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With the rapid development of cloud computing, the increasing demand for server energy consumption in data center leads to crucial economic and environmental issues. Reducing the data center energy consumption is of great significance to cut down the operating cost of data center and realize the global "double-carbon" strategic goal. Therefore, an increasing amount of research on power consumption models and prediction at different levels in cloud servers. This paper conducted a systematic study about existing work in power consumption models from two levels, hardware and software. At the hardware level, the overall energy consumption models of the cloud server is classified according to the additive server power models, system utilization based server power models and other server power models, the energy consumption models of the server components are also presented, including the CPU, memory, disk and network interface. At the software level, the server energy consumption models are summarized according to the category of machine learning, such as supervised learning, unsupervised learning and reinforcement learning. By comparing existing approaches and solutions, we analyzed their advantages, limitations, and suitable scenarios. In addition, we also pointed out several possible research directions.