This study proposes an energy consumption prediction method based on Reinforcement learning and Generative Adversarial Networks (Re-GANs). The algorithm constructs the generator and discriminator in Generative?Adversarial?Nets (GANs) into the Agent and reward function in reinforcement learning respectively. In the training process, the current real energy consumption sequence is taken as the input state of the Agent (generator), and a set of generation sequences with a fixed length is constructed. Combined with the discriminator and Monte-Carlo search method, the reward function of the current sequence is further constructed as a reward for the first subsequent energy consumption value of the real sample sequence. On this basis, the objective function of reward is constructed, and the optimal parameters are solved. Finally, the proposed algorithm is used to predict the public building energy consumption data of the Downing Street complex. The experimental results show that the proposed algorithm has higher prediction accuracy than the multi-layer perception machine, gated loop neural network, and convolution neural network.