Energy consumption prediction of residential buildings

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DC Field	Value	Language
dc.contributor.author	Diwan, Parikshit
dc.contributor.author	Singh, Pushpendra (Advisor)
dc.date.accessioned	2018-09-24T11:06:59Z
dc.date.available	2018-09-24T11:06:59Z
dc.date.issued	2018-04-18
dc.identifier.uri	http://repository.iiitd.edu.in/xmlui/handle/123456789/666
dc.description.abstract	Residential Electricity consumption accounts for 25% of electric energy consumption consumed in India and is expected to increase in the future to rapid urbanization, growing income levels, etc. In this project we have tried to build models which can help us predict electricity consumption .An ability to do so will aide in managing demand , help estimate wear and tear of the system and most importantly reduce wastage. In this project we have explored 3 approaches to do so: ARIMA model , the VAR model and Neural Network model.	en_US
dc.language.iso	en_US	en_US
dc.publisher	IIIT-Delhi	en_US
dc.subject	Time series analysis	en_US
dc.subject	ARIMA	en_US
dc.subject	VAR	en_US
dc.subject	Neural nets	en_US
dc.subject	MASE	en_US
dc.title	Energy consumption prediction of residential buildings	en_US
dc.type	Other	en_US
Appears in Collections:	Year-2018

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