Load scheduling for smart home using day-ahead prediction from Artificial Neural Network (ANN)

Artificial Neural Network (ANN) in Mixed-Integer Linear Programming (MILP) technique for load scheduling of appliances in a single smart home system. This proposed method is achieved through backpropagation method of ANN tools in MATLAB which is the central mechanism by which neural networks learn t...

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Main Author:	Joharry, Siti Hajar
Format:	Thesis
Language:	English
Published:	2020
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/93120/1/SitiHajarJoharryMSKE2020.pdf
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id	my-utm-ep.93120
record_format	uketd_dc
spelling	my-utm-ep.931202021-11-19T03:23:51Z Load scheduling for smart home using day-ahead prediction from Artificial Neural Network (ANN) 2020 Joharry, Siti Hajar TK Electrical engineering. Electronics Nuclear engineering Artificial Neural Network (ANN) in Mixed-Integer Linear Programming (MILP) technique for load scheduling of appliances in a single smart home system. This proposed method is achieved through backpropagation method of ANN tools in MATLAB which is the central mechanism by which neural networks learn to predict the next day load consumption of a home and promptly inserting the output to the MILP which would optimize the process of load scheduling. The integration of ANN with MILP can contribute to the precision of load scheduling. Having said that, to obtain the day ahead energy consumption, the annual data of the home is extract and injected in ANN as input and target classes. Hence, with the process of backpropagation, energy consumption is predicted while taking into consideration the Mean Squared Error (MSE) of the model. This prediction is then incorporated in the programming of MILP for optimization of load scheduling. The performance of the model is then evaluated by comparing before and after the optimization process. A total load of the appliance has been reduced from 51.24 kW/day to 44.84 kW/day. Furthermore, the overall cost of the electricity bill has been reduced from $3.98/day to $2.45/day. Therefore, the deduction of 38.44% of electricity bills makes the proposed method notably applicable and best to use in real time situation. 2020 Thesis http://eprints.utm.my/id/eprint/93120/ http://eprints.utm.my/id/eprint/93120/1/SitiHajarJoharryMSKE2020.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:135986 masters Universiti Teknologi Malaysia, Faculty of Engineering - School of Electrical Engineering Faculty of Engineering - School of Electrical Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TK Electrical engineering Electronics Nuclear engineering
spellingShingle	TK Electrical engineering Electronics Nuclear engineering Joharry, Siti Hajar Load scheduling for smart home using day-ahead prediction from Artificial Neural Network (ANN)
description	Artificial Neural Network (ANN) in Mixed-Integer Linear Programming (MILP) technique for load scheduling of appliances in a single smart home system. This proposed method is achieved through backpropagation method of ANN tools in MATLAB which is the central mechanism by which neural networks learn to predict the next day load consumption of a home and promptly inserting the output to the MILP which would optimize the process of load scheduling. The integration of ANN with MILP can contribute to the precision of load scheduling. Having said that, to obtain the day ahead energy consumption, the annual data of the home is extract and injected in ANN as input and target classes. Hence, with the process of backpropagation, energy consumption is predicted while taking into consideration the Mean Squared Error (MSE) of the model. This prediction is then incorporated in the programming of MILP for optimization of load scheduling. The performance of the model is then evaluated by comparing before and after the optimization process. A total load of the appliance has been reduced from 51.24 kW/day to 44.84 kW/day. Furthermore, the overall cost of the electricity bill has been reduced from $3.98/day to $2.45/day. Therefore, the deduction of 38.44% of electricity bills makes the proposed method notably applicable and best to use in real time situation.
format	Thesis
qualification_level	Master's degree
author	Joharry, Siti Hajar
author_facet	Joharry, Siti Hajar
author_sort	Joharry, Siti Hajar
title	Load scheduling for smart home using day-ahead prediction from Artificial Neural Network (ANN)
title_short	Load scheduling for smart home using day-ahead prediction from Artificial Neural Network (ANN)
title_full	Load scheduling for smart home using day-ahead prediction from Artificial Neural Network (ANN)
title_fullStr	Load scheduling for smart home using day-ahead prediction from Artificial Neural Network (ANN)
title_full_unstemmed	Load scheduling for smart home using day-ahead prediction from Artificial Neural Network (ANN)
title_sort	load scheduling for smart home using day-ahead prediction from artificial neural network (ann)
granting_institution	Universiti Teknologi Malaysia, Faculty of Engineering - School of Electrical Engineering
granting_department	Faculty of Engineering - School of Electrical Engineering
publishDate	2020
url	http://eprints.utm.my/id/eprint/93120/1/SitiHajarJoharryMSKE2020.pdf
_version_	1747818635323244544

Load scheduling for smart home using day-ahead prediction from Artificial Neural Network (ANN)

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