The Jiangmen Underground Neutrino Observatory (JUNO) with its satellite Taishan Antineutrino Observatory (TAO) is a next-generation neutrino experiment with a broad physics program. Currently under construction, JUNO is expected to start data-taking in 2024. The central detector of JUNO is an acrylic sphere filled with 20 kt of liquid-scintillator (LS) surrounded by 43212 photomultiplier tubes (PMTs).

The primary goals of the experiment are to determine the neutrino mass ordering (NMO) within 3-4$\sigma$ in 6 years and to measure neutrino oscillation parameters $\sin^2{\theta_{12}}$, $\Delta m_{21}^2$, $\Delta m^{2}_{31}$ with sub-percent precision. To achieve the goals, JUNO will study reactor antineutrino emitted from two nuclear power plants located 52.5 km away from the detector.

The main requirement for JUNO is a high energy resolution. The detector is constructed to provide an energy resolution of 3% at 1 MeV. In this study, neutrino energy reconstruction with machine learning techniques is presented. The reconstruction techniques are based on aggregated information collected by PMTs. Two models are considered: Boosted Decision Trees and Fully Connected Deep Neural Network. Moreover, the transferability of the approach is shown with an example of JUNO's satellite detector TAO.