The energy sector is entering a pivotal transition driven by decarbonization, energy security, and rising expectations for reliability and resilience. Across renewables, oil and gas, and electric power systems, data are increasingly multi-source, multi-modal, and heterogeneous, while operations remain tightly constrained by physics and shaped by uncertainty. Delivering deployable and trustworthy intelligence therefore requires bridging physics- and process-based models with data-driven methods, so that modelling, optimization, and control can be executed reliably in real operations.

Guided by the vision of low-carbon and safety-oriented intelligent optimization and control of energy processes, this special issue—Toward Next-Generation Smart Energy: Intelligent Technologies, Knowledge and Digital Systems, and Modelling—invites research and practice that focus on intelligent energy engineering that enable smart energy transformation, covering domains of oil and gas (smart drilling, intelligent surface facilities, field development, etc.), renewables (wind/solar forecasting, asset health, inverter and plant control, grid integration, etc.), and electric power (situational awareness, protection, stability control, flexibility scheduling, etc.). We welcome submissions that apply intelligent and AI-based approaches to address key challenges in these fields. 

Topics of interest include, but are not limited to:

· Machine learning and deep learning for forecasting, soft sensing, pattern recognition, and anomaly detection

· Fault diagnosis, prognostics, and predictive maintenance

· Reinforcement learning and closed-loop decision-making for process control and operations

· Advanced process control and optimization

· Physics-informed learning and mechanistic–data fusion

· Digital twins for units, plants, and fields, including online calibration and data assimilation

· Simulation-based optimization and surrogate modeling

· Mixed-integer, robust, and stochastic optimization for planning, scheduling, and supply-chain operations

· Uncertainty quantification, probabilistic prediction, and risk-aware decision support

· Integrated subsurface–surface optimization across the oil and gas development lifecycle

· Edge–cloud deployment, industrial IoT sensing, and data infrastructure for industrial AI

· Trustworthy AI: validation, interpretability, safety, and operational governance

Guest Editors: 

Dong Xiao, Professor, Southwest Petroleum University, China

Li Yang, Professor, China University of Mining and Technology, China

Hongwei Yang, Associate Professor, China University of Petroleum-Beijing, China

Li Yang, Associate Professor, Southwest Petroleum University, China

Deadline

The deadline for manuscript submissions is 31 December 2026, but we can accommodate extensions on a case-by-case basis. Manuscripts submitted before the deadline will be subject to an APC of $2750 USD. All accepted papers will be published online.

Submission Instructions

Please submit the full manuscript to The Knowledge Engineering Review via our Online Submission System. All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors for submission of manuscripts is available on the For Authors page.

Additionally, please choose the topic of this Special Issue when submitting and specify it in your cover letter. For further inquiries, please contact Guest Editors:

Dong Xiao (xiaodong@swpu.edu.cn)

Li Yang (li.yang@cumt.edu.cn)

Hongwei Yang (zerotone@cup.edu.cn)

Li Yang (liyang61@swpu.edu.cn)