Predictive Model of Energy Consumption Using Machine Learning: A Case Study of Residential Buildings in South Africa

The recurrent load shedding crisis in South Africa has highlighted the need to accurately predict electricity consumption for residential buildings. This has significant ramifications for daily life and economic productivity. To address this challenge, this study leverages machine learning models to...

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Bibliographic Details
Published in:Sustainability Vol. 16; no. 11; p. 4365
Main Authors: Moulla, Donatien Koulla, Attipoe, David, Mnkandla, Ernest, Abran, Alain
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.06.2024
Subjects:
Case studies
Electric power production
Energy consumption
Energy industry
Energy management systems
Energy use
Green technology
Households
Machine learning
Neural networks
Smart houses
Wind power
South Africa
Sub-Saharan Africa
Africa
ISSN:2071-1050, 2071-1050
Online Access:Get full text
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Summary:The recurrent load shedding crisis in South Africa has highlighted the need to accurately predict electricity consumption for residential buildings. This has significant ramifications for daily life and economic productivity. To address this challenge, this study leverages machine learning models to predict the hourly energy consumption of residential buildings in South Africa. This study evaluates the performance of various regression techniques, including Random Forest (RF), Decision Tree (DT), Extreme Gradient Boosting (XGBoost), and Adaptive Boosting (AdaBoost) machine learning models, using a national residential dataset that contains measurements collected every hour. The objective is to determine the most effective models for predicting next-hour residential building consumption. These models use historical patterns of energy usage to capture temporal details such as seasonal variations and rolling averages. Feature engineering methods are further employed to enhance their predictive capabilities. The performance of each individual model was evaluated using criteria such as the mean squared error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and coefficient of determination (R2). The results show that both RF and DT achieve the best accuracy for the prediction of residential electricity consumption (because the MSE, MAE, and MAPE for RF and DT are very close to 0). These findings offer actionable insights for households, businesses, and policymakers. By enabling more accurate and granular energy consumption forecasts, this can mitigate the effects of load shedding. This study contributes to the discourse on sustainable energy management by combining advanced machine learning models with real-world energy challenges.
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ISSN:2071-1050
2071-1050
DOI:10.3390/su16114365