Performance Assessment Model Research Articles

Low Complexity Energy Disaggregation Algorithm for Non-intrusive Load Monitoring

Energy Disaggregation is the efficient technique to detect the energy profile of individual electric load by disaggregating the overall power consumption. The benefits of energy disaggregation are not only limited to residents but also helps to improve the building efficiency through load identification process. The idea of this paper is to provide a widespread review of energy disaggregation and present a scheme for non-intrusive load monitoring at a building level model. As the scheme involves a simple regression model with better accuracy, it is less complex to achieve energy disaggregation for the real time load identification in educational institution. Various metrics involved in the assessment of model performance like accuracy, standard errors values and computation time were evaluated and demonstrated for validity. A broad investigation is done for building level energy disaggregation and probable elucidations were discussed for future research initiation.

Performance assessment of rainfall forecasting models for urban Guwahati City using machine learning techniques and singular spectrum analysis

Abstract Rainfall forecasting is pivotal for issuing flood warnings and flood management. Machine learning (ML) models are popular as they can effectively manage extensive data and non-stationarity of the data series with improved performance and cost-effective solutions. However, more studies are required to understand the dynamic characteristics of rainfall. This study proposes a hybrid model and demonstrates its efficiency in improving the daily rainfall forecast. Singular spectrum analysis (SSA) was used as a data pre-processing technique (successfully removing and identifying the nature of noise) and coupled with ML models (artificial neural network (ANN) and support vector machine (SVM)) improving daily scale forecast. Since the current response of the hydrological system depends on previous responses, rainfall at the next time step was derived with the previous 2-, 3-, 5- and 7-day rainfall. Study shows that the first eigen vector derived through SSA is the trend component which has a maximum contribution of 18.75%, suggesting it can explain 18.75% of the given rainfall series. The 16.42% (eigen vector 2-9) contributes to periodicity, with period of 1 year, 6 months, and 4 months within the data. Conclusively, the hybrid SSA-ML model outperformed the single model for daily rainfall forecasts.

Assessing the performance of urban heritage conservation projects – influencing factors, aspects and priority weights

Heritage conservation in urban areas involves complex systems often faced with the dilemmas of maintaining the built form’s historical character, improving infrastructure, and managing development through stakeholder cooperation. At present, the performance of any conservation project is solely vested in conserving the built fabric. Evaluation tools for urban heritage conservation projects do not have provisions for measuring the subjective value of stakeholders who are part of the heritage setting. This study tries to identify and prioritise the factors that need to be considered when developing a conservation project performance assessment model for an urban heritage conservation project from the perspective of experts that can be further evaluated from the perspective of stakeholders. As these complex systems can be better viewed in the context of developing countries, the case of India is adopted. From a literature review, factors that contribute to the outcome of an urban heritage conservation project were identified and categorised into six aspects: the physical, social, economic, cultural, political, and continuity aspects. Through an expert survey, the factors that constituted each aspect were filtered using the feature selection method of correlation to avoid factors that may seem related. The factors under each aspect were ranked using a weighted average ranking method to identify the most prioritised factors determining the outcome of an urban heritage conservation project. The priority weights of the aspects were calculated using Saaty’s analytic hierarchy process. The results show that the cultural aspect was the most important aspect, followed by the continuity aspect. The social and physical aspects were prioritised similarly, followed by the economic and political aspects. This study is distinctive because it identifies the influential factors that can help develop a conservation project performance assessment model for an urban heritage conservation project.

Augmentation of Additional Arabic Dataset for Jawi Writing and Classification Using Deep Learning

This research aims to create an additional dataset containing Arabic characters for writing Jawi script and to train classification models using deep learning architectures such as InceptionV3 and ResNet34. The initial stage of the study involves digital image processing to obtain the additional Arabic character dataset from several sources, including HMBD, AHAWP, and HUCD, encompassing various connected and disconnected forms of Jawi script. Image processing includes steps such as preprocessing to enhance image quality, segmentation to separate Arabic characters from the background, and augmentation to increase dataset variability. Once the dataset is formed, we train the models using appropriate training data for each InceptionV3 and ResNet34 architecture. The classification evaluation results indicate that the model with ResNet34 architecture achieved the best performance with an accuracy of 96%. This model successfully recognizes Jawi script accurately and consistently, even for classes with similar shapes. The main contribution of this research is the availability of the additional Arabic character dataset that can be utilized for Jawi script recognition and performance assessment of various deep learning models. The study also emphasizes the importance of selecting the appropriate architecture for specific character recognition tasks. The research findings affirm that the model with ResNet34 architecture has excellent capability in recognizing the additional Arabic characters for writing Jawi. The results of this research have the potential to support further developments in Jawi character recognition applications and provide valuable insights for researchers in the field of character recognition sourced from Arabic characters. Dataset augmentation results can be accessed at https://singkat.usk.ac.id/g/En0skCKGAR

Performance assessment of college students higher vocational mathematics education using fuzzy evaluation model

Higher vocational mathematics education is advanced and related to real-time applications providing vast knowledge. Teaching and training peculiar mathematical problems improve their educational and career-focused performance. Therefore optimal performance assessment methods are required for reducing the lack of knowledge in mathematics learning. This article hence introduces an Articulated Performance Assessment Model (APAM) for consenting mathematics assessment. In this model, fuzzy optimization is used for consenting different factors such as understandability, problem-solving, and replication. The understandability is identified using similar problem progression by the students, whereas replication is the application of problem-solving skills for articulated mathematical models. From perspectives, problem-solving and solution extraction is the theme that has to be met by the student. The assessments hence generate a perplexed outcome due to which the fuzzy optimization for high and low-level understandability is evaluated. The optimization recommends the change in varying steps in problem explanation and iterated replication for leveraging the students’ performance. This process swings between irrelevant and crisp inputs during fuzzification. In this process, the crisp inputs are the maximum replications produced by the students for better understanding. Therefore, the proposed model is evaluated using efficiency, maximum replication, fuzzification rate, and analytical time.

AI and machine learning in medical imaging: key points from development to translation.

Innovation in medical imaging artificial intelligence (AI)/machine learning (ML) demands extensive data collection, algorithmic advancements, and rigorous performance assessments encompassing aspects such as generalizability, uncertainty, bias, fairness, trustworthiness, and interpretability. Achieving widespread integration of AI/ML algorithms into diverse clinical tasks will demand a steadfast commitment to overcoming issues in model design, development, and performance assessment. The complexities of AI/ML clinical translation present substantial challenges, requiring engagement with relevant stakeholders, assessment of cost-effectiveness for user and patient benefit, timely dissemination of information relevant to robust functioning throughout the AI/ML lifecycle, consideration of regulatory compliance, and feedback loops for real-world performance evidence. This commentary addresses several hurdles for the development and adoption of AI/ML technologies in medical imaging. Comprehensive attention to these underlying and often subtle factors is critical not only for tackling the challenges but also for exploring novel opportunities for the advancement of AI in radiology.

Abstract B092: Development of predictive models for expression of a tumor specific biomarker and CD3 on H&E digital slides

Abstract Characterization of a patient’s tumor microenvironment is fundamental to advancing translational strategies in immuno-oncology. Histopathological evaluation of tissue slides from patients can provide this invaluable information, informing disease heterogeneity, tumor contexture, and target expression - all important to identifying patients more likely to benefit from therapy. However, availability of sufficient tissue is often a challenge to produce such a comprehensive tumor characterization. Measurement of target expression using an H&E slide could greatly reduce the usage of tissue for IHC making sections available for other investigative purposes. Here, an AI-driven predictive model was developed on H&E digital slides to simulate the expression of a tumor specific biomarker and CD3 across 4 different tumor types. Four epithelial tumor types (TNBC, NSCLC, ovarian, and cervical cancer) consisted of 400 H&E slides, 400 CD3 slides and 400 tumor specific biomarker IHC slides (100 cases per tumor) were used for this study. The Bio-AI Predict-X platform was utilized for the optimization of separate tumor models using pathology annotations as basis for development. The network output included tumor tiles as well as the tiles from adjacent stromal/normal tissue. Models were optimized until an AUC > 0.8 was achieved and prediction results were approved by the combined pathology teams. Predict-X was used for IHC marker quantification on a tile basis for both CD3 and the tumor specific biomarker slides with significant correlation to manual counts (p<0.001, r>0.8). Images from each case were co-registered so that ground truth for each tile with the IHC count was used in the predictive model. Tiles from tumor and corresponding adjacent stroma/normal tissue from all cases were subdivided into 3 groups for predictive model development. A training, validation, and test set were used, the latter of which was not used for development, only as a final assessment of model performance. A pan-tumor predictive model was developed for CD3 IHC on TNBC, cervical and ovarian cancers. The test set AUC values on cases from all 4 tumors were >0.7. Since the tumor specific biomarker stain was specific to tumor morphology, 4 separate predictive models were developed - one for each of the indications used with a test AUC >0.86. These findings suggest that deep learning can be used as a complementary method to prescreen H&E-stained images to enhance the detection rate of the tumor specific biomarker and CD3 positivity in patient tumors. Additional work will involve the optimization of these models for implementation in a clinical setting. Citation Format: Alan Jerusalmi, Krishna Bairavi, Ayushi Shah, Tom Chittenden, Mike Bonham, Chung-Wein Lee, Brandon Higgs, Anantharaman Muthuswamy. Development of predictive models for expression of a tumor specific biomarker and CD3 on H&E digital slides [abstract]. In: Proceedings of the AACR Special Conference on Ovarian Cancer; 2023 Oct 5-7; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(5 Suppl_2):Abstract nr B092.

Optimizing Neural Networks for Enhanced Material Property Predictions: Insights from Bulk Modulus Analysis

This research presents a deep learning model designed to accurately compute material properties, with a specific focus on the bulk modulus. This study places significant emphasis on hyperparameter optimization, involving adjustments to batch size, learning rate, hidden layer, and neuron count. The dataset, comprising 7107 diverse materials, undergoes thorough preprocessing, which includes outlier removal and the extraction of elemental property descriptors using the matminer library and the Magpie dataset. The core model utilized in this research is an Artificial Neural Network (ANN), with the descriptors serving as crucial input features. Model performance assessment is conducted by using the Mean Absolute Error (MAE) as a quantitative metric, providing insights into predictive accuracy. This research also employs sensitivity analysis to scrutinize the significance of 132 features in predicting the bulk modulus property, contributing to an understanding of material behavior dynamics and facilitating model optimization. The results highlight the impact of neuron count, layer depth, learning rate, and batch size on prediction accuracy. Furthermore, feature importance analysis underscores the critical role of specific material properties, with mean covalent radius emerging as the most influential factor in predicting the bulk modulus. These discoveries provide guidelines for optimizing neural network configurations and material property descriptors for predicting material elasticity.

Prognostic factors and prediction models for hospitalisation and all-cause mortality in adults presenting to primary care with a lower respiratory tract infection: a systematic review

ObjectiveTo identify and synthesise relevant existing prognostic factors (PF) and prediction models (PM) for hospitalisation and all-cause mortality within 90 days in primary care patients with acute lower respiratory tract...

WITHDRAWN: Evaluating the generation capabilities of large Chinese language models

This paper unveils CG-Eval, the first-ever comprehensive and automated evaluation framework designed for assessing the generative capabilities of large Chinese language models across a spectrum of academic disciplines. CG-Eval stands out for its automated process, which critically assesses models based on their proficiency in generating precise and contextually relevant responses to a diverse array of questions within six key domains: Science and Engineering, Humanities and Social Sciences, Mathematical Calculations, Medical Practitioner Qualification Examination, Judicial Examination, and Certified Public Accountant Examination. Alongside this, we introduce Gscore, an innovative composite index developed from a weighted sum of multiple metrics. Gscore uniquely automates the quality measurement of a model's text generation against reference standards, providing a detailed and nuanced assessment of model performance. This automation not only enhances the efficiency and scalability of the evaluation process but also ensures objective and consistent assessment across various models. The detailed test data and results, highlighting the robust capabilities and comparative performance of the evaluated models, are accessible at http://cgeval.besteasy.com/.

DEBCM: Deep Learning-Based Enhanced Breast Invasive Ductal Carcinoma Classification Model in IoMT Healthcare Systems.

Accurate breast cancer (BC) diagnosis is a difficult task that is critical for the proper treatment of BC in IoMT (Internet of Medical Things) healthcare systems. This paper proposes a convolutional neural network (CNN)-based diagnosis method for detecting early-stage breast cancer. In developing the proposed method, we incorporated the CNN model for the invasive ductal carcinoma (IDC) classification using breast histology image data. We have incorporated transfer learning (TL) and data augmentation (DA) mechanisms to improve the CNN model's predictive outcomes. For the fine-tuning process, the CNN model was trained with breast histology image data. Furthermore, the held-out cross-validation method for best model selection and hyper-parameter tuning was incorporated. In addition, various performance evaluation metrics for model performance assessment were computed. The experimental results confirmed that the proposed model outperformed the baseline models across all evaluation metrics, achieving 99.04% accuracy. We recommend the proposed method for early recognition of BC in IoMT healthcare systems due to its high performance.

Analysing the Nexus between the Financial Sector and Economic Growth in Nigeria: A Comparative Investigation using, BVAR, Linear Regression (OLS), and PPML Models

Aims: This study aims to analyze the complex relationship between the financial sector and economic growth in Nigeria. The study aims to provide comprehensive insights into this nexus by employing a comparative investigation using three distinct models: Linear Regression, Poisson Pseudo Maximum Likelihood (PPML), and Bayesian Vector Autoregression (BVAR).
 Methodology: The study then applied three different models, with a specific focus on the BVAR(2) model, supported by various diagnostic tests and stability assessments. The inclusion of Linear regression analysis and Poisson Pseudo Maximum Likelihood Estimator (PPML) enhances the depth of the study, providing nuanced insights into the impact of specific financial sector variables on economic growth.
 Results: The BVAR (2) model emerges as the optimal choice, demonstrating its reliability in capturing dynamic interactions and offering a powerful tool for policymakers. Specific results, such as the significant negative impact of D(CPS) in the regression analysis and the high R-squared in PPML, provide actionable insights into areas requiring policy interventions and underscore the substantial contribution of the financial sector to economic growth.
 Conclusion: The comparative assessment of model performances, favoring the BVAR model, guides future research and policy considerations, providing a reliable framework for further investigations. The study's insights are positioned as valuable for policymakers seeking to enhance economic growth through strategic interventions in the financial sector. Overall, the abstract succinctly encapsulates the aims, methodology, results, and concluding implications of the study on the nexus between the financial sector and economic growth in Nigeria.

Concept analysis of end-of-life care competency of long-term-care-hospital nurses: Using a hybrid model

Purpose: This study identified the attributes and indicators of end-of-life nursing competency in long-term-care-hospital nurses and clarified the definition of the concept. Methods: The Competency Outcomes Performance Assessment model was used as a conceptual framework, and conceptual analysis was performed using the hybrid model presented by Schwartz-Barcott and Kim. In the theoretical phase, the attributes of end-of-life care competencies were explored through a literature review. In the fieldwork phase, focus group interview data were analyzed to derive the attributes of nursing competency at the end of life. In the final analysis phase, the attributes and indicators of end-of-life care competency were compared, analyzed, and integrated. Results: The attributes of end-of-life care competency among nurses in long-term care hospitals included comprehensive symptom management, effective communication, situational response, patient-centered care, information provision and education, resource management, demonstrating leadership, and professional development. Conclusion: End-of-life care competency in long-term-care-hospital nurses can be defined as a comprehensive set of competencies that includes symptom management, situational adaptation, effective communication, resource utilization, leadership, patient-centered care, meeting the needs of patients’ families through adequate information provision and education, and enhancing individual nursing capabilities through professional development. These results can serve as a foundation for developing tools to measure end-of-life care competencies among nurses in long-term care hospitals.

Developing a novel image marker to predict the clinical outcome of neoadjuvant chemotherapy (NACT) for ovarian cancer patients

ObjectiveNeoadjuvant chemotherapy (NACT) is one kind of treatment for advanced stage ovarian cancer patients. However, due to the nature of tumor heterogeneity, the clinical outcomes to NACT vary significantly among different subgroups. Partial responses to NACT may lead to suboptimal debulking surgery, which will result in adverse prognosis. To address this clinical challenge, the purpose of this study is to develop a novel image marker to achieve high accuracy prognosis prediction of NACT at an early stage. MethodsFor this purpose, we first computed a total of 1373 radiomics features to quantify the tumor characteristics, which can be grouped into three categories: geometric, intensity, and texture features. Second, all these features were optimized by principal component analysis algorithm to generate a compact and informative feature cluster. This cluster was used as input for developing and optimizing support vector machine (SVM) based classifiers, which indicated the likelihood of receiving suboptimal cytoreduction after the NACT treatment. Two different kernels for SVM algorithm were explored and compared. A total of 42 ovarian cancer cases were retrospectively collected to validate the scheme. A nested leave-one-out cross-validation framework was adopted for model performance assessment. ResultsThe results demonstrated that the model with a Gaussian radial basis function kernel SVM yielded an AUC (area under the ROC [receiver characteristic operation] curve) of 0.806 ± 0.078. Meanwhile, this model achieved overall accuracy (ACC) of 83.3%, positive predictive value (PPV) of 81.8%, and negative predictive value (NPV) of 83.9%. ConclusionThis study provides meaningful information for the development of radiomics based image markers in NACT treatment outcome prediction.

Multi-objective assessment of hydrological model performances using Nash–Sutcliffe and Kling–Gupta efficiencies on a worldwide large sample of watersheds

We introduce a new diagnosis tool that is well suited to analyzing simulation results over large samples of watersheds. It consists of a modification of the classical Taylor diagram to simultaneously visualize several error components (based on bias, standard deviation or squared errors) that are commonly used in efficiency criteria (such as the Nash–Sutcliffe efficiency (NSE) or the Kling–Gupta efficiency (KGE)) to evaluate hydrological model performance. We propose a methodological framework that explicitly links the graphical and numerical evaluation approaches, and show how they can be usefully combined to visually interpret numerical experiments conducted on large datasets. The approach is illustrated using results obtained by testing two rainfall-runoff models on a sample of 2050 watersheds from 8 countries and calibrated with two alternative objective functions (NSE and KGE). The assessment tool clearly highlights well-documented problems related to the use of the NSE for the calibration of rainfall-runoff models, which arise due to interactions between the ratio of simulated to observed standard deviations and the correlation coefficient. We also illustrate the negative impacts of classical mathematical transformations (square root) applied to streamflow when employing NSE and KGE as metrics for model calibration.

Predictions of Early Hospitalization of Diabetes Patients Based on Deep Learning: A Review

Unmanaged diabetes can result in a number of complications that need to be hospitalised. Diabetes is a chronic disorder. With preventive treatment, outcomes may be improved through early prediction of diabetes-related hospitalisation using data-driven algorithms. Here, we examine recent advances in deep learning methods for anticipating readmissions and unexpected hospital stays in adult patients with diabetes. Firstly, we present an overview of the main factors that indicate the need for hospitalisation due to diabetic complications. The research on hospitalisation risk prediction using structured health data, such as demographics, prescriptions, test results, etc., using conventional machine learning techniques is then summarised. Using data from insurance claims and electronic health records, we then examine current research that has used deep learning models. It is emphasised that longitudinal data can be included using recurrent neural networks. Model architectures, training methods, and important data modalities are covered. The assessment also addresses deployment difficulty and the model's performance assessment on real-world datasets. Ultimately, potential paths forward include hybrid models that integrate data diversity, explainable predictions, and clinical knowledge. In order to provide evidence-based predictions of the risk of hospitalisation and readmission for diabetes patients, we examine the potential and constraints of recently developed deep learning algorithms in this review.

A Standardized Benchmarking Framework to Assess Downscaled Precipitation Simulations

Abstract Presently, there is no standardized framework or metrics identified to assess regional climate model precipitation output. Because of this, it can be difficult to make a one-to-one comparison of their performance between regions or studies, or against coarser-resolution global climate models. To address this, we introduce the first steps toward establishing a dynamic, yet standardized, benchmarking framework that can be used to assess model skill in simulating various characteristics of rainfall. Benchmarking differs from typical model evaluation in that it requires that performance expectations are set a priori. This framework has innumerable applications to underpin scientific studies that assess model performance, inform model development priorities, and aid stakeholder decision-making by providing a structured methodology to identify fit-for-purpose model simulations for climate risk assessments and adaptation strategies. While this framework can be applied to regional climate model simulations at any spatial domain, we demonstrate its effectiveness over Australia using high-resolution, 0.5° × 0.5° simulations from the CORDEX-Australasia ensemble. We provide recommendations for selecting metrics and pragmatic benchmarking thresholds depending on the application of the framework. This includes a top tier of minimum standard metrics to establish a minimum benchmarking standard for ongoing climate model assessment. We present multiple applications of the framework using feedback received from potential user communities and encourage the scientific and user community to build on this framework by tailoring benchmarks and incorporating additional metrics specific to their application. Significance Statement We introduce a standardized benchmarking framework for assessing the skill of regional climate models in simulating precipitation. This framework addresses the lack of a uniform approach in the scientific community and has diverse applications in scientific research, model development, and societal decision-making. We define a set of minimum standard metrics to underpin ongoing climate model assessments that quantify model skill in simulating fundamental characteristics of rainfall. We provide guidance for selecting metrics and defining benchmarking thresholds, demonstrated using multiple case studies over Australia. This framework has broad applications for numerous user communities and provides a structured methodology for the assessment of model performance.

Socioeconomic disparities and regional environment are associated with cervical lymph node metastases in children and adolescents with differentiated thyroid cancer: developing a web-based predictive model.

To establish an online predictive model for the prediction of cervical lymph node metastasis (CLNM) in children and adolescents with differentiated thyroid cancer (caDTC). And analyze the impact between socioeconomic disparities, regional environment and CLNM. We retrospectively analyzed clinicopathological and sociodemographic data of caDTC from the Surveillance, Epidemiology, and End Results (SEER) database from 2000 to 2019. Risk factors for CLNM in caDTC were analyzed using univariate and multivariate logistic regression (LR). And use the extreme gradient boosting (XGBoost) algorithm and other commonly used ML algorithms to build CLNM prediction models. Model performance assessment and visualization were performed using the area under the receiver operating characteristic (AUROC) curve and SHapley Additive exPlanations (SHAP). In addition to common risk factors, our study found that median household income and living regional were strongly associated with CLNM. Whether in the training set or the validation set, among the ML models constructed based on these variables, the XGBoost model has the best predictive performance. After 10-fold cross-validation, the prediction performance of the model can reach the best, and its best AUROC value is 0.766 (95%CI: 0.745-0.786) in the training set, 0.736 (95%CI: 0.670-0.802) in the validation set, and 0.733 (95%CI: 0.683-0.783) in the test set. Based on this XGBoost model combined with SHAP method, we constructed a web-base predictive system. The online prediction model based on the XGBoost algorithm can dynamically estimate the risk probability of CLNM in caDTC, so as to provide patients with personalized treatment advice.

Evaluating generalized feature importance via performance assessment of machine learning models for predicting elastic properties of materials

Identifying key descriptors and understanding important features across different classes of materials are crucial for machine learning (ML) tools to both predict material properties and reveal the physics underlying any process of interest. Traditionally, the predictive modeling of elastic properties of materials is limited to only a few classes of materials and a small set of ML tools despite the broad applications of these materials. Users now have a broad choice of ML models ranging from simple regression models to graph neural networks (GNN) for predicting structure–property relationships. While in recent years, GNNs have outshined traditional ML models in terms of predictability, their intensive data requirement and lack of interpretability may limit practical applicability. Here, we develop a domain-segmented feature space using a diverse set of material attributes and perform a predictive analysis using state-of-the-art ML tools using elastic modulus prediction as a representative example. By deducing a model-independent overall ranking based on feature importance learned by each model, the knowledge is then transferred to GNNs. Our findings indicate a threshold limit on the predictability of traditional ML models, but our approach of transferring task-specific feature importance knowledge to the GNNs can enhance their performance by reducing their data requirement while retaining considerable accuracy.

A predictive model of long-term performance assessment of Ground Source Heat Pump (GSHP) systems in Japanese regions

This study predicts the long-term performance of ground source heat pump (GSHP) systems in different climatic regions of Japan. The research model calculates the dynamic heat load of a typical Japanese building in various cities using their meteorological data, considering different schedules for heating and cooling seasons. The model aims to analyze the performance degradation of the GSHP system after 15 years of operation due to the imbalanced heating and cooling loads. Additionally, it compares ground temperature changes, coefficient of performance (COP) variations, and annual operation times based on different thermal imbalance ratios (TIR) for the case studies. The results demonstrate that TIR serves as an appropriate factor for comprehensively evaluating system aspects, including GHE length, ground temperature, and COP degradation, across various climates. It was found that ground temperatures increased by a maximum of up to 4 °C in cooling-dominated regions due to higher thermal imbalance accumulative loads, while it decreased to around 1 °C in heating-dominated regions. The model also revealed that the COP decreased in the dominant operation for each case study due to induced thermal imbalance and soil temperature change. The thermal imbalance caused the system cooling COP decreased by 0.015/year in the worst case (Okinawa City) and 0.006/year in average for other cases.