The use of predictive models in epidemiology is relatively narrow as most of the studies report results of traditional statistical models such as Linear, Logistic, or Cox regressions. In this study, a high-dimensional epidemiological cohort, collected within the Kuopio Ischemic Heart Disease Risk Factor Study in 1984-1989, was used to investigate the predictive ability of models with embedded variable selection. Simple Logistic Regression with seven preselected risk factors was compared to k-Nearest Neighbors, Logistic Lasso Regression, Decision Tree, Random Forest, and Multilayer Perceptron in predicting cardiovascular death for the aged men from Kuopio Ischemic Heart Disease Risk Factor for the long horizon of 30 ± 3 years: 746 predictor variables were available for 2682 men (705 cardiovascular deaths were registered). We considered two scenarios of handling competing risks (removing subjects and treating them as non-cases). The best average AUC on the test sample was 0.8075 (95%CI, 0.8051-0.8099) in scenario 1 and 0.7155 (95%CI, 0.7128-0.7183) in scenario 2 achieved with Logistic Lasso Regression, which was 6.04% and 5.50% higher than the baseline AUC provided by Logistic Regression with manually preselected predictors. In both scenarios Logistic Lasso Regression, Random Forest, and Multilayer Perceptron outperformed Simple Logistic Regression.

When identifying and comparing forecasting models, there may be a risk that poorly selected criteria could lead to wrong conclusions. Thus, it is important to know how sensitive the results are to the selection of criteria. This contribution aims to study the sensitivity of the identification and comparison results to the choice of criteria. It compares typically applied criteria for tuning and performance assessment of load forecasting methods with estimated costs caused by the forecasting errors. The focus is on short-term forecasting of the loads of energy systems. The estimated costs comprise electricity market costs and network costs. We estimate the electricity market costs by assuming that the forecasting errors cause balancing errors and consequently balancing costs to the market actors. The forecasting errors cause network costs by overloading network components thus increasing losses and reducing the component lifetime or alternatively increase operational margins to avoid those overloads. The lifetime loss of insulators, and thus also the components, is caused by heating according to the law of Arrhenius. We also study consumer costs. The results support the assumption that there is a need to develop and use additional and case-specific performance criteria for electricity load forecasting.

This paper aims to introduce a predictive weather-based control policy for the microgrid energy management to improve the resilience of the microgrid. This policy relies on the application of machine learning models for the prediction of microgrid load demand and solar production and supply interruption in the upstream distribution network. The predictions serve as an input to multiobjective chance constraint optimization that balances the microgrid resilience and economic objectives based on the probability of a supply interruption. The interruption predictions are made with a decision-tree-based model that can predict an upcoming interruption in the distribution network with 78% of the maximum accuracy. The case study microgrid consisting of several customers, solar photovoltaic generation, and battery storage is applied to cluster areas located in Finland. Overall, the developed control policy shows an improvement in the daily resilience of the microgrid in regard to an interruption in the main grid when compared with economic dispatch only.

In this paper we compare a number of two-criterion filtering techniques for feature selection in cardiovascular predictive modelling. We design two-objective schemes based on different combinations of four criteria describing the quality of reduced feature sets. To find attribute subsystems meeting the introduced criteria in an optimal way, we suggest applying a cooperative multi-objective genetic algorithm. It includes various search strategies working in a parallel way, which allows additional experiments to be avoided when choosing the most effective heuristic for the problem considered. The performance of filtering techniques was investigated in combination with the SVM model on a population-based epidemiological database called KIHD (Kuopio Ischemic Heart Disease Risk Factor Study). The dataset consists of a large number of variables on various characteristics of the study participants. These baseline measures were collected at the beginning of the study. In addition, all major cardiovascular events that had occurred among the participants over an average of 27 years of follow-up were collected from the national health registries. As a result, we found that the usage of the filtering technique including intra- and inter-class distances led to a significant reduction of the feature set (up to 11 times, from 433 to 38 features) without detriment to the predictive ability of the SVM model. This implies that there is a possibility to cut down on the clinical tests needed to collect the data, which is relevant to the prediction of cardiovascular diseases.