Energy efficiency

Energy management

Hybrid CNN-LSTM Model Achieves Superior Performance in Chiller Power Consumption Forecasting

Researchers at the University of Malaysia Pahang have developed a novel approach to forecasting chiller power consumption in commercial buildings, using a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model optimized by the Barnacles Mating Optimizer (BMO). This innovative method has demonstrated superior performance in optimizing building energy management systems