Accurate medium-term load forecasting (MTLF) is critical for capacity planning, maintenance scheduling, and intelligent resource allocation in modern power systems. However, existing approaches are typically designed for fixed-horizon, contiguous prediction and often suffer from multistep error accumulation, limited spatiotemporal modeling capacity, and insufficient uncertainty quantification. This article proposes DiffFlex, a diffusion-based probabilistic forecasting framework that reformulates MTLF as a temporal outpainting problem. By decoupling forecasting from temporal contiguity, DiffFlex enables direct parallel prediction over arbitrary, noncontiguous future intervals without cumulative error propagation. The framework integrates fused historical blocks to capture cyclical consumption patterns, models spatiotemporal-interval dependencies across multivariate load series, and employs an interval-aware diffusion module to generate full predictive distributions. Experiments on the Independent System Operator New England, New York Independent System Operator, and European Network of Transmission System Operators for Electricity datasets across 1- to 12-week horizons show that DiffFlex outperforms state-of-the-art baselines in both point and probabilistic accuracy, achieving average mean absolute percentage errors of 8.88%, 9.71%, and 4.86%, respectively. These results indicate that DiffFlex can serve as a practical tool for scenario-based planning and risk-informed decision-making in power system operations.

Accurate carbon price forecasting is critical for the effectiveness of emissions trading, as it enables informed financial decision-making, risk management, and investment strategies while advancing global efforts to mitigate climate change. However, the inherently non-stationary and nonlinear nature of carbon prices creates substantial challenges for traditional statistical models, which often require extensive manual feature engineering when combined with machine learning. This study introduces a novel adaptive end-to-end transformer-based deep learning model that enhances both the accuracy and robustness of carbon price forecasts. The model employs a spatio-temporal attention mechanism to capture time-dependent and time-independent influences, thereby improving feature extraction. It further integrates a self-decomposition mechanism that enables adaptive preprocessing without reliance on external decomposition methods. To optimize performance, particle swarm optimization is applied for hyperparameter tuning, improving both efficiency and convergence. Empirical analyses using carbon price data from China’s Emissions Trading System (ETS) and the European Union ETS show that the proposed method consistently outperforms state-of-the-art forecasting models in both daily and multi-step prediction tasks. In daily forecasting, the model achieves mean absolute percentage errors of 3.26% for Hubei ETS, 3.71% for Guangdong ETS, 4.83% for Beijing ETS, and 1.73% for the EU ETS. These results underscore the method’s strong cross-market and cross-asset generalization, establishing it as a reliable tool for policymakers, investors, and carbon market participants.

Optical fiber allocation in cable manufacturing is a core process for reducing material costs, improving production efficiency, and optimizing inventory management. However, factors such as variations in optical fiber lengths and storage shelf-life significantly increase the complexity of the problem, making traditional heuristic algorithms difficult to apply. To address this challenge, this paper proposes a modified proximal policy optimization (MPPO) algorithm. First, a Markov decision process (MDP) is constructed for optical fiber allocation, designing dynamic state and action spaces along with the corresponding reward function. Second, an exponential decay function is introduced to design an adaptive clipping coefficient, thereby improving the constraint on the divergence between the new and old policies in the proximal policy optimization (PPO) algorithm. Finally, the network model of the PPO algorithm is enhanced by adding hidden layers and incorporating a previous actor network, thereby improving the agent’s flexibility and perceptual capabilities. The results show that the proposed method exhibits superior performance and stability, highlighting its potential in optical fiber allocation.

Natural gas is vital in the world’s energy portfolio and is widely applied to power generation, urban heating, and manufacturing. Forecasting natural gas consumption with high accuracy is thus crucial in order to maintain a reliable supply for various applications. The demand for natural gas often exhibits different seasonal patterns regarding customers of different characteristics. The precision of forecasters will be vulnerably affected without carefully exploring the periodicity of usage. This paper proposes a novel method, Dual Convolution with Seasonal Decomposition Network, for natural gas consumption forecasting. The proposed method applies multiple seasonal-trend decomposition to separate time series into periodic patterns and residual components. In addition, local and global convolution are combined to predict series with significant fluctuations and poor periodicity. Simulations show that on city-level forecasting, the proposed method outperforms state-of-the-art methods in terms of overall prediction accuracy and variation sensitivity regardless of different time intervals. The performance of the method is robust to the forecasting horizon. The method can be deployed in practical circumstances to forecast the natural gas consumption of residential quarters, cities, or even countries in different time spans.

The Industrial Internet of Things (IIoT) is characterized by digitalization, networking, and smartness, which opens a world of the interconnection of all things and makes mass personalization possible. As a result, traditional industrial firms are forced to change their operation mode from the manufacture-oriented one to the manufacture-and-service-oriented type. In particular, furniture production is a typical domain featured by mass personalization from networking, where order selection (service aspect) and board cutting (manufacture aspect) are mainly concerned. We formulate customized furniture production as a multi-objective optimization problem and propose two algorithms to solve it, i.e., an integrated algorithm and a two-stage decoupling algorithm. As a secondary, the robust mixed-integer linear optimization algorithm is proposed to deal with the uncertainty such as fluctuations in production capacity and raw materials cost. The numerical experiments using real industrial data demonstrate that the proposed algorithms effectively improve firms’ operational efficiency by achieving a balance between service and manufacture. Moreover, they present remarkable performance under various circumstances. The developed methods could apply to a wide range of mass personalization for related manufacturing scenarios of IIoT with digital servitization, including computer, communication, and consumer electronics products (3C products) machining, automobile accessory production, and chip manufacturing.

Short-term electricity load forecasting has become increasingly important due to the privatization and deregulation in the energy market. This study proposes a probabilistic learning method to predict hourahead and day-ahead load demand. Unlike methods in previous studies, the proposed method integrates wavelet transform and feature selection as key preprocessing steps. Features are divided into current state related features and historical information related features. Current state related features are forecasted by the regression model before being added into the load prediction model. The entire learning and prediction process is based on the relevance vector machine (RVM) that utilizes load data characteristics. A number of test cases are presented using benchmark datasets from the New York Independent System Operator (NYISO) and ISO New England. Based on the detailed empirical comparison, the proposed RVM-based integrated method outperforms classical time series approaches and state-of-the-art artificial intelligence methods on short-term load forecasting. (C) 2020 Elsevier B.V. All rights reserved.

Background: Electrocardiography (ECG) is a fundamental diagnostic tool, frequently used in clinical practice, for testing heart conditions. Recently, computer-aided ECG interpretation has become more widely investigated. However, researches on concurrent ECG diagnosis for multiple heart abnormalities are limited and therefore a market-applicable multi-label automatic diagnosis framework that covers a wide range of arrhythmias, with better-than-human accuracy in diagnosing ECGs with multi-labels, has not yet been developed. We therefore aimed to engineer a deep learning approach for the automated multi-label diagnosis of impulse or conduction abnormalities by realtime ECG analysis. Methods: We utilized a dataset with 70,692 patients, consisting of adults with age ≥18 years. 180112 ECGs (standard 10-second and 12-lead format) with 21 distinct rhythm classes, including various impulse or conduction abnormalities, were used for the diagnosis of arrhythmias in multi-label and single-label level. Base rhythm labels were assessed by trained personnel under the supervision of cardiologists. We ensured that each abnormality had a sample size of at least 4,000 ECGs, except idioventricular rhythm (1,780 ECGs due to the rarity of said disease), for compiling a balanced dataset. We allocated ECGs to a training or a validation dataset for the multi-label diagnostic model training, and evaluated model performance on a test dataset by calculating the area under the curve (AUC) of the receiver operating characteristic (ROC) curve, as well as quantifying accuracy, sensitivity, specificity, and the F1 score. The test dataset was annotated by the consensus of a committee of board-certified actively-practicing cardiologists. Validation of results for the test dataset were compared to diagnoses made by 53 ECG cardiologists who have a wide range of ECG interpretation experience (range=0-12+ years). Findings: We demonstrated that in multi-label level, our convolutional neural network (CNN) approach toward diagnosing heart abnormalities resulted in AUC scores above 0.913 for most of the diseases, with an average score of 0.985 (95% confidence interval (CI) 0.988-0.994). The model obtained an average F1 score of 0.890 (95% CI0.877- 0.903), sensitivity of 0.869 (95% CI 0.851-0.887), specificity of 0.995 (95% CI 0.994- 0.996). The average F1 score computed from our deep learning model exceeded the score from human cardiologists regardless of ECG interpretation experience (0 < Y ≤ 6 years: 0.788, 6 < Y ≤ 12 years: 0.815, Y > 12 years: 0.829). The CNN outputs also showed identical or increased performance with respect to average sensitivity and accuracy as compared to the cardiologists. In single-label level, the CNN achieved an average F1 of 0.918, higher than the average level of the cardiologists 0.798. Interpretation: Our automatic ECG diagnosis system is better than human cardiologists to classify particular waveforms into clinically defined categories accurately, to distinguish a wide range of distinct arrhythmias, and to apply both single label and multiple label ECGs. Our study is therefore a promising foundation for the deployment of computational decision-support systems in practical clinical applications.Funding Statement: Project supported by the Major International (Regional) Joint Research Project, the Ministry of Management Science, the National Natural Science Foundation of China (Grant No. 71520107003).Declaration of Interests: Dr. Yuan reports grants from National Natural Science Foundation of China, during the conduct of the study; In addition, Dr. Yuan has a patent An automatic ECG classification method, system and equipment based on deep learning algorithm pending to Ye Yuan, Xiaoyun Yang, Hongling Zhu, Yiran Wang, Cheng Cheng, Xingyi Li, Hang Yin, Jingyi Wang. There are no other conflicts of interest.Ethics Approval Statement: To protect patients’ privacy, we used anonymized data as to prevent breaches. Accordingly, a written informed consent was not required for this study, since the ECG samples were appropriately anonymized and de-identified, as according to the Health Insurance Portability and Accountability Act Safe Harbor provision. The study design was evaluated and exempted from full review by the Huazhong University of Science and Technology Institutional Review Board.