The DOI matches its Crossref record.
Artificial neural networks and adaptive neuro-fuzzy assessments for ground-coupled heat pump system
Open accessAbstract
This article present a comparison of artificial neural network (ANN) and adaptive neuro-fuzzy inference systems (ANFIS) applied for modelling a ground-coupled heat pump system (GCHP). The aim of this study is predicting system performance related to ground and air (condenser inlet and outlet) temperatures by using desired models. Performance forecasting is the precondition for the optimal design and energy-saving operation of air-conditioning systems. So obtained models will help the system designer to realize this precondition. The most suitable algorithm and neuron number in the hidden layer are found as Levenberg–Marquardt (LM) with seven neurons for ANN model whereas the most suitable membership function and number of membership functions are found as Gauss and two, respectively, for ANFIS model. The root-mean squared (RMS) value and the coefficient of variation in percent (cov) value are 0.0047 and 0.1363, respectively. The absolute fraction of variance (R2) is 0.9999 which can be considered as very promising. This paper shows the appropriateness of ANFIS for the quantitative modeling of GCHP systems.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Experimental evaluation of using various renewable energy sources for heating a greenhouse
Esen, M., & Yuksel, T. (2013) · Energy and Buildings · doi:10.1016/j.enbuild.2013.06.018 · Cited by 939
Self-citationNoorchenarboo, M., & Grolinger, K. (2025) · Energy and Buildings · doi:10.1016/j.enbuild.2024.115177 · Cited by 38
Metadata
- Authors
- Esen, H., Inalli, M., Sengur, A., & Esen, M.
- Venue
- Energy and Buildings
- Publisher
- Elsevier BV
- Published
- 2008-01
- Volume
- 40
- Issue
- 6
- Pages
- 1074-1083
- ISSN
- 0378-7788 (ISSN-L)
- OpenAlex
- W2083130827
- Cited by
- 267
- References
- 0
- FWCI
- 1.73
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Artificial neural networks and adaptive neuro-fuzzy assessments for ground-coupled heat pump system. https://app.cochat.ai/papers/10.1016/j.enbuild.2007.10.002
- The paper
- Esen, H., Inalli, M., Sengur, A., & Esen, M. (2008). Artificial neural networks and adaptive neuro-fuzzy assessments for ground-coupled heat pump system. Energy and Buildings. https://doi.org/10.1016/j.enbuild.2007.10.002
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
