Charging water load prediction with a multilayer perceptron for an efficient facility management and maintenance of thermal-energy-storage air-conditioning
This research addresses the challenges in Thermal-Energy-Storage-Air-Conditioning (TES-AC) systems by developing a machine learning model for predicting the necessary water volume for chilling. TES-AC technology, utilizing thermal energy storage tanks, offers substantial benefits such as reduced chi...
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| Format: | Thesis (University of Nottingham only) |
| Language: | English |
| Published: |
2024
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| Online Access: | https://eprints.nottingham.ac.uk/77243/ |
| _version_ | 1848800977247797248 |
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| author | Sanzana, Mirza Rayana |
| author_facet | Sanzana, Mirza Rayana |
| author_sort | Sanzana, Mirza Rayana |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | This research addresses the challenges in Thermal-Energy-Storage-Air-Conditioning (TES-AC) systems by developing a machine learning model for predicting the necessary water volume for chilling. TES-AC technology, utilizing thermal energy storage tanks, offers substantial benefits such as reduced chiller operation, cost savings, and lower carbon emissions. However, determining the optimal chilled water volume poses challenges. The primary objective is to design a machine learning model leveraging Multilayer Perceptron (MLP) for predicting water load, incorporating input variables like weather forecasts, day of the week, and occupancy data.
The study validates the impact of weather data on chilled water volume, demonstrating its efficacy in prediction. The MLP-based model is fine-tuned through hyperparameter optimization, achieving a remarkable accuracy of 93.4%. The model provides specific water volume ranges, minimizing errors and aiding facility managers in informed decision-making to minimize disruptions.
Technical significance lies in the model's flexibility, allowing retraining for diverse TES-AC plants without requiring specific sensor inputs. This adaptability promotes widespread TES-AC adoption, contributing to environmentally friendly practices in building construction. The integration of the model into facility management software enhances predictive capabilities while offering common features, ensuring seamless incorporation into existing systems.
The research aligns with Sustainable Development Goals, particularly Goals 11, 12, and 13, emphasizing sustainable cities, responsible consumption, and climate action. By focusing on technical problem-solving, addressing challenges, and emphasizing the social significance through Sustainable Development Goals, this research provides a comprehensive solution to enhance TES-AC efficiency, thereby contributing to greener and more sustainable urban environments. |
| first_indexed | 2025-11-14T21:00:08Z |
| format | Thesis (University of Nottingham only) |
| id | nottingham-77243 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T21:00:08Z |
| publishDate | 2024 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-772432024-03-11T04:30:12Z https://eprints.nottingham.ac.uk/77243/ Charging water load prediction with a multilayer perceptron for an efficient facility management and maintenance of thermal-energy-storage air-conditioning Sanzana, Mirza Rayana This research addresses the challenges in Thermal-Energy-Storage-Air-Conditioning (TES-AC) systems by developing a machine learning model for predicting the necessary water volume for chilling. TES-AC technology, utilizing thermal energy storage tanks, offers substantial benefits such as reduced chiller operation, cost savings, and lower carbon emissions. However, determining the optimal chilled water volume poses challenges. The primary objective is to design a machine learning model leveraging Multilayer Perceptron (MLP) for predicting water load, incorporating input variables like weather forecasts, day of the week, and occupancy data. The study validates the impact of weather data on chilled water volume, demonstrating its efficacy in prediction. The MLP-based model is fine-tuned through hyperparameter optimization, achieving a remarkable accuracy of 93.4%. The model provides specific water volume ranges, minimizing errors and aiding facility managers in informed decision-making to minimize disruptions. Technical significance lies in the model's flexibility, allowing retraining for diverse TES-AC plants without requiring specific sensor inputs. This adaptability promotes widespread TES-AC adoption, contributing to environmentally friendly practices in building construction. The integration of the model into facility management software enhances predictive capabilities while offering common features, ensuring seamless incorporation into existing systems. The research aligns with Sustainable Development Goals, particularly Goals 11, 12, and 13, emphasizing sustainable cities, responsible consumption, and climate action. By focusing on technical problem-solving, addressing challenges, and emphasizing the social significance through Sustainable Development Goals, this research provides a comprehensive solution to enhance TES-AC efficiency, thereby contributing to greener and more sustainable urban environments. 2024-03-09 Thesis (University of Nottingham only) NonPeerReviewed application/pdf en cc_by https://eprints.nottingham.ac.uk/77243/1/Thesis-014757-Mirza%20Rayana%20Sanzana.pdf Sanzana, Mirza Rayana (2024) Charging water load prediction with a multilayer perceptron for an efficient facility management and maintenance of thermal-energy-storage air-conditioning. PhD thesis, University of Nottingham. multilayer perceptron thermal energy storage air conditioning machine learning neural networks |
| spellingShingle | multilayer perceptron thermal energy storage air conditioning machine learning neural networks Sanzana, Mirza Rayana Charging water load prediction with a multilayer perceptron for an efficient facility management and maintenance of thermal-energy-storage air-conditioning |
| title | Charging water load prediction with a multilayer perceptron for an efficient facility management and maintenance of thermal-energy-storage air-conditioning |
| title_full | Charging water load prediction with a multilayer perceptron for an efficient facility management and maintenance of thermal-energy-storage air-conditioning |
| title_fullStr | Charging water load prediction with a multilayer perceptron for an efficient facility management and maintenance of thermal-energy-storage air-conditioning |
| title_full_unstemmed | Charging water load prediction with a multilayer perceptron for an efficient facility management and maintenance of thermal-energy-storage air-conditioning |
| title_short | Charging water load prediction with a multilayer perceptron for an efficient facility management and maintenance of thermal-energy-storage air-conditioning |
| title_sort | charging water load prediction with a multilayer perceptron for an efficient facility management and maintenance of thermal-energy-storage air-conditioning |
| topic | multilayer perceptron thermal energy storage air conditioning machine learning neural networks |
| url | https://eprints.nottingham.ac.uk/77243/ |