Please use this identifier to cite or link to this item:
http://hdl.handle.net/10603/357009
Title: | Energy Prediction for Smart Buildings Pertaining to the Heating Ventilation and Air Conditioning Plants Using Machine Learning Techniques |
Researcher: | Goyal, Monika |
Guide(s): | Mrinal Pandey |
Keywords: | Air Conditioning Plants Using Machine Learning Techniques Computer Science Artificial Intelligence Computer Science Interdisciplinary Applications Smart Buildings Pertaining to the Heating Ventilation |
University: | Manav Rachna University |
Completed Date: | 2021 |
Abstract: | The world is facing a critical issue of over-consumption of energy and its wastage. Most of the newlineenergy is consumed by Buildings. Further, within buildings, Heating, Ventilation, and Air newlineConditioning (HVAC) plant is the largest energy consumer. Several researchers have worked newlinein this area as to how the problem of energy wastage should be handled. Researchers have tried newlineto explore different aspects of energy wastage in buildings- Occupancy, Occupant behavior, newlinelack of robust methods for energy prediction and analysis, lack of Building management newlinesystems etc. Machine Learning has tremendous power that can be used to find solutions to newlinevarious real-life problems prevalent in society. An initial and important step to counter newlineenergy wastage is to accurately predict the amount of energy consumed in buildings and by newlineHVAC. newlineThis research addresses the problem of accurate energy prediction by proposing an approach newlineusing Machine Learning, Ensemble Learning, and Deep Learning techniques. The research was newlinecarried out using two datasets- One of them is a standard dataset, related to Energy newlineconsumption, that was collected from a public UCI repository. It consists of eight building newlineparameters as input variables and Heating load and Cooling load as two output variables. newlineAnother dataset related to Energy consumption was collected from a commercial building in newlineIndia. The dataset was raw, so it was pre-processed. Datasets were partitioned into 70%-30% newlineratio and 80%-20% ratio for Training and testing purposes respectively. Feature selection was newlineperformed on datasets by using Filter methods and Wrapper methods. Several Machine newlineLearning models were explored, namely MLR, SVR, KNN, CART. Ensemble Learning newlinemethods that were explored are- Bagging, RF, GBM and XGBoost. LSTM and GRU models newlinewere explored in Deep Learning. Hyper-parameter tuning was done to obtain the best possible newlineresults. Root Mean Square Error, Mean Square Error, Mean Absolute Error and R Squared newlinewere used as performance metrics for model evaluation. |
Pagination: | |
URI: | http://hdl.handle.net/10603/357009 |
Appears in Departments: | Department of Computer Science Engineering and Technology |
Files in This Item:
File | Description | Size | Format | |
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01_title.pdf | Attached File | 385.92 kB | Adobe PDF | View/Open |
02_declaration.pdf | 396.59 kB | Adobe PDF | View/Open | |
03_certificate.pdf | 384.04 kB | Adobe PDF | View/Open | |
04_acknowledgement.pdf | 287.2 kB | Adobe PDF | View/Open | |
05_contents.pdf | 383.57 kB | Adobe PDF | View/Open | |
06_list of tables and figures.pdf | 195.28 kB | Adobe PDF | View/Open | |
07_abstract.pdf | 179.08 kB | Adobe PDF | View/Open | |
08_chapter-1.pdf | 772.73 kB | Adobe PDF | View/Open | |
09_chapter-2.pdf | 920.61 kB | Adobe PDF | View/Open | |
10_chapter-3.pdf | 1.64 MB | Adobe PDF | View/Open | |
11_chapter-4.pdf | 1.61 MB | Adobe PDF | View/Open | |
12_chapter-5.pdf | 2.47 MB | Adobe PDF | View/Open | |
13_chapter-6.pdf | 1.48 MB | Adobe PDF | View/Open | |
14_chapter-7.pdf | 490.31 kB | Adobe PDF | View/Open | |
15_references.pdf | 344.99 kB | Adobe PDF | View/Open | |
16_list of publications.pdf | 198.03 kB | Adobe PDF | View/Open | |
17_papers published.pdf | 8 MB | Adobe PDF | View/Open | |
80_recommendation.pdf | 896.29 kB | Adobe PDF | View/Open | |
similarity verification certificate and report.pdf | 2.77 MB | Adobe PDF | View/Open |
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