Abstract: A method and a system for automatic machine learning-based prediction of new energy power with cloud-edge collaboration are disclosed. The method includes: obtaining, in response to a power prediction demand for a target new energy station, future numerical weather prediction data of the target new energy station in a future period and historical output power of a historical period corresponding to the future period; selecting, based on missing of the future numerical weather prediction data and a data amount of the historical output power, a target power prediction model corresponding to the target new energy station; and adjusting the target power prediction model according to the target working mode, and predicting, by the adjusted target power prediction model, a target output power of the target new energy station in the future period, based on the future numerical weather prediction data and the historical output power.

Abstract: A method of producing a carbon nanotube may include heating a convergent flow nozzle and a flow chamber of a carbon nanotube reactor, flowing a vaporized reactant stream through the nozzle and collecting a carbon nanotube product from an outlet of a heated flow chamber disposed downstream of the heated convergent flow nozzle. The vaporized reactant stream may include a carbon nanotube nucleation carbon source, a carbon nanotube growth carbon source, and a carbon nanotube catalyst precursor. The nozzle may have a nozzle contraction ratio of from 3:1 to 300:1.