Application of an AI-based optimal control framework in smart buildings using borehole thermal energy storage combined with wastewater heat recovery

This article introduces a cutting-edge energy system to meet the residential building's high energy demands while lowering emissions and related operating expenses. The central concept revolves around rule-based control strategies designed to exploit wastewater's heat, accounting for a sub...

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Published in:JOURNAL OF ENERGY STORAGE
Main Authors: Alshamrani, Ali; Abbas, Hasan Ali; Alkhayer, Alhussein G.; Mausam, Kuwar; Abdullah, Shivan Ismael; Alsehli, Mishal; Rajab, Husam; Ahmed, Mohsen; El-Shafay, A. S.; Kassim, Murizah
Format: Article
Language:English
Published: ELSEVIER 2024
Subjects:
Online Access:https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001322208400001
author Alshamrani
Ali; Abbas
Hasan Ali; Alkhayer
Alhussein G.; Mausam
Kuwar; Abdullah
Shivan Ismael; Alsehli
Mishal; Rajab
Husam; Ahmed
Mohsen; El-Shafay
A. S.; Kassim
Murizah
spellingShingle Alshamrani
Ali; Abbas
Hasan Ali; Alkhayer
Alhussein G.; Mausam
Kuwar; Abdullah
Shivan Ismael; Alsehli
Mishal; Rajab
Husam; Ahmed
Mohsen; El-Shafay
A. S.; Kassim
Murizah
Application of an AI-based optimal control framework in smart buildings using borehole thermal energy storage combined with wastewater heat recovery
Energy & Fuels
author_facet Alshamrani
Ali; Abbas
Hasan Ali; Alkhayer
Alhussein G.; Mausam
Kuwar; Abdullah
Shivan Ismael; Alsehli
Mishal; Rajab
Husam; Ahmed
Mohsen; El-Shafay
A. S.; Kassim
Murizah
author_sort Alshamrani
spelling Alshamrani, Ali; Abbas, Hasan Ali; Alkhayer, Alhussein G.; Mausam, Kuwar; Abdullah, Shivan Ismael; Alsehli, Mishal; Rajab, Husam; Ahmed, Mohsen; El-Shafay, A. S.; Kassim, Murizah
Application of an AI-based optimal control framework in smart buildings using borehole thermal energy storage combined with wastewater heat recovery
JOURNAL OF ENERGY STORAGE
English
Article
This article introduces a cutting-edge energy system to meet the residential building's high energy demands while lowering emissions and related operating expenses. The central concept revolves around rule-based control strategies designed to exploit wastewater's heat, accounting for a substantial amount of the total heating demand in residential buildings. The system is also integrated with heat pumps to recover the radiators' return water energy to preheat the ventilation air passively. Naturally-driven borehole thermal energy storage is added for post-cooling the ventilation air to incorporate higher renewable shares in building energy systems while lowering CO2 emission. TRNSYS and MATLAB software are used to design, control, optimize, and assess the system's performance from technological, environmental, and economic perspectives with the aid of artificial intelligence. According to the results, the proposed smart system is superior due to higher efficiency and lower energy cost while saving CO2 emissions compared to the conventional ventilation system. The results further show that the suggested smart integration effectively meets needs and reduces dependency on the centralized energy network by independently recovering or producing thermal energy through the developed control framework. According to the artificial neural network-assisted optimization outcomes, energy cost, total cost, and CO2 index are reduced by about 41.5 USD.MWh(-1), 10,306 USD, and 1.7 kg.MWh(-1), respectively. Furthermore, there is an annual extra 3 kWh of improved energy generation thanks to the optimal storage/usage of energy. The results further show that strategic optimization techniques like reducing mass flow rate and borehole depth perform well to maximize efficiency while reducing environmental impact and saving significant money.
ELSEVIER
2352-152X
2352-1538
2024
101

10.1016/j.est.2024.113824
Energy & Fuels

WOS:001322208400001
https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001322208400001
title Application of an AI-based optimal control framework in smart buildings using borehole thermal energy storage combined with wastewater heat recovery
title_short Application of an AI-based optimal control framework in smart buildings using borehole thermal energy storage combined with wastewater heat recovery
title_full Application of an AI-based optimal control framework in smart buildings using borehole thermal energy storage combined with wastewater heat recovery
title_fullStr Application of an AI-based optimal control framework in smart buildings using borehole thermal energy storage combined with wastewater heat recovery
title_full_unstemmed Application of an AI-based optimal control framework in smart buildings using borehole thermal energy storage combined with wastewater heat recovery
title_sort Application of an AI-based optimal control framework in smart buildings using borehole thermal energy storage combined with wastewater heat recovery
container_title JOURNAL OF ENERGY STORAGE
language English
format Article
description This article introduces a cutting-edge energy system to meet the residential building's high energy demands while lowering emissions and related operating expenses. The central concept revolves around rule-based control strategies designed to exploit wastewater's heat, accounting for a substantial amount of the total heating demand in residential buildings. The system is also integrated with heat pumps to recover the radiators' return water energy to preheat the ventilation air passively. Naturally-driven borehole thermal energy storage is added for post-cooling the ventilation air to incorporate higher renewable shares in building energy systems while lowering CO2 emission. TRNSYS and MATLAB software are used to design, control, optimize, and assess the system's performance from technological, environmental, and economic perspectives with the aid of artificial intelligence. According to the results, the proposed smart system is superior due to higher efficiency and lower energy cost while saving CO2 emissions compared to the conventional ventilation system. The results further show that the suggested smart integration effectively meets needs and reduces dependency on the centralized energy network by independently recovering or producing thermal energy through the developed control framework. According to the artificial neural network-assisted optimization outcomes, energy cost, total cost, and CO2 index are reduced by about 41.5 USD.MWh(-1), 10,306 USD, and 1.7 kg.MWh(-1), respectively. Furthermore, there is an annual extra 3 kWh of improved energy generation thanks to the optimal storage/usage of energy. The results further show that strategic optimization techniques like reducing mass flow rate and borehole depth perform well to maximize efficiency while reducing environmental impact and saving significant money.
publisher ELSEVIER
issn 2352-152X
2352-1538
publishDate 2024
container_volume 101
container_issue
doi_str_mv 10.1016/j.est.2024.113824
topic Energy & Fuels
topic_facet Energy & Fuels
accesstype
id WOS:001322208400001
url https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001322208400001
record_format wos
collection Web of Science (WoS)
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