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 |