AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
A novel hybrid heat load prediction model for district heating systems (DHS), integrating convolutional neural networks (CNN), attention mechanisms (ATT), and bidirectional long short-term memory (BiLSTM) networks is proposed, aiming to enhance forecasting accuracy and reliability.
Reliable short-term prediction of global horizontal irradiance (GHI) is essential for maximizing photovoltaic (PV) power generation, improving grid reliability, and enabling efficient integration of renewable energy sources. This study proposes Lite-BiAttn, a lightweight hybrid deep learning framework that combines one...
The proposed CNN–BiLSTM–Attention framework performed better than conventional benchmark models of machine learning and deep learning, achieving a Root Mean Square Error of 0.088 and Seasonal and atmospheric-condition evaluations also showed consistent performance across different seasonal and atmospheric conditions.
A purely data-driven end-to-end SOC prediction framework based on sliding-window technology that adopts an SCSSA-optimized convolutional neural networks-long short-term memory-attention hybrid model that integrates a CNN for local feature extraction, an LSTM for modeling temporal dependencies, and an attention mechanis...
The availability of solar energy in Indonesia is hindered by small-scale variations in solar radiation that occur in tropical climates. For maintaining grid stability and for ensuring the efficient operation of solar power systems, accurate short-term prediction is necessary. In this study, a hybrid Convolutional Neura...
A hybrid model combining TCN and NLSTM to leverage the strengths of both architectures is proposed, achieving up to a 12.7% reduction in Root Mean Square Error (RMSE) and a mutation-inspired modification of the Driving Training-Based Optimization algorithm dynamically tunes the model’s hyperparameters.
The increasing penetration of solar energy into power systems poses significant challenges to grid dispatch and operation due to its inherent variability and strong dependence on meteorological conditions. Recently, Transformer-based deep learning models have demonstrated great potential in time-series analysis owing t...
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