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Joint Prediction of Wind-PV-Load-Price Based on Time-Conditioned Dynamic Soft Decoupling and Discrete State Space Physical Constraints

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Outgoing claims: the papers this work cites, builds on, or argues with.

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MTTLA‐DLW: Multi‐task TCN‐Bi‐LSTM transfer learning approach with dynamic loss weights based on feature correlations of the training samples for short‐term wind power prediction

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Probabilistic load forecasting for buildings considering weather forecasting uncertainty and uncertain peak load

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Real-time electricity price forecasting of wind farms with deep neural network transfer learning and hybrid datasets

inferredcites →https://doi.org/10.1016/j.enconman.2013.01.002openalex-snapshot

Development and validation of a simplified online cooling load prediction strategy for a super high-rise building in Hong Kong

inferredcites →https://doi.org/10.1016/j.eneco.2021.105273openalex-snapshot

Enhancing load, wind and solar generation for day-ahead forecasting of electricity prices

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Electricity price forecasting with high penetration of renewable energy using attention-based LSTM network trained by crisscross optimization

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Short-term integrated forecasting method for wind power, solar power, and system load based on variable attention mechanism and multi-task learning

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Accurate multi-step wind and solar power forecasting based on multi-scale convolutional Kolmogorov-Arnold network and improved Lemming-optimized attention fusion

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The role of probabilistic load and renewable prediction in enhancing day-ahead electricity price forecasts

inferredcites →https://doi.org/10.1038/s42005-025-02329-1openalex-snapshot

Physics-consistent machine learning with output projection onto physical manifolds

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A climatology of weather-driven anomalies in European photovoltaic and wind power production

inferredcites →https://doi.org/10.1049/esi2.70048openalex-snapshot

A Multiscale Lightweight Deep Learning Approach for Short‐Term Multi‐Energy Load Forecasting in Integrated Energy Systems

inferredcites →https://doi.org/10.1049/iet-rpg.2019.0614openalex-snapshot

Short‐term wind power forecasting based on two‐stage attention mechanism

inferredcites →https://doi.org/10.1063/5.0292276openalex-snapshot

KAPNet: A physics-informed neural network with physical constraints modeled by Kolmogorov–Arnold network for wind speed prediction

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Overcoming the integration bottleneck: a global review of renewable energy and grid adaptation strategies

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SL-ANFIS-LSTM: A Structure Learnable Fuzzy Neural Network for Ultra-Short-Term PV Power Forecasting

inferredcites →https://doi.org/10.4028/b-xdtcg5openalex-snapshot

12th Annual International Conference on Material Science and Engineering (12th ICMSE)

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MIPV-NWP-PINNs V1.0: development of a multi-scale photovoltaic power forecasting framework integrating numerical weather prediction with physics-informed neural networks

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