Year: 2022
Type: conference-paper
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.
Authors: Ohoud Almughram, Bassam Zafar, Sami Ben Slama
Institution: King Abdulaziz University
Language: English
Topic: Smart Grid Energy Management
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4212833145