Please use this identifier to cite or link to this item: http://hdl.handle.net/10266/5304
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dc.contributor.supervisorBala, Anju-
dc.contributor.authorKaur, Jasmeet-
dc.date.accessioned2018-08-23T05:28:29Z-
dc.date.available2018-08-23T05:28:29Z-
dc.date.issued2018-08-23-
dc.identifier.urihttp://hdl.handle.net/10266/5304-
dc.description.abstractEnergy Management refers to analyzing and saving the energy by monitoring and controlling the energy pro les. Energy Management has gained its importance in every sector such as transportation sector, agriculture sector, residential sector and industrial sector. Energy Management in dwellings has become the recurrent issue nowadays due to an increase of electrical appliances with the upcoming new technologies. The energy consumption of these appliances depends on various fac- tors such as climatic conditions, the number of occupants and their behaviour, the usage of appliances in the homes etc. The energy demand has increased in the residential sector and the energy suppliers are not able to ful l the need of energy which is leading to power cuts. Further, the appliances emit the harmful radiation and leading to greenhouse gas emissions. So, there is the need to predict the energy consumption in the residential sector and to optimize the energy in such a way such that energy supplier would be able to supply the electricity in dwellings. Though many researchers have worked on the energy consumption and its op- timization, a very few have taken into account the climatic conditions for its prediction. Hence, the motive of this dissertation is to predict and to optimize the energy consumption for the suppliers. For this, it was important to recognize the hidden patterns in which the appliances are being used in the house. For nding the hidden patterns, principal component analysis with K-means cluster- ing was performed and three clusters were formed which consist of the appliances having high energy consumption, moderate energy and the continuous energy consumption. The prediction models were implemented for predicting the energy consumption at appliance level after integrating the cluster according to di erent climate conditions such as temperature, wind speed, visibility etc. Finally, the hy- brid optimization was performed for minimizing the value of energy consumption. This dissertation will be bene ts to the energy suppliers for providing the elec- tricity demand to the end user and also will be bene ts to the households for completion for their daily activities.en_US
dc.language.isoenen_US
dc.subjectEnergy Efficiency, Home Appliances, Clouden_US
dc.titleA Hybrid Energy Optimization Approach for Home Appliancesen_US
dc.typeThesisen_US
Appears in Collections:Masters Theses@CSED

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