Transformer-Based Models for Probabilistic Time Series Forecasting with Explanatory Variables by Ricardo Caetano, José Manuel Oliveira, Patrícia Ramos

“…Furthermore, probabilistic forecasting enhances decision making by quantifying uncertainty, providing more reliable demand predictions for risk management. These findings underscore the effectiveness of Transformer-based models in retail forecasting and emphasize the importance of integrating domain-specific explanatory variables to achieve more accurate, context-aware predictions in dynamic retail environments.…”
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Design and Motion Simulation of the Drive Mechanism of Oil-immersed Transformer Spherical Detection Robots by Zhou Jiajun, Wu Binghui, Ji Dongmei, Deng Yangtai

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A generalisable agent-based model of residential electricity demand for load forecasting and demand response management by Baxter Lorenzo McIntosh Williams, Daniel Gnoth, R.J. Hooper, J. Geoffrey Chase

“…The model is constructed in MATLAB R2022b with sub-models for appliance use, space heating, and water heating, and validated with real electricity demand profiles from low-voltage distribution transformers in Aotearoa New Zealand and data from appliance use in homes around the country. …”
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Supply–Demand Dynamic Matching in Cloud Manufacturing Based on Hypernetwork Model by Jiawen Zhang, Cheng Wang

“…In response to the escalating demand for personalisation and customisation, the manufacturing industry is increasingly driven towards digital and intelligent transformation. …”
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Forecasting models for Quebec’s lumber demand and exports using multivariate regression technique by Mounia Ferguene, Nadia Lehoux, Camélia Dadouchi

“…A number of methods were applied to estimate the models’ coefficients using a training data set, namely the Ordinary Least Squares method with a “backward” variable selection approach, LASSO and RIDGE regressions, and the Two-Step Least Squares method. …”
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Residential Electricity Demand Modelling: Validation of a Behavioural Agent-Based Approach by Baxter L. M. Williams, R. J. Hooper, Daniel Gnoth, J. G. Chase

“…However, traditional deterministic and stochastic models do not account for the important variability in behavioural-driven residential demand and thus cannot be used to design or optimise DR. …”
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Predictive Model for Short-Term Water Demand Forecasting and Feature Analysis in Urban Networks by Jorge E. Pesantez, Morgan DiCarlo, Fayzul Pasha, Emily Z. Berglund

“…Feature importance analysis underscores the significance of seasonal variables and lagged demand. The IONET model offers prompt training and valuable insights for optimizing WDS management, facilitating the digital transformation of water infrastructure.…”
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Research on sentiment index and real estate demand forecasting based on BERT-BiLSTM and ADL-MIDAS models by Mengkai Chen, Jun Wang, Feilong Zhao, Gaopeng Jiang

“…Simultaneously, Internet Concern index is constructed using Baidu search data as a non-directional sentiment proxy variable. Further adopting the Autoregressive Distributed Lag Mixed Data Sampling model (ADL-MIDAS), we compare the predictive performance of these two sentiment indices alongside macroeconomic variables on market demand. …”
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Monthly Load Forecasting in a Region Experiencing Demand Growth: A Case Study of Texas by Jeong-Hee Hong, Geun-Cheol Lee

“…In response, we propose a regression-based forecasting model that incorporates a carefully designed set of input features, including a nonlinear trend, lagged demand variables, a seasonality-adjusted month variable, average temperature of a representative area, and calendar-based proxies for industrial activity. …”
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An inventory model for deteriorating items with blockchain process: how will it transform inventory management system by Jitendra Kaushik

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Enhancing employees’ workplace well-being through workplace digitization: exploring the mediating roles of job demands and digital anxiety by Zhiyong Han, Guoqing Song, Yanlong Zhang, Lei Yan, Lei Yan

“…Consequently, investigating the mechanisms for improving workplace well-being during this transition has emerged as a critical area of inquiry in both scholarly and practical realms.MethodsBased on the Job Demands-Resources (JD-R) model, job demands and digital anxiety are introduced as mediating variables, and a dual-mediation model is constructed to explore the specific mechanism through which workplace digitization affects workplace well-being. …”
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Digital Transformation for Sustainability in Industry 4.0: Alleviating the Corporate Digital Divide and Enhancing Supply Chain Collaboration by Qi Li, Weijian Tian, Hua Zhang

“…The research utilizes firm-level data from the Chinese stock market and accounting research databases and performs robustness checks, including methods such as the instrumental variable approach and the Heckman two-stage model, to ensure the validity of the findings. (3) Results: The study finds that the corporate digital divide exacerbates imbalances in both upstream and downstream chains. …”
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Sizing and Characterization of Load Curves of Distribution Transformers Using Clustering and Predictive Machine Learning Models by Pedro Torres-Bermeo, Kevin López-Eugenio, Carolina Del-Valle-Soto, Guillermo Palacios-Navarro, José Varela-Aldás

“…The efficient sizing and characterization of the load curves of distribution transformers are crucial challenges for electric utilities, especially given the increasing variability of demand, driven by emerging loads such as electric vehicles. …”
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Evaluating the performance of regional climate models to simulate seasonal variability of precipitation in Abbay and Awash river basins, Ethiopia by Kassa Abera Tareke

“…Abstract Projected climate data has vital importance in analyzing the seasonal and annual variability of precipitation. This study was conducted based on regional climate model (RCM) performance evaluation in the Abbay and Awash river basins, Ethiopia. …”
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Metro Timetabling for Time-Varying Passenger Demand and Congestion at Stations by Keping Li, Hangfei Huang, Paul Schonfeld

“…Time-varying passenger demand and train capacity are considered in a nonsmooth, nonconvex programming model, which is transformed into a mixed integer programming model with a discrete time-space graph (DTSG). …”
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Semi-analytical approach for solving the mathematical model of solid-phase diffusion in electrodes: An application of modified differential transform method by Shivaranjini S, Neetu Srivastava

“…The problem considered is based on Fick's second law and is represented as a partial differential equation (PDE). The modelled PDE is converted to its dimensionless form using suitable dimensionless variables. …”
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Transforming Building Energy Management: Sparse, Interpretable, and Transparent Hybrid Machine Learning for Probabilistic Classification and Predictive Energy Modelling by Yiping Meng, Yiming Sun, Sergio Rodriguez, Binxia Xue

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Optimal control of production inventory systems with inventory-level-dependent demand by LIU Ming, DONG Shao-hua, LI Xian-feng, LIU Cai-yun, PANG Hui

“…In this model, the production rate and price level are control variables, the production rate is bounded, the inventory level is state variables, and the demand rate depends on the inventory level and price level. …”
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Shale Gas Transition in China: Evidence Based on System Dynamics Model for Production Prediction by Yingchao Chen, Yang Zhang

“…This study employs a system dynamics model to forecast future production trends in shale gas in China, analyze its implications for the natural gas supply–demand structure, and explore pathways for sustainable development. …”
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Markov approach for inventory control with meta-heuristics in intermittent demand environment. by Ferhat Yuna, Burak Erkayman, Mustafa Yılmaz

“…The proposed approach contributes to inventory management by minimizing the negativities caused by demand variability through the Markov process. A mathematical model has been proposed for stock level optimization, but no feasible solution has been found. …”
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