#energy-forecasting News & Analysis

4 articles tagged with #energy-forecasting. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

4 articles

AINeutralarXiv – CS AI · Jun 26/10

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EnergyMamba: An Uncertainty-Aware Graph-Enhanced Selective State Space Model for Energy Consumption Prediction

Researchers introduce EnergyMamba, a machine learning framework that combines graph neural networks with state-space models to predict energy consumption while quantifying prediction uncertainty. The system achieves 5% accuracy improvement over existing methods by simultaneously modeling spatial grid relationships and temporal patterns, with enhanced reliability during abnormal conditions like extreme weather.

AIBullisharXiv – CS AI · May 296/10

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Uncertainty-Aware Transfer Learning for Cross-Building Energy Forecasting: Toward Robust and Scalable District-Level Energy Management

Researchers developed an uncertainty-aware transfer learning framework using Temporal Fusion Transformers to enable energy forecasting models trained on one building to work effectively on different buildings with minimal retraining. The approach achieved 93.2% prediction interval coverage and demonstrated that freezing most model parameters while fine-tuning only output layers produces superior cross-building generalization compared to full model retraining.

AIBullisharXiv – CS AI · Mar 36/108

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A Deep Learning Framework for Heat Demand Forecasting using Time-Frequency Representations of Decomposed Features

Researchers developed a deep learning framework using Continuous Wavelet Transform and CNNs for heat demand forecasting in district heating systems. The model achieved 36-43% reduction in forecasting errors compared to existing methods, reaching up to 95% accuracy in predicting day-ahead heat demand across multiple European cities.

AIBullisharXiv – CS AI · Mar 36/1011

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FreeGNN: Continual Source-Free Graph Neural Network Adaptation for Renewable Energy Forecasting

Researchers developed FreeGNN, a continual source-free graph neural network framework for renewable energy forecasting that adapts to new sites without requiring source data or target labels. The system uses a teacher-student strategy with memory replay and achieved strong performance across three real-world datasets including GEFCom2012, Solar PV, and Wind SCADA.