Machine Learning Data-Driven Residential Load Multi-Level Forecasting With Univariate and Multivariate Time Series Models Toward Sustainable Smart Homes

Abstract

Residential energy consumption is rapidly increasing every year due to demographic and behavioral changes, such as the rising population and the adoption of work-from-home post-COVID-19. High energy consumption emits a substantial amount of carbon dioxide and other Greenhouse Gases, contributing to global warming. It becomes crucial to accurately predict residential load. To enable smart home electricity consumption control, as well as efficient generation, planning, and usage, we predict household energy consumption at very short-term, short-term, and medium-term forecast levels using univariate and multivariate time series data. This study assesses the impact of different household units (water heater and air conditioning), areas (kitchen, laundry, office, living room, bathroom, ironing room, teenager room, and parents’ room), and time (i.e., hour, day, and month) on energy consumption. Comparative analysis and numerical experimental results between the most used approaches, Support Vector Regression and Long Short-Term Memory, reveal that the former outperforms the latter across all forecast levels using different datasets. The findings of this paper will be useful to energy companies and household owners in enhancing energy efficiency and earning carbon credits by reducing the emission of carbon dioxide and other Greenhouse Gases.

Keywords

• Carbon credit
• carbon emission
• deep learning
• energy consumption prediction
• energy efficiency
• forecast levels