Home Energy Management Machine Learning Prediction Algorithms: A Review

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.

A Review of Cooling and Heating Loads Predictions of Residential Buildings Using Data-Driven Techniques

“…A review of data-driven methods based on AI that could be utilised to achieve an efficient Home Energy Management System (HEMS) has been conducted [3]. The authors focused on research studies that investigated the problem of predicting HEMS factors including household consumption, energy load forecasting, load generation, and estimating energy bill prices, among others.…”

“…Energy efficiency can be achieved by constructing or advancing buildings that optimise energy use through building energy management systems (BEMS) [3]. A BEMS is a central computer-aided system that manages the movement of cooled and heated air through buildings to help stakeholders, including homeowners, tenants, and businesses, to substantially improve energy use through HVAC, lighting, and room equipment that require power to operate [4].…”

“…A BEMS is a central computer-aided system that manages the movement of cooled and heated air through buildings to help stakeholders, including homeowners, tenants, and businesses, to substantially improve energy use through HVAC, lighting, and room equipment that require power to operate [4]. A BEMS focuses on multiple factors including the household consumption rate, energy load forecasting, load generation rate and forecasting energy bill prices [3]. These factors can be estimated from historical data related to BEMS parameters using data-driven techniques.…”