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

Leila Ismail, Huned Materwala, Fida K. Dankar

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Pages (from-to)55632-55668
Number of pages37
JournalIEEE Access
Volume12
DOIs
Publication statusPublished - 2024

Keywords

  • Carbon credit
  • Jensen-Shannon divergence
  • carbon emission
  • deep learning
  • energy consumption prediction
  • energy efficiency
  • forecast levels
  • greenhouse gases (GHGs)
  • load forecasting
  • long short-term memory (LSTM)
  • machine learning
  • residential building energy consumption
  • root mean square error (RMSE)
  • support vector regression (SVR)
  • symmetric mean absolute percentage error (sMAPE)
  • time series forecasting

ASJC Scopus subject areas

  • General Engineering
  • General Computer Science
  • General Materials Science

Fingerprint

Dive into the research topics of 'Machine Learning Data-Driven Residential Load Multi-Level Forecasting with Univariate and Multivariate Time Series Models Toward Sustainable Smart Homes'. Together they form a unique fingerprint.

Cite this