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As a result of increasing population and globalization, the demand for energyhas greatly risen. Therefore, accurate energy consumption forecasting hasbecome an essential prerequisite for government planning, reducing powerwastage and stable operation of the energy management system. In this work wepresent a comparative analysis of major machine learning models for time seriesforecasting of household energy consumption. Specifically, we use Weka, a datamining tool to first apply models on hourly and daily household energyconsumption datasets available from Kaggle data science community. The modelsapplied are: Multilayer Perceptron, K Nearest Neighbor regression, SupportVector Regression, Linear Regression, and Gaussian Processes. Secondly, we alsoimplemented time series forecasting models, ARIMA and VAR, in python toforecast household energy consumption of selected South Korean households withand without weather data. Our results show that the best methods for theforecasting of energy consumption prediction are Support Vector Regressionfollowed by Multilayer Perceptron and Gaussian Process Regression.

Comparative Analysis of Time Series Forecasting Approaches for Household Electricity Consumption Prediction