Cluster-based ensemble learning for wind power modeling with meteorological wind data

Optimal implementation and monitoring of wind energy generation hinge on reliable power modeling that is vital for understanding turbine control, farm operational optimization, and grid load balance. Based on the idea of similar wind condition leads to similar wind power; this paper constructs a modeling scheme that orderly integrates three types of ensemble learning algorithms, bagging, boosting, and stacking, and clustering approaches to achieve optimal power modeling. It also investigates applications of different clustering algorithms and methodology for determining cluster numbers in wind power modeling. The results reveal that all ensemble models with clustering exploit the intrinsic information of wind data and thus outperform models without it by approximately 15% on average. The model with the best farthest first clustering is computationally rapid and performs exceptionally well with an improvement of around 30%. The modeling is further boosted by about 5% by introducing stacking that fuses ensembles with varying clusters. The proposed modeling framework thus demonstrates promise by delivering efficient and robust modeling performance.Comment: UNDER REVIEW Renewable & Sustainable Energy Review

Prediction of high-performance concrete compressive strength through a comparison of machine learning techniques

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceHigh-performance concrete (HPC) is a highly complex composite material whose characteristics are extremely difficult to model. One of those characteristics is the concrete compressive strength, a nonlinear function of the same ingredients that compose HPC: cement, fly ash, blast furnace slag, water, superplasticizer, age, and coarse and fine aggregates. Research has shown time and time again that concrete strength is not determined just by the water-to-cement ratio, which was for years the go to metric. In addition, traditional methods that attempt to model HPC, such as regression analysis, do not provide sufficient prediction power due to nonlinear proprieties of the mixture. Therefore, this study attempts to optimize the prediction and modeling of the compressive strength of HPC by analyzing seven different machine learning (ML) algorithms: three regularization algorithms (Lasso, Ridge and Elastic Net), three ensemble algorithms (Random Forest, Gradient Boost and AdaBoost), and Artificial Neural Networks. All techniques were built and tested with a dataset composed of data from 17 different concrete strength test laboratories, under the same experimental conditions, which enabled a fair comparison amongst them and between different previous studies in the field. Feature importance analysis and outlier analysis were also performed, and all models were subject to a Wilcoxon Signed-Ranks Test to ensure statistically significant results. The final results show that the more complex ML algorithms provided greater accuracy than the regularization techniques, with Gradient Boost being the superior model amongst them, providing more accurate predictions than the sate-of-the-art. Better results were achieved using all variables and without removing outlier observations

Methods to Improve the Prediction Accuracy and Performance of Ensemble Models

The application of ensemble predictive models has been an important research area in predicting medical diagnostics, engineering diagnostics, and other related smart devices and related technologies. Most of the current predictive models are complex and not reliable despite numerous efforts in the past by the research community. The performance accuracy of the predictive models have not always been realised due to many factors such as complexity and class imbalance. Therefore there is a need to improve the predictive accuracy of current ensemble models and to enhance their applications and reliability and non-visual predictive tools. The research work presented in this thesis has adopted a pragmatic phased approach to propose and develop new ensemble models using multiple methods and validated the methods through rigorous testing and implementation in different phases. The first phase comprises of empirical investigations on standalone and ensemble algorithms that were carried out to ascertain their performance effects on complexity and simplicity of the classifiers. The second phase comprises of an improved ensemble model based on the integration of Extended Kalman Filter (EKF), Radial Basis Function Network (RBFN) and AdaBoost algorithms. The third phase comprises of an extended model based on early stop concepts, AdaBoost algorithm, and statistical performance of the training samples to minimize overfitting performance of the proposed model. The fourth phase comprises of an enhanced analytical multivariate logistic regression predictive model developed to minimize the complexity and improve prediction accuracy of logistic regression model. To facilitate the practical application of the proposed models; an ensemble non-invasive analytical tool is proposed and developed. The tool links the gap between theoretical concepts and practical application of theories to predict breast cancer survivability. The empirical findings suggested that: (1) increasing the complexity and topology of algorithms does not necessarily lead to a better algorithmic performance, (2) boosting by resampling performs slightly better than boosting by reweighting, (3) the prediction accuracy of the proposed ensemble EKF-RBFN-AdaBoost model performed better than several established ensemble models, (4) the proposed early stopped model converges faster and minimizes overfitting better compare with other models, (5) the proposed multivariate logistic regression concept minimizes the complexity models (6) the performance of the proposed analytical non-invasive tool performed comparatively better than many of the benchmark analytical tools used in predicting breast cancers and diabetics ailments. The research contributions to ensemble practice are: (1) the integration and development of EKF, RBFN and AdaBoost algorithms as an ensemble model, (2) the development and validation of ensemble model based on early stop concepts, AdaBoost, and statistical concepts of the training samples, (3) the development and validation of predictive logistic regression model based on breast cancer, and (4) the development and validation of a non-invasive breast cancer analytic tools based on the proposed and developed predictive models in this thesis. To validate prediction accuracy of ensemble models, in this thesis the proposed models were applied in modelling breast cancer survivability and diabetics’ diagnostic tasks. In comparison with other established models the simulation results of the models showed improved predictive accuracy. The research outlines the benefits of the proposed models, whilst proposes new directions for future work that could further extend and improve the proposed models discussed in this thesis

A machine learning-remote sensing framework for modelling water stress in Shiraz vineyards

Thesis (MA)--Stellenbosch University, 2018.ENGLISH ABSTRACT: Water is a limited natural resource and a major environmental constraint for crop production in viticulture. The unpredictability of rainfall patterns, combined with the potentially catastrophic effects of climate change, further compound water scarcity, presenting dire future scenarios of undersupplied irrigation systems. Major water shortages could lead to devastating loses in grape production, which would negatively affect job security and national income. It is, therefore, imperative to develop management schemes and farming practices that optimise water usage and safeguard grape production. Hyperspectral remote sensing techniques provide a solution for the monitoring of vineyard water status. Hyperspectral data, combined with the quantitative analysis of machine learning ensembles, enables the detection of water-stressed vines, thereby facilitating precision irrigation practices and ensuring quality crop yields. To this end, the thesis set out to develop a machine learning–remote sensing framework for modelling water stress in a Shiraz vineyard. The thesis comprises two components. Component one assesses the utility of terrestrial hyperspectral imagery and machine learning ensembles to detect water-stressed Shiraz vines. The Random Forest (RF) and Extreme Gradient Boosting (XGBoost) ensembles were employed to discriminate between water-stressed and non-stressed Shiraz vines. Results showed that both ensemble learners could effectively discriminate between water-stressed and non-stressed vines. When using all wavebands (p = 176), RF yielded a test accuracy of 83.3% (KHAT = 0.67), with XGBoost producing a test accuracy of 80.0% (KHAT = 0.6). Component two explores semi-automated feature selection approaches and hyperparameter value optimisation to improve the developed framework. The utility of the Kruskal-Wallis (KW) filter, Sequential Floating Forward Selection (SFFS) wrapper, and a Filter-Wrapper (FW) approach, was evaluated. When using optimised hyperparameter values, an increase in test accuracy ranging from 0.8% to 5.0% was observed for both RF and XGBoost. In general, RF was found to outperform XGBoost. In terms of predictive competency and computational efficiency, the developed FW approach was the most successful feature selection method implemented. The developed machine learning–remote sensing framework warrants further investigation to confirm its efficacy. However, the thesis answered key research questions, with the developed framework providing a point of departure for future studies.AFRIKAANSE OPSOMMING: Water is 'n beperkte natuurlike hulpbron en 'n groot omgewingsbeperking vir gewasproduksie in wingerdkunde. Die onvoorspelbaarheid van reënvalpatrone, gekombineer met die potensiële katastrofiese gevolge van klimaatsverandering, voorspel ‘n toekoms van water tekorte vir besproeiingstelsels. Groot water tekorte kan lei tot groot verliese in druiweproduksie, wat 'n negatiewe uitwerking op werksekuriteit en nasionale inkomste sal hê. Dit is dus noodsaaklik om bestuurskemas en boerderypraktyke te ontwikkel wat die gebruik van water optimaliseer en druiweproduksie beskerm. Hyperspectrale afstandswaarnemingstegnieke bied 'n oplossing vir die monitering van wingerd water status. Hiperspektrale data, gekombineer met die kwantitatiewe analise van masjienleer klassifikasies, fasiliteer die opsporing van watergestresde wingerdstokke. Sodoende verseker dit presiese besproeiings praktyke en kwaliteit gewasopbrengs. Vir hierdie doel het die tesis probeer 'n masjienleer-afstandswaarnemings raamwerk ontwikkel vir die modellering van waterstres in 'n Shiraz-wingerd. Die tesis bestaan uit twee komponente. Komponent 1 het die nut van terrestriële hiperspektrale beelde en masjienleer klassifikasies gebruik om watergestresde Shiraz-wingerde op te spoor. Die Ewekansige Woud (RF) en Ekstreme Gradiënt Bevordering (XGBoost) algoritme was gebruik om te onderskei tussen watergestresde en nie-gestresde Shiraz-wingerde. Resultate het getoon dat beide RF en XGBoost effektief kan diskrimineer tussen watergestresde en nie-gestresde wingerdstokke. Met die gebruik van alle golfbande (p = 176) het RF 'n toets akkuraatheid van 83.3% (KHAT = 0.67) behaal en XGBoost het 'n toets akkuraatheid van 80.0% (KHAT = 0.6) gelewer. Komponent twee het die gebruik van semi-outomatiese veranderlike seleksie benaderings en hiperparameter waarde optimalisering ondersoek om die ontwikkelde raamwerk te verbeter. Die nut van die Kruskal-Wallis (KW) filter, sekwensiële drywende voorkoms seleksie (SFFS) wrapper en 'n Filter-Wrapper (FW) benadering is geëvalueer. Die gebruik van optimaliseerde hiperparameter waardes het gelei tot 'n toename in toets akkuraatheid (van 0.8% tot 5.0%) vir beide RF en XGBoost. In die algeheel het RF beter presteer as XGBoost. In terme van voorspellende bevoegdheid en berekenings doeltreffendheid was die ontwikkelde FW benadering die mees suksesvolle veranderlike seleksie metode. Die ontwikkelde masjienleer-afstandwaarnemende raamwerk benodig verder navorsing om sy doeltreffendheid te bevestig. Die tesis het egter sleutelnavorsingsvrae beantwoord, met die ontwikkelde raamwerk wat 'n vertrekpunt vir toekomstige studies verskaf.Master

Novel Computationally Intelligent Machine Learning Algorithms for Data Mining and Knowledge Discovery

This thesis addresses three major issues in data mining regarding feature subset selection in large dimensionality domains, plausible reconstruction of incomplete data in cross-sectional applications, and forecasting univariate time series. For the automated selection of an optimal subset of features in real time, we present an improved hybrid algorithm: SAGA. SAGA combines the ability to avoid being trapped in local minima of Simulated Annealing with the very high convergence rate of the crossover operator of Genetic Algorithms, the strong local search ability of greedy algorithms and the high computational efficiency of generalized regression neural networks (GRNN). For imputing missing values and forecasting univariate time series, we propose a homogeneous neural network ensemble. The proposed ensemble consists of a committee of Generalized Regression Neural Networks (GRNNs) trained on different subsets of features generated by SAGA and the predictions of base classifiers are combined by a fusion rule. This approach makes it possible to discover all important interrelations between the values of the target variable and the input features. The proposed ensemble scheme has two innovative features which make it stand out amongst ensemble learning algorithms: (1) the ensemble makeup is optimized automatically by SAGA; and (2) GRNN is used for both base classifiers and the top level combiner classifier. Because of GRNN, the proposed ensemble is a dynamic weighting scheme. This is in contrast to the existing ensemble approaches which belong to the simple voting and static weighting strategy. The basic idea of the dynamic weighting procedure is to give a higher reliability weight to those scenarios that are similar to the new ones. The simulation results demonstrate the validity of the proposed ensemble model

LVAD Occlusion Condition Monitoring Using Boost Classification Trees

Cardiac related diseases are a serious health risk for adults. Consequently, therapies exist to treat these aliments such as heart transplant and medication. Heart transplant remains the gold standard for treating severe heart failure, however left ventricular assistive devices, a cardiac blood pump, are become a viable long term treatment. Unfortunately, with the benefits of these devices come risks of clot formation. These occlusions can cause strokes, further cardiac damage, or even death. Therefore, it is critical that these occlusions be detected as early as possible. This work presents an expanded method to non-invasively monitor the condition of a Thoratec HeartMate II ventricular assist device through the application of a boosted classification tree. In addition, both inflow and outflow blockages measured at aorta and pump locations were experimentally tested on a cardiac phantom. The proposed method presents a potential outpatient diagnostic method that may assist experienced cardiologists in their treatment of LVAD patients

An Ensemble Approach for Multi-Step Ahead Energy Forecasting of Household Communities

This paper addresses the estimation of household communities' overall energy usage and solar energy production, considering different prediction horizons. Forecasting the electricity demand and energy generation of communities can help enrich the information available to energy grid operators to better plan their short-term supply. Moreover, households will increasingly need to know more about their usage and generation patterns to make wiser decisions on their appliance usage and energy-trading programs. The main issues to address here are the volatility of load consumption induced by the consumption behaviour and variability in solar output influenced by solar cells specifications, several meteorological variables, and contextual factors such as time and calendar information. To address these issues, we propose a predicting approach that first considers the highly influential factors and, second, benefits from an ensemble learning method where one Gradient Boosted Regression Tree algorithm is combined with several Sequence-to-Sequence LSTM networks. We conducted experiments on a public dataset provided by the Ausgrid Australian electricity distributor collected over three years. The proposed model's prediction performance was compared to those by contributing learners and by conventional ensembles. The obtained results have demonstrated the potential of the proposed predictor to improve short-term multi-step forecasting by providing more stable forecasts and more accurate estimations under different day types and meteorological conditionspublishedVersio