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Records with Keyword: Machine Learning
Development of a Two-Stage ESS-Scheduling Model for Cost Minimization Using Machine Learning-Based Load Prediction Techniques
Minsu Park, Jaehwi Kim, Dongjun Won, Jaehee Kim
August 14, 2019 (v1)
Keywords: bagging, energy storage system, ensemble learning, load prediction, Machine Learning, random forest, two-stage algorithm
Effective use of energy storage systems (ESS) is important to reduce unnecessary power consumption. In this paper, a day-ahead two-stage ESS-scheduling model based on the use of a machine learning technique for load prediction has been proposed for minimizing the operating cost of the energy system. The proposed algorithm consists of two stages of ESS. In the first stage, ESS is used to minimize demand charges by reducing the peak load. Then, the remaining capacity is used to reduce energy charges through arbitrage trading, thereby minimizing the total operating cost. To achieve this purpose, accurate load prediction is required. Machine learning techniques are promising methods owing to the ability to improve forecasting performance. Among them, ensemble learning is a well-known machine learning method which helps to reduce variance and prevent overfitting of a model. To predict loads, we employed bootstrap aggregating (bagging) or random forest technique-based decision trees after Ho... [more]
An Accurate Clinical Implication Assessment for Diabetes Mellitus Prevalence Based on a Study from Nigeria
Muhammad Noman Sohail, Ren Jiadong, Musa Uba Muhammad, Sohaib Tahir Chauhdary, Jehangir Arshad, Antony John Verghese
July 31, 2019 (v1)
Subject: Biosystems
Keywords: clinical implications, cluster, data mining, Decision table, diabetes, epidemiology, forecast, Machine Learning, PART, real-life patients, regression, Weka
The increasing rate of diabetes is found across the planet. Therefore, the diagnosis of pre-diabetes and diabetes is important in populations with extreme diabetes risk. In this study, a machine learning technique was implemented over a data mining platform by employing Rule classifiers (PART and Decision table) to measure the accuracy and logistic regression on the classification results for forecasting the prevalence in diabetes mellitus patients suffering simultaneously from other chronic disease symptoms. The real-life data was collected in Nigeria between December 2017 and February 2019 by applying ten non-intrusive and easily available clinical variables. The results disclosed that the Rule classifiers achieved a mean accuracy of 98.75%. The error rate, precision, recall, F-measure, and Matthew’s correlation coefficient MCC were 0.02%, 0.98%, 0.98%, 0.98%, and 0.97%, respectively. The forecast decision, achieved by employing a set of 23 decision rules (DR), indicates that age, ge... [more]
CDL4CDRP: A Collaborative Deep Learning Approach for Clinical Decision and Risk Prediction
Mingrui Sun, Tengfei Min, Tianyi Zang, Yadong Wang
July 30, 2019 (v1)
Keywords: clinical diagnosis, decision support systems, Machine Learning, prediction algorithms, recommender systems
(1) Background: Recommendation algorithms have played a vital role in the prediction of personalized recommendation for clinical decision support systems (CDSSs). Machine learning methods are powerful tools for disease diagnosis. Unfortunately, they must deal with missing data, as this will result in data error and limit the potential patterns and features associated with obtaining a clinical decision; (2) Methods: Recent years, collaborative filtering (CF) have proven to be a valuable means of coping with missing data prediction. In order to address the challenge of missing data prediction and latent feature extraction, neighbor-based and latent features-based CF methods are presented for clinical disease diagnosis. The novel discriminative restricted Boltzmann machine (DRBM) model is proposed to extract the latent features, where the deep learning technique is adopted to analyze the clinical data; (3) Results: Proposed methods were compared to machine learning models, using two diffe... [more]
Extracting Valuable Information from Big Data for Machine Learning Control: An Application for a Gas Lift Process
Ana Carolina Spindola Rangel Dias, Felipo Rojas Soares, Johannes Jäschke, Maurício Bezerra de Souza Jr, José Carlos Pinto
July 28, 2019 (v1)
Keywords: echo state network, gas lift, Machine Learning, model predictive control (MPC)
The present work investigated the use of an echo state network for a gas lift oil well. The main contribution is the evaluation of the network performance under conditions normally faced in a real production system: noisy measurements, unmeasurable disturbances, sluggish behavior and model mismatch. The main pursued objective was to verify if this tool is suitable to compose a predictive control scheme for the analyzed operation. A simpler model was used to train the neural network and a more accurate process model was used to generate time series for validation. The system performance was investigated with distinct sample sizes for training, test and validation procedures and prediction horizons. The performance of the designed ESN was characterized in terms of slugging, setpoint changes and unmeasurable disturbances. It was observed that the size and the dynamic content of the training set tightly affected the network performance. However, for data sets with reasonable information co... [more]
A Systematic Grey-Box Modeling Methodology via Data Reconciliation and SOS Constrained Regression
José Luis Pitarch, Antonio Sala, César de Prada
July 25, 2019 (v1)
Keywords: grey-box model, Machine Learning, process modeling, SOS programming
Developing the so-called grey box or hybrid models of limited complexity for process systems is the cornerstone in advanced control and real-time optimization routines. These models must be based on fundamental principles and customized with sub-models obtained from process experimental data. This allows the engineer to transfer the available process knowledge into a model. However, there is still a lack of a flexible but systematic methodology for grey-box modeling which ensures certain coherence of the experimental sub-models with the process physics. This paper proposes such a methodology based in data reconciliation (DR) and polynomial constrained regression. A nonlinear optimization of limited complexity is to be solved in the DR stage, whereas the proposed constrained regression is based in sum-of-squares (SOS) convex programming. It is shown how several desirable features on the polynomial regressors can be naturally enforced in this optimization framework. The goodnesses of the... [more]
Data-Mining for Processes in Chemistry, Materials, and Engineering
Hao Li, Zhien Zhang, Zhe-Ze Zhao
July 25, 2019 (v1)
Keywords: chemistry, data-mining, Energy, engineering, Machine Learning, materials, neural networks
With the rapid development of machine learning techniques, data-mining for processes in chemistry, materials, and engineering has been widely reported in recent years. In this discussion, we summarize some typical applications for process optimization, design, and evaluation of chemistry, materials, and engineering. Although the research and application targets are various, many important common points still exist in their data-mining. We then propose a generalized strategy based on the philosophy of data-mining, which should be applicable for the design and optimization targets for processes in various fields with both scientific and industrial purposes.
Ensemble Learning Approach for Probabilistic Forecasting of Solar Power Generation
Azhar Ahmed Mohammed, Zeyar Aung
February 27, 2019 (v1)
Keywords: ensemble models, Machine Learning, probabilistic forecasting, regression, solar power
Probabilistic forecasting accounts for the uncertainty in prediction that arises from inaccurate input data due to measurement errors, as well as the inherent inaccuracy of a prediction model. Because of the variable nature of renewable power generation depending on weather conditions, probabilistic forecasting is well suited to it. For a grid-tied solar farm, it is increasingly important to forecast the solar power generation several hours ahead. In this study, we propose three different methods for ensemble probabilistic forecasting, derived from seven individual machine learning models, to generate 24-h ahead solar power forecasts. We have shown that while all of the individual machine learning models are more accurate than the traditional benchmark models, like autoregressive integrated moving average (ARIMA), the ensemble models offer even more accurate results than any individual machine learning model alone does. Furthermore, it is observed that running separate models on the da... [more]
A Review of Classification Problems and Algorithms in Renewable Energy Applications
María Pérez-Ortiz, Silvia Jiménez-Fernández, Pedro A. Gutiérrez, Enrique Alexandre, César Hervás-Martínez, Sancho Salcedo-Sanz
January 7, 2019 (v1)
Keywords: applications, classification algorithms, Machine Learning, Renewable and Sustainable Energy
Classification problems and their corresponding solving approaches constitute one of the fields of machine learning. The application of classification schemes in Renewable Energy (RE) has gained significant attention in the last few years, contributing to the deployment, management and optimization of RE systems. The main objective of this paper is to review the most important classification algorithms applied to RE problems, including both classical and novel algorithms. The paper also provides a comprehensive literature review and discussion on different classification techniques in specific RE problems, including wind speed/power prediction, fault diagnosis in RE systems, power quality disturbance classification and other applications in alternative RE systems. In this way, the paper describes classification techniques and metrics applied to RE problems, thus being useful both for researchers dealing with this kind of problem and for practitioners of the field.
Principal Component Analysis of Process Datasets with Missing Values
Kristen A. Severson, Mark C. Molaro, Richard D. Braatz
July 31, 2018 (v1)
Keywords: chemometrics, Machine Learning, missing data, multivariable statistical process control, principal component analysis, process data analytics, process monitoring, Tennessee Eastman problem
Datasets with missing values arising from causes such as sensor failure, inconsistent sampling rates, and merging data from different systems are common in the process industry. Methods for handling missing data typically operate during data pre-processing, but can also occur during model building. This article considers missing data within the context of principal component analysis (PCA), which is a method originally developed for complete data that has widespread industrial application in multivariate statistical process control. Due to the prevalence of missing data and the success of PCA for handling complete data, several PCA algorithms that can act on incomplete data have been proposed. Here, algorithms for applying PCA to datasets with missing values are reviewed. A case study is presented to demonstrate the performance of the algorithms and suggestions are made with respect to choosing which algorithm is most appropriate for particular settings. An alternating algorithm based... [more]
On the Use of Multivariate Methods for Analysis of Data from Biological Networks
Troy Vargason, Daniel P. Howsmon, Deborah L. McGuinness, Juergen Hahn
July 31, 2018 (v1)
Keywords: autism spectrum disorder, classification, Fisher discriminant analysis, Machine Learning, Multivariate Statistics, one carbon metabolism, probability density function, transsulfuration, urine toxic metals
Data analysis used for biomedical research, particularly analysis involving metabolic or signaling pathways, is often based upon univariate statistical analysis. One common approach is to compute means and standard deviations individually for each variable or to determine where each variable falls between upper and lower bounds. Additionally, p-values are often computed to determine if there are differences between data taken from two groups. However, these approaches ignore that the collected data are often correlated in some form, which may be due to these measurements describing quantities that are connected by biological networks. Multivariate analysis approaches are more appropriate in these scenarios, as they can detect differences in datasets that the traditional univariate approaches may miss. This work presents three case studies that involve data from clinical studies of autism spectrum disorder that illustrate the need for and demonstrate the potential impact of multivariate... [more]
Deterministic Global Optimization with Artificial Neural Networks Embedded
Global deterministische Optimierung von Optimierungsproblemen mit künstlichen neuronalen Netzwerken
Artur M Schweidtmann, Alexander Mitsos
October 15, 2018 (v2)
Subject: Optimization
Artificial neural networks (ANNs) are used in various applications for data-driven black-box modeling and subsequent optimization. Herein, we present an efficient method for deterministic global optimization of ANN embedded optimization problems. The proposed method is based on relaxations of algorithms using McCormick relaxations in a reduced-space [\textit{SIOPT}, 20 (2009), pp. 573-601] including the convex and concave envelopes of the nonlinear activation function of ANNs. The optimization problem is solved using our in-house global deterministic solver MAiNGO. The performance of the proposed method is shown in four optimization examples: an illustrative function, a fermentation process, a compressor plant and a chemical process optimization. The results show that computational solution time is favorable compared to the global general-purpose optimization solver BARON.
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