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Predictive Modelling of Home Appliances Energy Consumption in Belgium

Chaudhary, Anant (2018) Predictive Modelling of Home Appliances Energy Consumption in Belgium. Masters thesis, Dublin, National College of Ireland.

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The increasing trend in energy consumption is becoming cause of concern for the entire world, as the energy consumption is increasing year after year so is the carbon and greenhouse gas emission, the majority portion of the electricity generated is consumed by industrial sector but a considerable amount is also consumed by residential sector. It is important to study the energy consuming behaviour in the residential sector and predict the energy consumption by home appliances as it consume maximum amount of energy in the residence. The European energy goal of 2020 is to reduce the energy consumption and carbon emission by 20%. This project focuses on predicting the energy consumption of home appliances based on humidity and temperature. It has resulted in implementation of five prediction regression models, i.e. multiple regression, lasso regression, ridge regression, SVM regression and Random Forest are developed and results are presented based on RMSE, MAE and MAPE, the dataset for the analysis was taken from a house located in Stambruges (Belgium), keeping European goal of 2020 in mind. In addition the results of reviewed literature of home energy consumption in Europe is also presented.

Item Type: Thesis (Masters)
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
Q Science > QA Mathematics > Computer software
T Technology > T Technology (General) > Information Technology > Computer software
T Technology > TK Electrical engineering. Electronics. Nuclear engineering > Electricity Supply
G Geography. Anthropology. Recreation > GE Environmental Sciences > Environment
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: Caoimhe Ní Mhaicín
Date Deposited: 06 Nov 2018 11:10
Last Modified: 06 Nov 2018 11:10

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