Intelligent Environment Monitoring and Control / (Record no. 607354)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 02537nam a22001577a 4500 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 629.8 |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Faiz, Muhammad Faizan |
| 245 ## - TITLE STATEMENT | |
| Title | Intelligent Environment Monitoring and Control / |
| Statement of responsibility, etc. | Muhammad Faizan Faiz |
| 264 ## - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Place of production, publication, distribution, manufacture | Islamabad : |
| Name of producer, publisher, distributor, manufacturer | SMME- NUST; |
| Date of production, publication, distribution, manufacture, or copyright notice | 2023. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 73p. |
| Other physical details | Soft Copy |
| Dimensions | 30cm |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | Heating, Ventilation, and Air Conditioning (HVAC) systems play a vital role in building<br/>energy management by controlling the indoor environment and ensuring the occupant’s comfort.<br/>These systems are responsible for regulating the temperature and air quality inside buildings,<br/>thereby creating a comfortable and healthy indoor environment for occupants. However, the<br/>energy consumption of HVACs contributes significantly towards overall energy usage of a<br/>building and carbon footprint creating a challenge for building energy management. To address<br/>this challenge, this research proposes the development of a predictive model for HVAC<br/>temperature forecasting using Machine Learning (ML) algorithms to optimize energy efficiency<br/>while maintaining thermal comfort in buildings. The study focuses on comparing the performance<br/>of Transformer Neural Networks and CNN-LSTM, a seq2seq model combining Convolutional<br/>Neural Networks (CNN) and Long-Short Term Memory (LSTM) on multiple forecasting horizons.<br/>Both models are validated using data obtained from multiple devices which are deployed in a room<br/>verified by feedback survey forms filled by occupants. The transformer model outperformed,<br/>achieving an R2 score of 0.936 at a 1 minute forecasting horizon, surpassing the performance of<br/>CNN-LSTM model at all tested forecasting horizons. The transformer model yielded significant<br/>energy savings thereby reducing energy consumption by almost 50 percent compared to the nonAI conventional methods, particularly at forecasting horizons of 1 minute and 60 minutes, while<br/>the occupant survey also favored a 60-minute forecasting horizon indicating that the proposed<br/>model can effectively balance energy efficiency with occupant comfort. The performance of<br/>transformer model particularly with a 60-minute forecasting horizon underscores its potential to<br/>optimize energy efficiency while ensuring thermal comfort in building energy management<br/>systems. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | MS Robotics and Intelligent Machine Engineering |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Supervisor : Dr. Sara Ali |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="http://10.250.8.41:8080/xmlui/handle/123456789/34400">http://10.250.8.41:8080/xmlui/handle/123456789/34400</a> |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | |
| Koha item type | Thesis |
| Withdrawn status | Permanent Location | Current Location | Shelving location | Date acquired | Full call number | Barcode | Koha item type |
|---|---|---|---|---|---|---|---|
| School of Mechanical & Manufacturing Engineering (SMME) | School of Mechanical & Manufacturing Engineering (SMME) | E-Books | 12/13/2023 | 629.8 | SMME-TH-862 | Thesis |