Method of and system for generating a weather forecast

A method of creating a weather forecast, the method being executed on a server including a processor and a machine learning module, the method comprises:

• receiving of a request to create a weather forecast;
• receiving by a machine learning module at least one current weather measurement parameter being a weather parameter at a measurement time;
• receiving by the machine learning module a first average value of a historical weather parameter for the measurement time;
• the first average value of the historical weather parameter having been generated based on an indication of the measurement time;
• creating by the machine learning module a normalized value of a weather measurement parameter based on a difference between a current weather measurement parameter and the first average value of the historical weather parameter for the measurement time;
• creating by the machine learning module a normalized value of a weather forecasting parameter based on the normalized value of the weather measurement parameter, the normalized value of the weather forecasting parameter is connected to a future forecasting time following after the measurement time,
• the normalized value of the weather forecasting parameter being indicative of a difference between a predicted value of the weather forecasting parameter and a second average value of the historical weather parameter associated with the future forecasting time;
• at a given point in the future forecasting time:
• receiving a given average value of the historical weather parameter for the given point in the future forecasting time,
• the given average value of the historical weather parameter having been generated based on an indication of the given point in the future forecasting time;
• predicting, based on the normalized value of the weather measurement parameter, the normalized value of the weather forecasting parameter for the given point in the future forecasting time;
• based on the normalized value of the weather forecasting parameter and the given average value of the historical parameter for the given point in the future forecasting time, creating the weather forecast.

A server for creating a weather forecast, the server comprising:

• a machine learning module and a processor operationally connected to the machine learning module,
• the processor is configured to execute:
• receiving, by the machine learning module, at least one current weather measurement parameter, the at least one current weather measurement parameter being a weather parameter at a measurement time;
• receiving, by the machine learning module, a first average value of a historical weather parameter for the measurement time,
• the first average value of the historical weather parameter having been generated based on an indication of the measurement time;
• generating, by the machine learning module, a normalized value of the weather measurement parameter based on a difference between the current weather measurement parameter and the first average value of the historical weather parameter for the measurement time;
• training the machine learning module to create a normalized value of a weather forecasting parameter based on the normalized value of the weather measurement parameter, the normalized value of the weather forecasting parameter being associated with a future forecasting time, the future forecasting time occurring after the measurement time,
• the normalized value of the weather forecasting parameter being indicative of a difference between a predicted value of the weather forecasting parameter and a second average value of the historical weather parameter associated with the future forecasting time;
• at a given point in the future forecasting time:
• receiving, by the machine learning module, a given average value of the historical weather parameter for the given point in the future forecasting time,
• the given average value of the historical weather parameter having been generated based on an indication of the given point in the future forecasting time;
• predicting, by the machine learning module, the normalized value of the weather forecasting parameter for the given point in the future forecasting time;
• generating, based on the normalized value of the weather forecasting parameter and the given average value of the historical parameter for the given point in the future forecasting time, the weather forecast.

A method of generating a weather forecast, the method executable on a server, the server including a processor and a machine learning module, the method comprising:

• receiving, by the machine learning module, at least one current weather measurement parameter, the at least one current weather measurement parameter being a weather parameter at a measurement time;
• receiving, by the machine learning module, a first average value of a historical weather parameter for the measurement time,
• the first average value of the historical weather parameter having been generated based on an indication of the measurement time;
• generating, by the machine learning module, a normalized value of the weather measurement parameter based on a difference between the current weather measurement parameter and the first average value of the historical weather parameter for the measurement time, wherein the normalized value of the weather measurement parameter is calculated according to a formula:
• ΔT=|T1−T2|,
• where T1 is the current weather measurement parameter, T2 is the first average value of the historical weather parameter;
• training the machine learning module to create a normalized value of a weather forecasting parameter based on, at least partially, the normalized value of the weather measuring parameter, the normalized value of the weather forecasting parameter being associated with a future forecasting time, the future forecasting time occurring after the measurement time;
• the normalized value of the weather forecasting parameter being indicative of a difference between a predicted value of the weather forecasting parameter and a second average value of the historical weather parameter associated with the future forecasting time;
• at a given point in the future forecasting time:
• receiving, by the machine learning module, a given average value of the historical weather parameter for the given point in the future forecasting time,
• the given average value of the historical weather parameter having been generated based on an indication of the given point in the future forecasting time;
• predicting, by the machine learning module, the normalized value of the weather forecasting parameter for the given point in the future forecasting time;
• based on the normalized value of the weather forecasting parameter and the given average value of the historical weather parameter, creating the weather forecast.