A Nonlinear Autoregressive With Exogenous Inputs Artificial Neural Network Model for Building Thermal Load Prediction

2019 ◽  
Vol 142 (5) ◽  
Author(s):  
Byeongho Yu ◽  
Dongsu Kim ◽  
Heejin Cho ◽  
Pedro Mago
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Nonlinear System ◽  
Prediction Model ◽  
Thermal Load ◽  
Ann Model ◽  
Load Prediction ◽  
Artificial Neural ◽  
Nonlinear Autoregressive

Abstract Thermal load prediction is a key part of energy system management and control in buildings, and its accuracy plays a critical role to improve building energy performance and efficiency. Regarding thermal load prediction, various types of prediction model have been considered and studied, such as physics-based, statistical, and machine learning models. Physical models can be accurate but require extended lead time for model development. Statistical models are relatively simple to develop and require less computation time, but they may not provide accurate results for complex energy systems with intricate nonlinear dynamic behaviors. This study proposes an artificial neural network (ANN) model, one of the prevalent machine learning methods to predict building thermal load, combining with the concept of nonlinear autoregressive with exogenous inputs (NARX). NARX-ANN prediction model is distinguished from typical ANN models because the NARX concept can address nonlinear system behaviors effectively based on its recurrent architectures and time indexing features. To examine the suitability and validity of NARX-ANN model for building thermal load prediction, a case study is carried out using the field data of an academic campus building at Mississippi State University (MSU). Results show that the proposed NARX-ANN model can provide an accurate and robust prediction performance and effectively address nonlinear system behaviors in the prediction.

A Non-Linear Auto-Regressive With Exogenous Inputs (NARX) Artificial Neural Network (ANN) Model for Building Thermal Load Prediction

2019 ◽  
Author(s):  
Byeongho Yu ◽  
Dongsu Kim ◽  
Heejin Cho ◽  
Pedro Mago
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Nonlinear System ◽  
Prediction Model ◽  
Thermal Load ◽  
Ann Model ◽  
Load Prediction ◽  
Non Linear ◽  
Artificial Neural Network Ann

Abstract Thermal load prediction is a key part of energy system management and control in buildings, and its accuracy plays a critical role to improve and maintain building energy performance and efficiency. To address this issue, various types of prediction model have been considered and studied, such as physics-based, statistical, and machine learning models. Physical models can be accurate but require extended lead time for model development. Statistical models are relatively simple to develop and require less computation time than other models, but they may not provide accurate results for complex energy systems with an intricate nonlinear dynamic behavior. This study proposes an Artificial Neural Network (ANN) model, one of the prevalent machine learning methods to predict building thermal load, combining with the concept of Non-linear Auto-Regression with Exogenous inputs (NARX). NARX-ANN prediction model is distinguished from typical ANN models due to the fact that the NARX concept can address nonlinear system behaviors effectively based on recurrent architectures and time indexing features. To examine the suitability and validity of NARX-ANN model for building thermal load prediction, a case study is carried out using field data of an academic campus building at Mississippi State University. Results show that the proposed NARX-ANN model can provide an accurate prediction performance and effectively address nonlinear system behaviors in the prediction.

Surface Roughness Prediction in Turning of Free Machining Steel 1215 by Artificial Neural Network

2011 ◽  
Vol 188 ◽  
pp. 535-541
Author(s):  
Xiao Jiang Cai ◽  
Z.Q. Liu ◽  
Q.C. Wang ◽  
Shu Han ◽  
Qing Long An ◽  
...  
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Artificial Neural Network ◽  
Prediction Model ◽  
Mean Squared Error ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Ann Model ◽  
Roughness Prediction ◽  
Artificial Neural

Surface roughness is a significant aspect of the surface integrity concept. It is efficient to predict the surface roughness in advance by a prediction model. In this study, artificial neural network is used to model the surface roughness in turning of free machining steel 1215. The inputs considered in the prediction ANN model were cutting speed, feed rate and depth of cut, and the output was Ra. Several feed-forward neural networks with different architectures were compared in terms of prediction accuracy, and then the best prediction model, a 3-4-1-1 ANN was capable of predicting Ra with a mean squared error 5.46%, was presented.

scholarly journals Artificial Neural Network coupled Condition Monitoring for advanced Fault Diagnosis of Engine

2021 ◽  
Author(s):  
Suvendu Mohanty ◽  
Swarup Paul ◽  
Soudip Hazra
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Engine Performance ◽  
Wear Particle ◽  
Severity Index ◽  
Ann Model ◽  
Artificial Neural ◽  
Engine Failure ◽  
Artificial Neural Network Ann ◽  
Nonlinear Autoregressive

Abstract This paper reflects on the use of the Artificial Neural Network ( ANN) approach to diagnose and interpret engine failure behaviour. The current research focuses on the analysis of quantitative wear trend patterns through Condition Tracking (CM) and soft computational approaches. Oil analysis has been carried out to observe the engine failure trend. An ANN model using a Nonlinear Autoregressive with Exogenous Input (NARX) architecture has been employed to predict quantitative outputs such as Wear Particle Concentration (WPC), Wear Severity Index (WSI), Severity Index (SI) and Percentage of Large Particle (PLP) in connection with input functions of Engine Running Hours, RPM and oil temperature. Correlation function and error similarity are statistically evaluated to represent the model's robustness and effectively chart the loss input-output sequence. The subsequent ANN model demonstrates the capabilities for advance diagnosis and better prediction of engine performance.

scholarly journals A hybrid data envelopment analysis—artificial neural network prediction model for COVID-19 severity in transplant recipients

2021 ◽  
Author(s):  
Ignacio Revuelta ◽  
Francisco J. Santos-Arteaga ◽  
Enrique Montagud-Marrahi ◽  
Pedro Ventura-Aguiar ◽  
Debora Di Caprio ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Data Envelopment Analysis ◽  
Hospital Admission ◽  
Prediction Model ◽  
Data Envelopment ◽  
Transplant Recipients ◽  
Ann Model ◽  
Hybrid Data ◽  
Artificial Neural

AbstractIn an overwhelming demand scenario, such as the SARS-CoV-2 pandemic, pressure over health systems may outburst their predicted capacity to deal with such extreme situations. Therefore, in order to successfully face a health emergency, scientific evidence and validated models are needed to provide real-time information that could be applied by any health center, especially for high-risk populations, such as transplant recipients. We have developed a hybrid prediction model whose accuracy relative to several alternative configurations has been validated through a battery of clustering techniques. Using hospital admission data from a cohort of hospitalized transplant patients, our hybrid Data Envelopment Analysis (DEA)—Artificial Neural Network (ANN) model extrapolates the progression towards severe COVID-19 disease with an accuracy of 96.3%, outperforming any competing model, such as logistic regression (65.5%) and random forest (44.8%). In this regard, DEA-ANN allows us to categorize the evolution of patients through the values of the analyses performed at hospital admission. Our prediction model may help guiding COVID-19 management through the identification of key predictors that permit a sustainable management of resources in a patient-centered model.

scholarly journals An Artificial Neural Network Prediction Model for Posttraumatic Epilepsy: Retrospective Cohort Study (Preprint)

2020 ◽  
Author(s):  
Xueping Wang ◽  
Jie Zhong ◽  
Ting Lei ◽  
Deng Chen ◽  
Haijiao Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Prediction Model ◽  
Cross Validation ◽  
Ann Model ◽  
Back Propagation Algorithm ◽  
Training Cohort ◽  
Posttraumatic Epilepsy ◽  
Artificial Neural ◽  
Sensitivity Specificity

BACKGROUND Posttraumatic epilepsy (PTE) is a common sequela after traumatic brain injury (TBI), and identifying high-risk patients with PTE is necessary for their better treatment. Although artificial neural network (ANN) prediction models have been reported and are superior to traditional models, the ANN prediction model for PTE is lacking. OBJECTIVE We aim to train and validate an ANN model to anticipate the risks of PTE. METHODS The training cohort was TBI patients registered at West China Hospital. We used a 5-fold cross-validation approach to train and test the ANN model to avoid overfitting; 21 independent variables were used as input neurons in the ANN models, using a back-propagation algorithm to minimize the loss function. Finally, we obtained sensitivity, specificity, and accuracy of each ANN model from the 5 rounds of cross-validation and compared the accuracy with a nomogram prediction model built in our previous work based on the same population. In addition, we evaluated the performance of the model using patients registered at Chengdu Shang Jin Nan Fu Hospital (testing cohort 1) and Sichuan Provincial People’s Hospital (testing cohort 2) between January 1, 2013, and March 1, 2015. RESULTS For the training cohort, we enrolled 1301 TBI patients from January 1, 2011, to December 31, 2017. The prevalence of PTE was 12.8% (166/1301, 95% CI 10.9%-14.6%). Of the TBI patients registered in testing cohort 1, PTE prevalence was 10.5% (44/421, 95% CI 7.5%-13.4%). Of the TBI patients registered in testing cohort 2, PTE prevalence was 6.1% (25/413, 95% CI 3.7%-8.4%). The results of the ANN model show that, the area under the receiver operating characteristic curve in the training cohort was 0.907 (95% CI 0.889-0.924), testing cohort 1 was 0.867 (95% CI 0.842-0.893), and testing cohort 2 was 0.859 (95% CI 0.826-0.890). Second, the average accuracy of the training cohort was 0.557 (95% CI 0.510-0.620), with 0.470 (95% CI 0.414-0.526) in testing cohort 1 and 0.344 (95% CI 0.287-0.401) in testing cohort 2. In addition, sensitivity, specificity, positive predictive values and negative predictors in the training cohort (testing cohort 1 and testing cohort 2) were 0.80 (0.83 and 0.80), 0.86 (0.80 and 0.84), 91% (85% and 78%), and 86% (80% and 83%), respectively. When calibrating this ANN model, Brier scored 0.121 in testing cohort 1 and 0.127 in testing cohort 2. Compared with the nomogram model, the ANN prediction model had a higher accuracy (<i>P</i>=.01). CONCLUSIONS This study shows that the ANN model can predict the risk of PTE and is superior to the risk estimated based on traditional statistical methods. However, the calibration of the model is a bit poor, and we need to calibrate it on a large sample size set and further improve the model.

scholarly journals Artificial Neural Network and Machine Learning Based Methods for Population Estimation of Rohingya Refugees: Comparing Data-Driven and Satellite Image-Driven Approaches

2019 ◽  
Vol 06 (04) ◽  
pp. 439-455 ◽  
Author(s):  
Nahian Ahmed ◽  
Nazmul Alam Diptu ◽  
M. Sakil Khan Shadhin ◽  
M. Abrar Fahim Jaki ◽  
M. Ferdous Hasan ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Satellite Image ◽  
Population Estimation ◽  
Data Driven ◽  
Gradient Boosting ◽  
Ann Model ◽  
Data Driven Approach ◽  
Artificial Neural

Manual field-based population census data collection method is slow and expensive, especially for refugee management situations where more frequent censuses are necessary. This study aims to explore the approaches of population estimation of Rohingya migrants using remote sensing and machine learning. Two different approaches of population estimation viz., (i) data-driven approach and (ii) satellite image-driven approach have been explored. A total of 11 machine learning models including Artificial Neural Network (ANN) are applied for both approaches. It is found that, in situations where the surface population distribution is unknown, a smaller satellite image grid cell length is required. For data-driven approach, ANN model is placed fourth, Linear Regression model performed the worst and Gradient Boosting model performed the best. For satellite image-driven approach, ANN model performed the best while Ada Boost model has the worst performance. Gradient Boosting model can be considered as a suitable model to be applied for both the approaches.

scholarly journals Development of an Artificial Neural Network Model and Comparison with Nomogram for Prediction of Pathological Complete Response After Neoadjuvant Chemotherapy in Breast Cancer

2021 ◽  
Author(s):  
Ji-Jung Jung ◽  
Eunyoung Kang ◽  
Eun-Kyu Kim ◽  
Jee Hyun Kim ◽  
Se Hyun Kim ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Neoadjuvant Chemotherapy ◽  
Pathological Complete Response ◽  
Complete Response ◽  
Machine Learning Algorithms ◽  
Ann Model ◽  
Artificial Neural

Abstract Identifying breast cancer patients who may benefit from neoadjuvant chemotherapy will facilitate personalized treatment regarding chemotherapy and surgery. In our work, we developed two predictive models, nomogram and a machine learning model based on artificial neural network (ANN), to anticipate pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) in breast cancer. We demonstrated that high level of estrogen receptor (ER) positivity, positive human epidermal growth factor receptor 2 (HER2) status, complete response on magnetic resonance imaging (MRI), abnormal CEA level after NAC, and abnormal CA15-3 level after NAC were significant predictors of pCR. A nomogram and ANN model trained to predict pCR were developed using these five predictors. The performance of the two models were tested using a fully independent test set. Validation test showed the area under the receiver operating characteristic curve (AUC) of 0.789 (95% confidence interval (CI), 0.707-0.871) for the nomogram and 0.876 (95% CI, 0.808-0.943) for the ANN model. Both models showed excellent performance, but the ANN model performed better in terms of accuracy and discrimination. Machine-learning algorithms hold promise in medical application and provide better prediction than nomogram.

scholarly journals Machine learning for early discrimination between transient and persistent acute kidney injury in critically ill patients with sepsis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xiao-Qin Luo ◽  
Ping Yan ◽  
Ning-Ya Zhang ◽  
Bei Luo ◽  
Mei Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Acute Kidney Injury ◽  
Logistic Regression ◽  
Prediction Model ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Kidney Injury ◽  
Artificial Neural

AbstractAcute kidney injury (AKI) is commonly present in critically ill patients with sepsis. Early prediction of short-term reversibility of AKI is beneficial to risk stratification and clinical treatment decision. The study sought to use machine learning methods to discriminate between transient and persistent sepsis-associated AKI. Septic patients who developed AKI within the first 48 h after ICU admission were identified from the Medical Information Mart for Intensive Care III database. AKI was classified as transient or persistent according to the Acute Disease Quality Initiative workgroup consensus. Five prediction models using logistic regression, random forest, support vector machine, artificial neural network and extreme gradient boosting were constructed, and their performance was evaluated by out-of-sample testing. A simplified risk prediction model was also derived based on logistic regression and features selected by machine learning algorithms. A total of 5984 septic patients with AKI were included, 3805 (63.6%) of whom developed persistent AKI. The artificial neural network and logistic regression models achieved the highest area under the receiver operating characteristic curve (AUC) among the five machine learning models (0.76, 95% confidence interval [CI] 0.74–0.78). The simplified 14-variable model showed adequate discrimination, with the AUC being 0.76 (95% CI 0.73–0.78). At the optimal cutoff of 0.63, the sensitivity and specificity of the simplified model were 63% and 76% respectively. In conclusion, a machine learning-based simplified prediction model including routine clinical variables could be used to differentiate between transient and persistent AKI in critically ill septic patients. An easy-to-use risk calculator can promote its widespread application in daily clinical practice.

scholarly journals Operational Dst index prediction model based on combination of artificial neural network and empirical model

2021 ◽  
Author(s):  
Wooyeon Park ◽  
Jaejin Lee ◽  
Kyung-Chan Kim ◽  
JongKil Lee ◽  
Keunchan Park ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Prediction Model ◽  
Space Weather ◽  
Large Scale ◽  
Geomagnetic Storms ◽  
Ann Model ◽  
Neural Network Models ◽  
Dst Index ◽  
Artificial Neural

<p class="Abstract" style="margin: 6pt 0cm 0.0001pt; font-size: 12pt; font-family: 굴림, sans-serif; color: rgb(0, 0, 0); text-align: justify; text-indent: 36pt;"><span lang="EN-US" style="font-family: &quot;Times New Roman&quot;, serif;">In this paper, an operational Dst index prediction model is developed by combining empirical and artificial neural network models. Artificial neural network algorithms are widely used to predict space weather conditions. While they require a large amount of data for machine learning, large-scale geomagnetic storms have not occurred sufficiently for the last 20 years, ACE and DSCOVR mission operation period. Conversely, the empirical models are based on numerical equations derived from human intuition and are therefore applicable to extrapolate for large storms. In this study, we distinguish between Coronal Mass Ejection (CME) driven and Corotating Interaction Region (CIR) driven storms, estimate the minimum Dst values, and derive an equation for describing the recovery phase. The combined Korea Astronomy and Space Science Institute (KASI) Dst Prediction (KDP) model achieved better performance contrasted to Artificial Neural Network (ANN) model only. This model could be used practically for space weather operation by extending prediction time to 24 hours and updating the model output every hour.<o:p></o:p></span></p>

Sign in / Sign up

Export Citation Format

Share Document