Qianpeng Hao^*, Qiang Li, Changyuan Yu, Deqing Zhang, Wenze Li, Yaowen Liu, Chao Wang, Chengran Song, Xiyu Feng, Xingchao Guo

Energy Engineering, Vol.123, No.9, 2026, DOI:10.32604/ee.2026.074324 - 06 August 2026

Abstract Driven by the “Carbon Peak and Carbon Neutrality” strategic goals, high penetration of renewable energy poses severe challenges to power system flexibility. Unlocking the adjustable potential of demand-side industrial loads has become a critical pathway for constructing new-type power systems. To address the limitations of existing research, including single-dimensional characterisation of adjustable potential, insufficient consideration of both best and worst solutions in evaluation methods, and a lack of cluster coordination perspectives, this paper proposes a multi-dimensional adjustable potential assessment and priority ranking method for industrial loads. Firstly, based on the Affinity Propagation (AP) and k-means… More >

Xuan Kien Pham¹, Van Dat Nguyen^2,*, Van Thanh Phan^3,*, Duc Trien Nguyen^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.086196 - 27 July 2026

Abstract Accurate energy consumption forecasting faces two major challenges: limited historical data and complex consumption patterns. To address these challenges, this study proposes a new hybrid framework named the Decomposition-based Grey-Neuro-Fuzzy Architecture (DeGNA). The model first uses the Denton method to convert limited annual records into high-frequency monthly data. Next, it applies STL decomposition to separate the data into trend, seasonal and residuals components. A rolling-window GM(1,1) model is then used to predict the main growth trend, while a GWO-optimized ANFIS model uses economic indicators (IIP and FDI) to forecast complex seasonal changes. This study evaluates… More >

Nusrat Yasmin Nadia¹, Md Habibul Arif², Habibor Rahman Rabby³, Md Iftekhar Monzur Tanvir¹, Md Jakir Hossen^4,*, M. F. Mridha⁵

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.074236 - 23 July 2026

Abstract Supply chain resilience and efficiency are vital in industries characterized by volatile demand and uncertain supply, such as textiles and personal protective equipment (PPE). Traditional forecasting and optimization approaches often operate in isolation, limiting their real-world effectiveness. This paper proposes a Hybrid AI Framework for Demand–Supply Forecasting and Optimization (HAF-DS), which integrates a Long Short-Term Memory (LSTM)–based demand forecasting module with a mixed-integer linear programming (MILP) optimization layer. The LSTM captures temporal and contextual demand dependencies, while the optimization layer prescribes cost-efficient replenishment and allocation decisions. The framework jointly minimizes forecasting error and operational cost More >

Zewen Ge^1,*, Jihui Liu², Shuai Yuan¹, Mufan Zhuang^3,*

Energy Engineering, Vol.123, No.8, 2026, DOI:10.32604/ee.2026.082217 - 12 July 2026

Abstract Limiting warming to the Paris temperature goals requires a rapid scale-up of low-carbon energy, yet recent experience suggests that deployment is increasingly shaped by delivery constraints rather than by technology cost trends alone. This review synthesizes peer-reviewed evidence on five constraints that repeatedly slow net-zero buildouts: lengthy approval and grid-connection processes; capital-intensive investment profiles that heighten sensitivity to the cost of capital and revenue risk; bottlenecks in critical minerals, processing, and manufacturing; social contestation and local governance that translate into siting exclusions, delays, and cancellations; and modeling traditions that can underrepresent these non-marginal frictions. Accordingly,… More > Graphic Abstract

Franjo Pranjić^1,*, Doris Beljan², Primož Praper³, Matej Fike¹, Peter Virtič¹, Antun Pfeifer², Neven Duić^2,4

Energy Engineering, Vol.123, No.8, 2026, DOI:10.32604/ee.2026.080284 - 12 July 2026

Abstract This paper evaluates the role of aggregated demand-side flexibility from public buildings in supporting renewable energy integration and decarbonization of the Slovenian energy system. Using high-resolution monitoring data from over 100 public buildings and the H2RES energy system optimization model, two long-term scenarios are analyzed for the period 2020–2050: a reference scenario without public building flexibility and a flexibility scenario in which public building heat demand is electrified via heat pumps. The year 2020 is used as an internally consistent optimization reference for demand levels and technology availability, rather than as a statistical reconstruction of… More >

Liangyu Lin, Li Feng^*, Lin Huang

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.083047 - 15 June 2026

Abstract The modern sports streaming market is severely fragmented, forcing fans into costly, siloed platforms. While blockchain-based decentralized architectures offer a unified, interoperable sport streaming ecosystem, securely delivering commercial video over untrusted infrastructure remains a profound cryptographic challenge. Existing schemes fail to simultaneously support highly granular on-demand highlights and large scale dynamic live subscriptions. To resolve this, we propose a novel decentralized authorization architecture that systematically integrates existing cryptographic primitives into a decoupled three-layer protocol. By securely bridging on-chain state transitions with off-chain cryptographic enforcement, our architecture directly maps commercial payment workflows onto the underlying key More >

Zujun Ding, Zhi Liu, Peng Huang, Yuhan Qian, Chengyi Li, Zizhuo Yu, Hui Huang, Baolian Liu, Wan Chen, Jie Ji^*

Energy Engineering, Vol.123, No.6, 2026, DOI:10.32604/ee.2025.074440 - 27 May 2026

Abstract Under extreme weather conditions (such as hurricanes and heatwaves causing sudden drops in renewable energy output and surges in load), microgrid operations face severe challenges due to the uncertainty of renewable energy and load fluctuations. Although existing research has focused on microgrid optimal scheduling or electric vehicle integration, there has not yet been a systematic approach to multi-timescale scheduling that combines electric vehicle fleets under extreme weather scenarios, and particularly, explicit modeling of weather events and their impact on component failure rates and transmission lines is lacking. This paper proposes, for the first time, a… More >

Jinsung Park^1,#, Jaehyuk Lee^1,2,#, Eunchan Kim^1,3,*

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.079734 - 08 May 2026

Abstract Accurate short-term load forecasting is essential for reliable power system operation, particularly under the increasing uncertainty caused by abnormal weather and socio-economic fluctuations. This study presents a month-conditioned boosting framework that integrates SHapley Additive Explanations (SHAPs) into model refinement. A baseline XGBoost model was first compared with linear and tree-based regressors, followed by enhancements through lagged and rolling-window features as well as loss weighting for vulnerable months. To further improve the performance, SHAP analysis was employed to identify the dominant error-contributing features, which guided the construction of targeted month-specific interaction terms for retraining. Experimental results More >

Sobhan Manjili¹, Saeid Jafarzadeh Ghoushchi¹, Mohammad Reza Maghami^2,*, Mazlan Mohamed^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.076217 - 27 April 2026

Abstract Accurate electricity load forecasting is crucial for optimizing power distribution networks, especially in rapidly growing cities like Tabriz (annual consumption growth of 7.2%). This study presents a hybrid AI framework integrating the Temporal Fusion Transformer (TFT) and XGBoost for residual error correction. The model is trained and evaluated using actual consumption data from Tabriz’s distribution network (2021–2023). Compared to a baseline TFT model, the proposed framework demonstrates a 11.2% reduction in RMSE (from 0.1249 to 0.1109) and a 10.7% decrease in MAE (from 0.0998 to 0.0891). Attention mechanism analysis reveals temperature (importance coefficient = 0.32), More >

Yang Li¹, Xianfu Gong¹, Sifan Chen¹, Yi Lei^2,*, Donghui Zhang², Yue Xing²

Energy Engineering, Vol.123, No.4, 2026, DOI:10.32604/ee.2025.072189 - 27 March 2026

Abstract This paper develops an innovative computational model for assessing the Carbon Emission Factor (CEF) of provincial power systems that incorporates inter-provincial electricity transfers and hybrid generation portfolios combining conventional and renewable sources. A key contribution lies in evaluating how deep regulation of thermal power plants influence the carbon intensity of coal-fired generation and coal-fired generation together with high penetration renewables. Furthermore, the study quantitatively analyzes the role of renewable energy consumption and the prospective application of Carbon Capture and Storage (CCS) in reducing system-wide CEF. Based on this framework, the paper proposes phased carbon emission… More > Graphic Abstract