Forecasting Residential Energy Consumption: Single Household Perspective

Abstract

With the development of smart electricity metering technologies, huge amounts of consumption data can be retrieved on a daily and hourly basis. Energy consumption forecasting facilitates electricity demand management and utilities load planning. Most studies have been focussed on commercial customers or residential building-level energy consumption, or have used behavioral and occupancy sensor data to characterize an individual household's electrical consumption. This study has analyzed energy consumption at single household level using smart meter data to improve residential energy services and gain insights into planning demand response programs. Electricity consumption for anonymous individual households has been predicted using a Support Vector Regression (SVR) modelling with both daily and hourly data granularity. The electricity usage data set for 2014 to 2016 was obtained from a Canadian utility company. Exploratory data analysis (EDA) was used for data visualization and feature selection. The analysis presented here demonstrates that forecasting residential energy consumption for individual households is feasible, but the accuracy is highly dependable on household behaviour variability.