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Power Load Probabilistic Prediction Based on Multi-Value Quantile Regression and Timing Fusion Ensemble Learning Model

Yuhang Liu, Fei Mei, Jun Zhang, Xiang Dai et al. • Entropy (2026)

The core component to ensure the refined and safe operation of distribution network scheduling is 10 kV bus load probabilistic prediction. However, existing probabilistic prediction methods suffer from insufficient dynamic feature extractio...

DOAJ Open Access (DOAJ) DOI: 10.3390/e28030329

Decentralized intelligence in smart grids: a comprehensive survey of edge computing and federated learning

Pijush Kanti Dutta Pramanik, Bijoy Kumar Upadhyaya, Ajay Kushwaha, Debashish Bhowmik • Energy and AI (2026)

Modern power grids are becoming more distributed and data-intensive, while centralized, cloud-based analytics struggle to keep pace. Routing everything through a central system introduces delays, strains network capacity, complicates compli...

DOAJ Open Access (DOAJ) DOI: 10.1016/j.egyai.2026.100925

Day-Ahead Electric Load Forecasting for the Residential Building with a Small-Size Dataset Based on a Self-Organizing Map and a Stacking Ensemble Learning Method

Jaehyun Lee, Jinho Kim, Woong Ko • Applied Sciences (2019)

Electric load forecasting for buildings is important as it assists building managers or system operators to plan energy usage and strategize accordingly. Recent increases in the adoption of advanced metering infrastructure (AMI) have made b...

DOAJ Open Access (DOAJ) DOI: 10.3390/app9061231

Hybrid Model Based on an SD Selection, CEEMDAN, and Deep Learning for Short-Term Load Forecasting of an Electric Vehicle Fleet

Ahmad Mohsenimanesh, Evgueniy Entchev, Filip Bosnjak • Applied Sciences (2022)

Forecasting the aggregate charging load of a fleet of electric vehicles (EVs) plays an important role in the energy management of the future power system. Therefore, accurate charging load forecasting is necessary for reliable and efficient...

DOAJ Open Access (DOAJ) DOI: 10.3390/app12189288