Home Energy Management Machine Learning Prediction Algorithms: A Review

DOI

10.2991/aisr.k.220201.008How to use a DOI?

Keywords

Home Energy Management System; Machine Learning algorithm; Prediction; Forecasting; Optimization

Abstract

Renewable energies are being introduced in countries around the world to move away from the environmental impacts from fossil fuels. In the residential sector, smart buildings that utilize smart appliances, integrate information and communication technology and utilize a renewable energy source for in-house power generation are becoming popular. Accordingly, there is a need to understand what factors influence the accuracy of managing such smart buildings. Thus, this study reviews the application of machine learning prediction algorithms in Home Energy Management Systems. Various aspects are covered, such as load forecasting, household consumption prediction, rooftop solar energy generation, and price prediction. Also, a proposed Home Energy Management System framework is included based on the most accurate machine learning prediction algorithms of previous studies. This review supports research into the selection of an appropriate model for predicting energy consumption of smart buildings.

Copyright

© 2022 The Authors. Published by Atlantis Press International B.V.

Open Access

This is an open access article under the CC BY-NC license.

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TY  - CONF
AU  - Ohoud Almughram
AU  - Bassam Zafar
AU  - Sami Ben Slama
PY  - 2022
DA  - 2022/02/02
TI  - Home Energy Management Machine Learning Prediction Algorithms: A Review
BT  - Proceedings of the 2nd International Conference on Industry 4.0 and Artificial Intelligence (ICIAI 2021)
PB  - Atlantis Press
SP  - 40
EP  - 47
SN  - 1951-6851
UR  - https://doi.org/10.2991/aisr.k.220201.008
DO  - 10.2991/aisr.k.220201.008
ID  - Almughram2022
ER  -