Appliance Energy Prediction Using Time Series Forecasting: A comparative analysis of different Machine Learning Algorithms

Volume: 8    Issue: 1

Year of Publication: 2021

Pages: (7-18)

Authors: Soham Talukdar

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Abstract

Energy prediction of appliances requires identifying and predicting individual appliance energy consumption when combined in a closed chain environment. This experiment aims to provide insight into reducing energy consumption by identifying trends and appliances involved. The proposed model tries to formalize such an approach using a time series forecasting- based process that considers the correlation between different appliances. The entire work has been conducted in two parts. The first part highlights and identifies the energy consumption trends. The second part focuses on the comparison and analysis of different algorithms. The main objective is to understand which algorithm provides a better result in predicting energy consumption. A comparison of algorithms for appliance usage prediction using identification and direct consumption reading is presented in this paper. The work is presented on real data taken from the REMODECE database, which comprises 19,735 instances with 29 attributes. The data records the energy for 10 minutes over about 4.5 months.

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Keywords

Energy, Prediction, Algorithm, Data.