Peramalan Beban Jangka Panjang pada Gardu Induk Bangil dengan Metode Generalized Regression Neural Network by Ali Rizal Chaidir

“…The analysis indicates that Transformer 3 is projected to reach overload by August 2038, with a forecasted peak load of 1407.7465 A. …”
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The Future Burden of Type 2 Diabetes in Belgium: A Microsimulation Model by José L. Peñalvo, Junior Ocira, Diana Sagastume, Maria Salve Vasquez, Stefanie Vandevijvere, Elly Mertens

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Exploring a long short-term memory for mountain flood forecasting based on watershed-internal knowledge graph and large language model. by Songsong Wang, Ouguan Xu

“…Additionally, we have implemented Recurrent Neural Networks (RNN) and Gated Recurrent Units (GRU) for comparative analysis with LSTM. The findings indicate that the LSTM model, when integrated with the watershed-internal KG and LLM, can effectively incorporate critical elements influencing water level changes, the accuracy of the LLM-KG-LSTM model is enhanced by 3% compared to the standard LSTM model, and the LSTM series outperforms both RNN and GRU models, Our method will guide future research from the perspective of focusing on forecasting algorithms to the perspective of focusing on the relationship between multi-dimensional disaster data and algorithm parallelism.…”
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A hybrid Bayesian network-based deep learning approach combining climatic and reliability factors to forecast electric vehicle charging capacity by David Chunhu Li

“…The increasing adoption of electric vehicles (EVs) necessitates advanced predictive models to accurately forecast charging demand and ensure reliable infrastructure planning. …”
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Carbon Dioxide Emission Forecasting Using BiLSTM Network Based on Variational Mode Decomposition and Improved Black-Winged Kite Algorithm by Yueqiao Yang, Shichuang Li, Haijun Liu, Jidong Guo

“…With the growing severity of global climate change, forecasting and managing carbon dioxide (CO₂) emissions has become one of the critical tasks in addressing climate change. …”
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Comparing the Forecast Performance of Advanced Statistical and Machine Learning Techniques Using Huge Big Data: Evidence from Monte Carlo Experiments by Faridoon Khan, Amena Urooj, Saud Ahmed Khan, Abdelaziz Alsubie, Zahra Almaspoor, Sara Muhammadullah

“…In some circumstances under large samples, Autometrics provides a similar forecast as MCP. In the presence of low and moderate autocorrelation, MCP shows outstanding forecasting performance except for the small sample case, whereas E-SCAD produces a remarkable forecast. …”
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FORECASTING FUTURE PUBLIC TRANSPORT MODE CHOICE BEHAVIOUR OF COMMUTERS IN BAHRAIN USING LOGIT AND CLASSIFICATION TREE MODELS: A COMPARATIVE STUDY by Marwa JAZI, Uneb GAZDER, Mudassar ARSALAN, Mohammed Raza MEHDI

“…This paper aims to explore the influential factors concerning travel mode choice in Bahrain and utilize mode choice models to forecast the probable utilities of various future public transport modes. …”
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Estimation and Forecasting of the Average Unit Cost of Energy Supply in a Distribution System Using Multiple Linear Regression and ARIMAX Modeling in Ecuador by Pablo Alejandro Mendez-Santos, Nathalia Alexandra Chacón-Reino, Luis Fernando Guerrero-Vásquez, Jorge Osmani Ordoñez-Ordoñez, Paul Andrés Chasi-Pesantez

“…This study models the average unit cost of electricity supply (USD/kWh) in Ecuador using multiple linear regression techniques and ARIMAX forecasting, based on monthly data from 2018 to 2024. …”
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Forecasting urban expansion: A dynamic urban growth model using DS-ConvLSTM to simulate multi-land regulation scenarios by Juyeong Nam, Changyeon Lee

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Predicting patient visits at the psychiatric polyclinic of a public hospital in Bali, Indonesia: A forecasting approach using single exponential smoothing by Ni Made Ratih Comala Dewi Dewi, Putu Cintariasih, Ni Wayan Suryani, Luh Gde Nita Sri Wahyuningsih

“…Background: Hospital planning requires effective management of resources, facilities, and costs, and accurate patient visit forecasting is integral to this process. Forecasting methods, such as single exponential smoothing, are widely used to predict patient visits and aid in resource allocation. …”
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Quarter-Hourly Power Load Forecasting Based on a Hybrid CNN-BiLSTM-Attention Model with CEEMDAN, K-Means, and VMD by Xiaoyu Liu, Jiangfeng Song, Hai Tao, Peng Wang, Haihua Mo, Wenjie Du

“…Accurate long-term power load forecasting in the grid is crucial for supply–demand balance analysis in new power systems. …”
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Chinese burdens and trends of diabetic retinopathy 1990–2021 and 15 years forecast: results from the Global Burden of Disease Study 2021 by Chun Jiang, Chun Jiang, Xiuhui He, Yingying Zhu, Liming Tao

“…Furthermore, we utilized joinpoint analysis and the Bayesian age-period-cohort model to explore the epidemiological patterns of the disease and forecast its impact for the years 2022 to 2036.ResultsIn 2021, the number of YLDs and prevalence attributed to DR were 86,317 (95% UI: 56,595 to 125,565) and 1.37 million (95% UI: 1.04 to 1.78), respectively. …”
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Forecasting the potential of global marine shipping carbon emission under artificial intelligence based on a novel multivariate discrete grey model by Zirui Zeng, Junwen Xu, Shiwei Zhou, Yufeng Zhao, Yansong Shi

“…Findings – To demonstrate the applicability and robustness of the new model in predicting marine shipping carbon emissions, the new model is used to forecast global marine shipping carbon emissions. Additionally, a comparative analysis is conducted with five other models. …”
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A Novel Optimized Hybrid VMD-PCA-XGBoost Model for Forecasting Precipitation: Exemplified by the Beijing-Tianjin-Hebei Study Region in China by Qiaoli Kong, Qian Li, Qi Bai, Xiaolong Mi, Joseph Awange, Shi Wang, Yi Yang, Guoli Bo

“…These findings underscore the model’s robustness and precision, offering a promising tool for improving precipitation forecasts. This study not only advances the methodological framework for atmospheric forecasting but also provides critical insights for enhancing disaster preparedness and mitigation strategies in climate-sensitive regions.…”
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Interpretable Machine Learning for Multi-Energy Supply Station Revenue Forecasting: A SHAP-Driven Framework to Accelerate Urban Carbon Neutrality by Zhihui Zhao, Minjuan Wang, Jin Wei, Xiao Cen, Shengnan Du, Ziwen Wu, Huanying Liu, Weiqiang Wang

“…The RF model achieved an R² of 0.98, demonstrating superior accuracy in predicting hourly gross transaction values. SHAP analysis further identified consumption volume and transaction frequency as the most influential factors, providing actionable insights for operational optimization. …”
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High-Resolution Spatiotemporal Forecasting with Missing Observations Including an Application to Daily Particulate Matter 2.5 Concentrations in Jakarta Province, Indonesia by I Gede Nyoman Mindra Jaya, Henk Folmer

“…Accurate forecasting of high-resolution particulate matter 2.5 (PM_2.5) levels is essential for the development of public health policy. …”
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ABOUT THE SYSTEM ANALYSIS OF UNEMPLOYMENT OF YOUTH: GENERAL TASKS AND PRIVATE MODELS OF MARKET INDICATORS by Natalia V. Kontsevaya, Irina V. Orlova

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Short-Term Wind Power Prediction Model Based on PSO-CNN-LSTM by Qingquan Lv, Jialin Zhang, Jianmei Zhang, Zhenzhen Zhang, Qiang Zhou, Pengfei Gao, Haozhe Zhang

“…Concurrently, its higher R² value indicates superior alignment between model predictions and the dataset. A comparative analysis of the four models confirms that the PSO-CNN-LSTM framework delivers precise seasonal power generation forecasts with enhanced adaptability and higher prediction accuracy.…”
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Search for oil deposits in YuS2–3 formations of the Southern part of the Priobskoye field based on a seismogeological model using exchroma^SG technology by D. A. Kotunov, S. A. Khalilov, D. V. Romanov, S. A. Lyubimov

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China’s Zero-Coal Power System Future by Yanzhe Ren, Gengfeng Li, Haoyuan Wang, Zhaohong Bie

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