在对建筑能耗进行回归预测时需要利用到时序特征与分类特征，而传统模型只能处理其中一种特征。针对该问题，文中提出了一种融合一维卷积与词嵌入的神经网络新构架，其中，一维卷积核能提取连续的时间序列特征，词嵌入模型能对离散的分类特征进行嵌入计算，从而建立能同时处理时序特征与分类特征的建筑能耗预测模型。通过与梯度提升决策回归树和长短时记忆网络的比较，证明所提出的模型在效率与准确率上都有良好的表现。在超参数调节上，采用基于贝叶斯优化的超参数自动优化算法，该算法能在树搜索空间上寻找最优超参数，相比于人工调参，超参数自动寻优算法能在较快的时间内提升模型本身的性能。最后进行了算例仿真，结果表明，文中提出的模型在性能上要优于集成学习模型与长短时记忆网络。

Building energy consumption prediction needs both time series features and categorical features, but traditional models can only deal with one of the features. Aiming at this problem, a new neural network integrating one-dimensional convolutional kernel and word embedding was proposed in this paper. The one-dimensional convolutional kernel can extract the continuous time series features, and the word embedding model can embed the discrete categorical features, based on which a building energy consumption prediction model is established that can simultaneously process both time series features and categorical features. By comparing with the gradient boosting decision regression tree and the long short time memory network, it is proved that the proposed model has good performance in efficiency and accuracy. In terms of hyperparameter adjustment, the automatic hyperparameter optimization algorithm based on Bayesian optimization was adopted, and the algorithm can find the optimal hyperparameter in the search space. Compared with manual optimization, the automatic hyperparameter optimization algorithm can improve the perfor-mance of the model in a relatively short time. Finally, simulation studies were conducted and the results demonstrate that the proposed model is better in performance than the ensemble learning model and the long short-time memory network.