In recent years, building energy consumption prediction gained a lot of research attention due to its importance in energy efficiency-related decision making. With the advancements in data analytics and machine learning, there has been numerous studies on developing data-driven building energy consumption prediction models based on support vector machines (SVM), artificial neural networks (ANN), and other statistical regression algorithms. These studies showed that each algorithm has its own advantages and disadvantages for different cases and that, therefore, the algorithms should be selected based on the specific application. However, none of the existing research efforts tested the effectiveness of deep learning - which is shown to outperform other machine learning algorithms in many other fields -in building energy consumption prediction. To address this gap, this paper (1) presents a deep learning-based model to predict cooling energy consumption of a building based on outdoor weather conditions (e.g., outdoor temperature), and (2) compares the prediction performance and computational efficiency of the deep learning-based model against other machine learning and statistical regression-based benchmark models. In order to generate a labelled dataset for training the models, a building was modelled and simulated by EnergyPlus in five locations. The models - the deep learning model as well as the other benchmark models - were trained using the simulation-generated data and the performance was evaluated in terms of accuracy and computational efficiency. The testing results showed that deep learning can be successfully applied to the field of building energy consumption prediction.