Search Results - "Forecasting methodology"

Short-Term Hydropower Generation Forecasting for Operational Planning and Early Energy Procurement: Multi-Model Evidence from Kazakhstan.

Authors: Rakhimzhanova, Altynshash Zhakiyev, Nurkhat Nugumanova, Aliya

Source: Energies (19961073). Jun2026, Vol. 19 Issue 11, p2520. 33p.

Subject Terms: *Hydroelectric power plants, *Forecasting methodology, *Hydrological databases, *Load dispatching in electric power systems

Geographic Terms: Kazakhstan

Precipitation Forecasting for Hydrologic Modeling in West-Central Florida using Seasonal Climate Outlooks.

Authors: Shrestha, Manoj Wang, Hui Geurink, Jeffrey S. et al.

Source: Hydrology & Earth System Sciences. 2026, Vol. 30 Issue 9, p2703-2716. 14p.

Subject Terms: *Precipitation forecasting, *Forecasting methodology, *Hydrologic models, *Long-range weather forecasting, *Hydrological forecasting, *Water management, El Niño

Geographic Terms: Florida

Company/Entity: United States. National Oceanic & Atmospheric Administration

Advances in Similar Day Methods for Short-Term Load Forecasting for Power Systems.

Authors: Borunda, Monica Conde-López, Luis Ruiz-Chavarría, Gerardo et al.

Source: Forecasting. Apr2026, Vol. 8 Issue 2, p32. 50p.

Scenario-Based Forecasts: Using Narratives and Human Judgment to Derive Expected Return, Volatility, Asymmetry, and Fat Tails.

Authors: Wynne, Bill¹ (AUTHOR) bwynne@poppertech.com

Source: Journal of Wealth Management. Summer2026, Vol. 29 Issue 1, p7-20. 14p.

E-Backtesting Expected Shortfall: What Defines a "Good" Forecasting Method for Chinese Regulators?

Authors: Zhao, Weihua¹ (AUTHOR)

Source: Risks. May2026, Vol. 14 Issue 5, p110. 25p.

Reference class forecasting: promises, problems, and a research agenda moving forward.

Authors: Cantarelli, Chantal C. Davis, Kate Pinto, Jeffrey K. et al.

Source: Production Planning & Control. May2026, Vol. 37 Issue 7, p691-709. 19p.

A Risk and Opportunity Driven Forecasting Framework for Faster, High-Confidence Executive Decision-Making.

Authors: Mehta, Jadvish¹

Source: Strategic Finance. May2026, p1-7. 7p.

MixNet: A scale-adaptive method for multivariate time series forecasting.

Authors: Wang, Xinhan Zhao, Bowen

Source: PLoS ONE. 5/26/2026, Vol. 21 Issue 5, p1-12. 12p.

An intelligent prediction method for blasting craters of cylindrical blastholes based on Wavelet-Augmented Physics-Informed Neural Networks.

Authors: Zhu, Ting Zhang, Hongyu Jia, Yongsheng et al.

Source: AIMS Mathematics. 2026, Vol. 11 Issue 5, p1-38. 38p.

A comparison of realized beta forecasting methods.

Authors: Zhou, Jian¹ (AUTHOR) jian@uoguelph.ca

Source: Applied Economics. Feb2026, p1-12. 12p. 1 Illustration.

Synergies strategies for cost contingency: uniting reference class forecasting and probabilistic risk analysis in infrastructure project delivery.

Authors: Sing, Michael C. P. Kazeem, Kayode O. Chan, Albert P. C. et al.

Source: Production Planning & Control. Feb2026, p1-16. 16p. 6 Illustrations.

A comprehensive review on traditional and cutting-edge approaches for wind speed/power forecasting.

Authors: AHUJA, Muskaan SAINI, Sanju

Source: Sigma: Journal of Engineering & Natural Sciences / Mühendislik ve Fen Bilimleri Dergisi. Feb2026, Vol. 44 Issue 1, p637-662. 26p.

Subjects: Wind forecasting, Forecasting methodology, Deep learning, Numerical weather forecasting, Energy consumption, Ensemble learning, Machine learning

A Maximum Wind Gust Forecast Method Based on Combination of Traditional Statistics and Machine Learning.

Authors: Hu, Haichuan Dai, Kan Zhu, Yuejian et al.

Source: Weather & Forecasting. Mar2026, Vol. 41 Issue 3, p505-517. 13p.

Subjects: Machine learning, Forecasting methodology, Windstorms, Meteorological databases, Weather forecasting, European Centre for Medium-Range Weather Forecasts (Organization), Mathematical statistics, Wind forecasting

Geographic Terms: China

CASHFLOW LESSONS FROM THE BIG END OF TOWN.

Authors: GRAY, ANNIE

Source: NZ Business (0113-4957). Mar2026, p22-25. 4p.

Entropy-Guided Regime Switching for Railway Passenger Flow Forecasting: An Adaptive EA-ARIMA-Informer Framework.

Authors: Tan, Silun Shan, Xinghua Wei, Zhengzheng et al.

Source: Entropy. Feb2026, Vol. 28 Issue 2, p182. 28p.

Hybrid Forecast-Enabled Adaptive Crowbar Coordination for LVRT Enhancement in DFIG Wind Turbines.

Authors: Su, Xianlong Kim, Hankil Kim, Changsu et al.

Source: Entropy. Feb2026, Vol. 28 Issue 2, p138. 27p.

Exploring predictive models to improve the accuracy of Housing Price Index forecasts in India's real estate sector.

Authors: Singh, Nutan Shanmugam, Kavitha

Source: PLoS ONE. 1/23/2026, Vol. 21 Issue 1, p1-14. 14p.

A Critical Analytical Review of Hybrid Grey Models and Their Applications in Forecasting Non-Stationary Time Series.

Authors: Naji, Ali Abdul Karim Kazem¹ ali.babylon1982@gmail.com

Source: Journal of Qadisiyah Computer Science & Mathematics. 2026, Vol. 18 Issue 1, p1-16. 16p.

Trend Prediction of Valve Internal Leakage in Thermal Power Plants Based on Improved ARIMA-GARCH.

Authors: Hou, Ruichun Cong, Lin Li, Kaiyong et al.

Source: Energies (19961073). Dec2025, Vol. 18 Issue 23, p6275. 29p.

Subjects: Time series analysis, Forecasting methodology, Trend analysis, Steam power plants, Robust statistics, Condition-based maintenance, Heteroscedasticity

Research on New Energy Power Generation Forecasting Method Based on Bi-LSTM and Transformer.

Authors: He, Hao He, Wei Guo, Jun et al.

Source: Energies (19961073). Oct2025, Vol. 18 Issue 19, p5165. 25p.

Subjects: Forecasting methodology, Deep learning, Long short-term memory, Renewable energy sources, Transformer models, Wind power plants, Electric power production, Photovoltaic power systems