Indoor temperature prediction of multiple zones in near future horizons is vital in developing an optimal regulation strategy of heating, ventilation, and air conditioning systems in large-scale complex buildings. This is, however, challenging due to the spatial–temporal correlation and multivariable coupling characteristics. This article proposes a novel deep learning framework incorporating the distributed long short-term memory and graph convolution network namely DL-GCN for indoor temperature prediction in large public buildings, aiming to learn the spatial–temporal correlation and multivariable coupling features. First, the indoor temperature and humidity data from different zones are handled by GCN networks to extract the temperature spatial features. Then, in the distributed LSTM module, other data, such as light and ac power consumption, are fused with the outputs of the GCN module, respectively, in a distributed way to learn the coupling interactions and temporal characteristics between these variables. Comparison study and ablation experiments are conducted using real datasets from a large-scale building to verify its effectiveness and superior performance in multizone indoor temperature prediction.

A proton exchange membrane fuel cell (PEMFC) has great application prospects due to its low emission and high efficiency. An accurate model to predict the dynamic output voltage is essential for the optimal control of the PEMFC for the applications on vehicles and power stations. In this article, a novel deep learning framework with the long short-term memory (LSTM) and artificial neural network (ANN) fusion is proposed to develop the PEMFC dynamic model by extracting both the historical and current information. The LSTM extracts the temporal information from the past PEMFC states with its order determined with autocorrelation and partial autocorrelation functions, while the influence of the system current inputs is learnt by the ANN. Then, the outputs of LSTM and ANN are concatenated with the multiple information fused to predict the PEMFC dynamic output voltage. After validated by the operating data from a laboratory-scale PEMFC system, the LSTM and ANN fusion model is compared with the existing models, such as support vector regression, ANN, and LSTM methods. The comparison results show that the proposed LSTM and ANN fusion model can provide the best prediction performance with the lowest mean square error of 1.303. The proposed LSTM and ANN fusion model can be helpful to develop the optimal control strategy of the PEMFC.