Method and system for automatic machine learning-based prediction of new energy power with cloud-edge collaboration

A method for automatic machine learning-based prediction of new energy power with cloud-edge collaboration, which is executed by a cloud server, the method comprising:

• training, for each new energy station in a set of stations, each initial model based on a training sample set corresponding to the new energy station, and selecting an optional power prediction model corresponding to the new energy station from trained initial models, the training sample set comprising sample weather prediction data and sample output power, and different initial models being constructed based on different algorithms;
• constructing, based on the optional power prediction model corresponding to each new energy station, a set of power prediction models, wherein the set of power prediction models is configured for an edge server to:
• select a target power prediction model corresponding to a target new energy station, based on missing of future numerical weather prediction data of the target new energy station in a future period and a data amount of historical output power of the target new energy station in a historical period corresponding to the future period;
• and
• adjust the target power prediction model in a target working mode, and predict, 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 of the future period and the historical output power of the historical period corresponding to the future period;
• and
• delivering the set of power prediction models to the edge server.

A device for automatic machine learning-based prediction of new energy power with cloud-edge collaboration, the device comprising:

• a response module configured to obtain, in respond 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;
• a selection module configured to select, 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 from a set of power prediction models, each optional power prediction model in the set of power prediction models being trained and delivered by a cloud server based on an automatic machine learning algorithm;
• an adjustment module configured to determine, based on the power prediction demand, a target working mode of the target power prediction model, the target working mode comprising a prediction business object, a prediction type, and a prediction time scale;
• and
• a prediction module configured to:
• adjust the target power prediction model according to the target working mode;
• and
• predict, 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.

A device for automatic machine learning-based prediction of new energy power with cloud-edge collaboration, the device comprising:

• a training module configured to:
• train, for each new energy station in a set of stations, each initial model based on a training sample set corresponding to the new energy station, and
• select an optional power prediction model corresponding to the new energy station from trained initial models, the training sample set comprising sample weather prediction data and sample output power, and different initial models being constructed based on different algorithms;
• a constructing module configured to construct, based on the optional power prediction model corresponding to each new energy station, a set of power prediction models, wherein the set of power prediction models is configured for an edge server to:
• select a target power prediction model corresponding to the target new energy station, based on missing of future numerical weather prediction data of the target new energy station in the future period and a data amount of the historical output power of the target new energy station in the historical period corresponding to the future period;
• and
• adjust the target power prediction model in a target working mode, and predict, 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 of the future period and the historical output power of the historical period corresponding to the future period;
• and
• a delivery module configured to deliver the set of power prediction models to the edge server.

A system for automatic machine learning-based prediction of new energy power with cloud-edge collaboration, the system comprising:

• a cloud server configured to:
• train, for each new energy station in a set of stations, each initial model based on a training sample set corresponding to the new energy station;
• select an optional power prediction model corresponding to the new energy station from trained initial models, the training sample set comprising sample weather prediction data and sample output power, and different initial models being constructed based on different algorithms;
• construct, based on the optional power prediction model corresponding to each new energy station, a set of power prediction models;
• and
• deliver the set of power prediction models to the edge server;
• and
• an edge server configured to:
• receive the set of power prediction models;
• obtain, 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,
• select, 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 from a set of power prediction models, each optional power prediction model in the set of power prediction models being trained and delivered by the cloud server based on an automatic machine learning algorithm;
• determine, based on the power prediction demand, a target working mode of the target power prediction model, the target working mode comprising a prediction business object, a prediction type, and a prediction time scale;
• and
• adjust the target power prediction model according to the target working mode, and predict, 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.