This study aims to achieve higher accuracy for energy efficiency forecasting tasks, using an improved model based on LSTNet.

This study aims to achieve higher accuracy for energy efficiency forecasting tasks, using an improved model based on LSTNet, and finds significant ...

With the development of artificial intelligence techniques, deep neural network-based tem- poral prediction models have made important advances in many.

This study develops an energy consumption prediction model based on a deep belief network, which is constructed according to the principles of a restricted ...

Compared with the Back Propagation Neural Network (BPNN) and the Long Short Temp Memory (LSTM) network, it is found that DRN has the highest prediction accuracy ...

Jul 19, 2024 · This paper focused on the energy consumption of heating, ventilation and air conditioning (HVAC) systems operating under various modes across different seasons.

This paper proposes a predictive modeling technique to effectively forecast HVAC system parameters using machine and deep learning models.

This study presents a hybrid deep neural network (DNN) model that combines both a basic DNN model and a thermodynamic model to counter the abovementioned ...

Our objective is to develop and evaluate a model to forecast the daily energy consumption of heating, ventilating, and air conditioning systems in buildings.

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