):852-870.

[26]Zheng X, Zhao JL, Yan YC, et al., 2026. A thermal management strategy for hybrid electric drive tracked vehicles considering system safety and energy consumption based on the GMA-TD3-MPC algorithm. Journal of Zhejiang University-SCIENCE A, 27(8):897-910.

[27]Zhou HY, Zhang SH, Peng JQ, et al., 2021. Informer: beyond efficient transformer for long sequence time-series forecasting. Proceedings of the 35th AAAI Conference on Artificial Intelligence, p.11106-11115.

[28]Zhou X, Wang SJ, Feng YH, et al., 2026. Integrating artificial intelligence in the lifecycle evolution of district heating networks: challenges and opportunities. Journal of Zhejiang University-SCIENCE A, 27(8):783-808.