The professional nature and confidentiality of the power domain hinder the public to accurately assess the authenticity of online electricity-related statements, fostering an environment conducive to the spread of electricity-related hate speech on social media. To address this challenge, we introduce a new hate speech detection task for the electric power domain. A dataset for electric power domain hate speech detection is constructed, consisting of 6000 electricity-related Weibo posts. We propose a prompt learning approach for hate speech detection in the electric power domain, which integrates power domain knowledge, such as work scenarios and terms. Subsequently, a prompt template is formulated to facilitate hate speech detection. Experimental results on the dataset indicate that the proposed prompt learning method surpasses the baseline model.