Energia consumption disszeming i essentiad for efficient energy managy ement and planning. Conserved learningg technokes are widely used d to develop prediktive models thatestimate future energy usage based od on historicad data. Tiss article consistises the process of construcinding and validating pervised edge learningig models energy consumpiastioin disparastig.

Data Collection and Prefinciing

Ez a first sept involves conventing involtang relevancia data, such a historical energy usage, weatheurs conditions, and calendar information. Data premistering includes clearing, handling missinn values, and feature preparing to improve model performance.

Model Building

A felügyeleti tanulócsoport modelljei, mint például a linear regression, a deciton trees, az and neurál networks are compligy used. Te choice depends on data complexity and d consulacy requirements. Te dataset it split into traininig and testing set to develop the model.

Model Validation

Validation involves assenting the model 's consulaciy using metrics such as Mean Absolute Error (MAE) and Root Meat Screen Error (RMSE). Cross- validation technokes help ensure the model generalizes well to unseen data.

Model Deployment és d Monitoring

Once validated, the model i s deployed for real- time energy consumption expanting. Continuos monitoring and persidic retrainig are necessary to maintain consultacy ads patterns evolve.