DOIONLINE

DOIONLINE NO - IJAECS-IRAJ-DOIONLINE-17501

Publish In
International Journal of Advances in Electronics and Computer Science-IJAECS
Journal Home
Volume Issue
Issue
Volume-7,Issue-9  ( Sep, 2020 )
Paper Title
Comparative Analysis of Time Series Forecasting Approaches for Household Electricity Consumption Prediction
Author Name
Muhammad Bilal, Hyeok Kim, Muhammad Fayaz, Pravin Pawar
Affilition
Department of Computer Science, State University of New York, Korea, Incheon Global Campus, Incheon, South Korea. Ninewatt Inc., Incheon Global Campus, Incheon, South Korea. 3Department of Computer Science, University of Central Asia, Naryn Campus, Naryn, Kyrgyz Republic.
Pages
10-16
Abstract
As a result of increasing population and globalization, the demand for energy has greatly risen. Therefore, accurate energy consumption forecasting has become an essential prerequisite for government planning, reducing power wastage and stable operation of the energy management system. In this work we present a comparative analysis of major machine learning models for time series forecasting of household energy consumption. Specifically, we use Weka – a data mining tool to first apply models on hourly and daily household energy consumption datasets available from Kaggle data science community. The models applied are: Multi-Layer Perceptron, K-Nearest Neighbor regression, Support Vector Regression, Linear Regression, and Gaussian Processes. Secondly, we also implemented time series forecasting models - ARIMA and VAR- in python to forecast household energy consumption of selected South Korean households with and without weather data. Our results show that the best methods for the forecasting of energy consumption prediction are Support Vector Regression followed by Multilayer Perceptron and Gaussian Process Regression. Keywords - Time Series Forecasting, ARIMA, VAR, WEKA, Multi-Layer Perceptron, K-Nearest Neighbor regression, Support Vector Regression, Linear Regression, and Gaussian Processes.
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