AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
Recently, the integration of Internet of Things (IoT) into buildings has sparked a technological revolution known as the Internet of Building Things (IoBT). Such new paradigm connects devices, sensors, and systems for intelligent control. Particularly, IoBT has advanced energy forecasting and enabled real-time data ana...
A novel transformer-based wide and deep convolutional neural network (TWiDeCNN) is proposed to efficiently identify electric energy theft in a scenario based on SGs, demonstrating its stability and effectiveness for electric energy theft detection.
Accurate prediction of chiller energy consumption is crucial for the efficient operation and intelligent management of heating, ventilation, and air conditioning (HVAC) systems in large buildings. However, such prediction remains challenging due to the multi-scale temporal coupling and nonstationary dynamics of chiller...
A three-layer “Aggregator-Edge-End user” architecture leveraging a data-driven workflow and a hybrid deep learning model combining a Temporal Convolutional Network (TCN) and a Bidirectional Long Short-Term Memory (BiLSTM) network is introduced.
A framework for a comparative evaluation of four representative forecasting methods: the Seasonal Autoregressive Integrated Moving Average (SARIMA) model, Extreme Gradient Boosting, the Long Short-Term Memory (LSTM) neural network, and a hybrid Variational Mode Decomposition–LSTM (VMD-LSTM) model is proposed.
A new hierarchical forecasting structure denoted as INRBO-SSA-LSTM, which significantly outperforms traditional baseline models across all indicators and effectively accommodates temporal demand variations, offering a robust foundation for the advancement of intelligent power management technology.
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