Implementation of Adaptive Network-Based Fuzzy Inference for Hybrid Ground Source Heat Pump
This paper presents a new development of an Adaptive Network-based Fuzzy Inference System (ANFIS) for a Hybrid Ground Source Heat Pump (HGSHP). The HGSHP is equipped with a supplementary heat sink composter to process organic solid waste (OSW), utilizing excess hot air from the condensing unit to ae...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10418919/ |
| _version_ | 1797316089786400768 |
|---|---|
| author | Siwakorn Chuensiri Kanet Katchasuwanmanee Attaporn Wisessint Apiniti Jotisankasa Cheema Soralump Vasutorn Siriyakorn Thongchart Kerdphol Peerayot Sanposh |
| author_facet | Siwakorn Chuensiri Kanet Katchasuwanmanee Attaporn Wisessint Apiniti Jotisankasa Cheema Soralump Vasutorn Siriyakorn Thongchart Kerdphol Peerayot Sanposh |
| author_sort | Siwakorn Chuensiri |
| collection | DOAJ |
| description | This paper presents a new development of an Adaptive Network-based Fuzzy Inference System (ANFIS) for a Hybrid Ground Source Heat Pump (HGSHP). The HGSHP is equipped with a supplementary heat sink composter to process organic solid waste (OSW), utilizing excess hot air from the condensing unit to aerate the compost pile. The Fuzzy Logic Controller (FLC) was developed using data collected by effective sensors installed in the HGSHP system. The main objective is to control the water flow rate with a Variable Speed Drive (VSD) to improve overall system performance. The dataset for ANFIS has been created and trained using MATLAB® software, then implemented on a Raspberry Pi nano-computer with Python coding. This paper compares the performance of ANFIS with two different cases: ANFIS with Triangular Membership Function (TriMF) and ANFIS with Gaussian Membership Function (GaussMF). After implementing ANFIS with TriMF and GaussMF, the average COP during composter operation and system cooling significantly increased. In contrast, the HGSHP system power consumption is sufficiently reduced in both case studies. Moreover, ANFIS also benefits the composting process, as evidenced by the increase in composter operation time, and vice versa for system cooling time. Ultimately, the implementation of ANFIS can improve the HGSHP system performance in both the TriMF and GaussMF cases, with the TriMF case showing a significant improvement in the HGSHP system performance compared to the GaussMF case. |
| first_indexed | 2024-03-08T03:13:12Z |
| format | Article |
| id | doaj.art-d954832744734f4bafc05ab775207a2a |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-08T03:13:12Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-d954832744734f4bafc05ab775207a2a2024-02-13T00:01:25ZengIEEEIEEE Access2169-35362024-01-0112210522106910.1109/ACCESS.2024.336166910418919Implementation of Adaptive Network-Based Fuzzy Inference for Hybrid Ground Source Heat PumpSiwakorn Chuensiri0https://orcid.org/0009-0008-6453-987XKanet Katchasuwanmanee1Attaporn Wisessint2https://orcid.org/0000-0001-6131-8483Apiniti Jotisankasa3https://orcid.org/0000-0002-7087-1472Cheema Soralump4Vasutorn Siriyakorn5https://orcid.org/0009-0008-6929-1455Thongchart Kerdphol6Peerayot Sanposh7https://orcid.org/0000-0003-1129-1862Department of Mechanical Engineering, Kasetsart University, Bangkok, ThailandDepartment of Mechanical Engineering, Kasetsart University, Bangkok, ThailandDepartment of Mechanical Engineering, Kasetsart University, Bangkok, ThailandDepartment of Civil Engineering, Kasetsart University, Bangkok, ThailandDepartment of Environmental Engineering, Kasetsart University, Bangkok, ThailandDepartment of Electrical Engineering, Kasetsart University, Bangkok, ThailandDepartment of Electrical Engineering, Kasetsart University, Bangkok, ThailandDepartment of Electrical Engineering, Kasetsart University, Bangkok, ThailandThis paper presents a new development of an Adaptive Network-based Fuzzy Inference System (ANFIS) for a Hybrid Ground Source Heat Pump (HGSHP). The HGSHP is equipped with a supplementary heat sink composter to process organic solid waste (OSW), utilizing excess hot air from the condensing unit to aerate the compost pile. The Fuzzy Logic Controller (FLC) was developed using data collected by effective sensors installed in the HGSHP system. The main objective is to control the water flow rate with a Variable Speed Drive (VSD) to improve overall system performance. The dataset for ANFIS has been created and trained using MATLAB® software, then implemented on a Raspberry Pi nano-computer with Python coding. This paper compares the performance of ANFIS with two different cases: ANFIS with Triangular Membership Function (TriMF) and ANFIS with Gaussian Membership Function (GaussMF). After implementing ANFIS with TriMF and GaussMF, the average COP during composter operation and system cooling significantly increased. In contrast, the HGSHP system power consumption is sufficiently reduced in both case studies. Moreover, ANFIS also benefits the composting process, as evidenced by the increase in composter operation time, and vice versa for system cooling time. Ultimately, the implementation of ANFIS can improve the HGSHP system performance in both the TriMF and GaussMF cases, with the TriMF case showing a significant improvement in the HGSHP system performance compared to the GaussMF case.https://ieeexplore.ieee.org/document/10418919/Adaptive network-based fuzzy inference systemair-conditioningcomposterhybrid ground source heat pump |
| spellingShingle | Siwakorn Chuensiri Kanet Katchasuwanmanee Attaporn Wisessint Apiniti Jotisankasa Cheema Soralump Vasutorn Siriyakorn Thongchart Kerdphol Peerayot Sanposh Implementation of Adaptive Network-Based Fuzzy Inference for Hybrid Ground Source Heat Pump IEEE Access Adaptive network-based fuzzy inference system air-conditioning composter hybrid ground source heat pump |
| title | Implementation of Adaptive Network-Based Fuzzy Inference for Hybrid Ground Source Heat Pump |
| title_full | Implementation of Adaptive Network-Based Fuzzy Inference for Hybrid Ground Source Heat Pump |
| title_fullStr | Implementation of Adaptive Network-Based Fuzzy Inference for Hybrid Ground Source Heat Pump |
| title_full_unstemmed | Implementation of Adaptive Network-Based Fuzzy Inference for Hybrid Ground Source Heat Pump |
| title_short | Implementation of Adaptive Network-Based Fuzzy Inference for Hybrid Ground Source Heat Pump |
| title_sort | implementation of adaptive network based fuzzy inference for hybrid ground source heat pump |
| topic | Adaptive network-based fuzzy inference system air-conditioning composter hybrid ground source heat pump |
| url | https://ieeexplore.ieee.org/document/10418919/ |
| work_keys_str_mv | AT siwakornchuensiri implementationofadaptivenetworkbasedfuzzyinferenceforhybridgroundsourceheatpump AT kanetkatchasuwanmanee implementationofadaptivenetworkbasedfuzzyinferenceforhybridgroundsourceheatpump AT attapornwisessint implementationofadaptivenetworkbasedfuzzyinferenceforhybridgroundsourceheatpump AT apinitijotisankasa implementationofadaptivenetworkbasedfuzzyinferenceforhybridgroundsourceheatpump AT cheemasoralump implementationofadaptivenetworkbasedfuzzyinferenceforhybridgroundsourceheatpump AT vasutornsiriyakorn implementationofadaptivenetworkbasedfuzzyinferenceforhybridgroundsourceheatpump AT thongchartkerdphol implementationofadaptivenetworkbasedfuzzyinferenceforhybridgroundsourceheatpump AT peerayotsanposh implementationofadaptivenetworkbasedfuzzyinferenceforhybridgroundsourceheatpump |