Abstract: Demand response (DR) for the home energy system (HES) can effectively promote energy conservation and emission reduction. Uncertainties of HESs require that the DR optimization strategy should be adjusted automatically and quickly. This paper investigates knowledge integration in reinforcement learning for DR optimization. First, an optimization framework of knowledge and reinforcement learning is established to form a complementary mechanism. Then, the home device models and their rule-based DR optimization knowledge set are established. Furthermore, based on the core of knowledge conversion into learning samples, a model of knowledge integration in reinforcement learning is designed. The dynamic adjustment of knowledge-guided action sampling probability, diversified knowledge, random exploration probability has been emphatically studied. The network and DQN algorithm with knowledge integration are designed. The case studies demonstrate that the proposed method can automatically adapt to uncertainties in the HES, and the energy cost is 11.1% lower than the knowledge rule-based method; compared with the standard DQN, under the same convergence criterion, the proposed method has a 3.3% lower energy cost and the convergence time is only 1/6 of the standard DQN.