The mobile Internet and Internet of Things are considered the main driving forc?es of 5G, as they require an ultra-dense deployment of small base stations to meet the in?creasing traffic demands. 5G new radio (NR) access is designed to enable denser network deployments, while leading to a significant concern about the network energy consump?tion. Energy consumption is a main part of network operational expense (OPEX), and base stations work as the main energy consumption equipment in the radio access network (RAN). In order to achieve RAN energy efficiency (EE), switching off cells is a strategy to reduce the energy consumption of networks during off-peak conditions. This paper intro?duces NR cell switching on/off schemes in 3GPP to achieve energy efficiency in 5G RAN, including intra-system energy saving (ES) scheme and inter-system ES scheme. Addition?ally, NR architectural features including central unit/distributed unit (CU/DU) split and dual connectivity (DC) are also considered in NR energy saving. How to apply artificial in?telligence (AI) into 5G networks is a new topic in 3GPP, and we also propose a machine learning (ML) based scheme to save energy by switching off the cell selected relying on the load prediction. According to the experiment results in the real wireless environment, the ML based ES scheme can reduce more power consumption than the conventional ES scheme without load prediction.