摘要：Now the world is becoming more sophisticated and networked, and a massive amount of data is being generated daily. For energy management in residential and commercial properties, it is essential to know how much energy each appliance uses. The forecast would be more clear and practical if the task is based purely on energy usage data. But in the real world, it’s not the case, energy consumption is strongly dependent on weather and surroundings also. In a home appliances network when measured/observed data is available then algorithms of supervised-based machine learning provide an immeasurable alternative to the annoyance associated with many engineering and data mining methodologies. The patterns of household energy consumption are changing based on temperature, humidity, hour of the day, etc. For predicting household energy consumption feature engineering is performed, and models are trained by using different machine learning algorithms such as Linear Regression, Lasso Regression, Random Forest, Extra Tree Regressor, XG Boost, etc.. To evaluate the models R square is used as the forecasting is based on time. R square tells how much percentage of variance in the dependent variable can be predicted. Finally, it is suggested that tree-based models are giving best results.
