Including high-cardinality attributes in predictive models: A case study in churn prediction in the energy sector

2015 ◽  
Vol 72 ◽  
pp. 72-81 ◽  
Author(s):  
Julie Moeyersoms ◽  
David Martens
Keyword(s):  
Predictive Models ◽  
Energy Sector ◽  
Churn Prediction

An Analysis on the Regional R&D Investment Factors to the Energy Sector - Case Study of 17 Local Governments in Korea -

2020 ◽  
Vol 46 ◽  
pp. 33-51
Author(s):  
Dong Hyun Kim ◽  
◽  
Ju Seok Lee ◽  
Han Soo Chang ◽  
Hyun Soo Cho
Keyword(s):  
Local Governments ◽  
Energy Sector ◽  
D Investment

scholarly journals A Case Study in View of Developing Predictive Models for Water Supply System Management

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3305
Author(s):  
Katarzyna Pietrucha-Urbanik ◽  
Barbara Tchórzewska-Cieślak ◽  
Mohamed Eid
Keyword(s):  
Water Supply ◽  
Predictive Models ◽  
Failure Modes ◽  
Supply System ◽  
Water Supply System ◽  
System Management ◽  
System A ◽  
Operational Failure ◽  
Operational Data

Initiated by a case study to assess the effectiveness of the modernisation actions undertaken in a water supply system, some R&D activities were conducted to construct a global predictive model, based on the available operational failure and recovery data. The available operational data, regarding the water supply system, are the pipes’ diameter, failure modes, materials, functional conditions, seasonality, and the number of failures and time-to-recover intervals. The operational data are provided by the water company responsible of the supply system. A predictive global model is proposed based on the output of the operational data statistical assessment. It should assess the expected effectiveness of decisions taken in support of the modernisation and the extension plan.

Evaluating prediction of continuous clinical values: a glucose case study (Preprint)

2021 ◽  
Author(s):  
George Hripcsak ◽  
David J Albers
Keyword(s):  
Predictive Models ◽  
Clinical Utility ◽  
Clinical Care ◽  
Glucose Measurement ◽  
Care Processes ◽  
Low Glucose ◽  
Potential Clinical Utility ◽  
Rms Error ◽  
Error Grid

BACKGROUND Background: It would be useful to be able to assess the utility of predictive models of continuous values before clinical trials are carried out. OBJECTIVE Objective: To compare metrics to assess the potential clinical utility of models that produce continuous value forecasts. METHODS Methods: We ran a set of data assimilation forecast algorithms on time series of glucose measurements from intensive care unit patients. We evaluated the forecasts using four sets of metrics: glucose root mean square error, a set of metrics on a transformed glucose value, the estimated effect on clinical care based on an insulin guideline, and a glucose measurement error grid (Parkes grid). We assessed correlation among the metrics and created a set of factor models. RESULTS Results: The metrics generally correlated with each other, but those that estimated the effect on clinical care correlated with the others the least and were generally associated with their own independent factors. The other metrics appeared to separate into those that emphasized errors in low glucose versus errors in high glucose. The Parkes grid was well correlated with the transformed glucose but not the estimation of clinical care. CONCLUSIONS Discussion: Our results indicate that we need to be careful before we assume that commonly used metrics like RMS error in raw glucose or even metrics like the Parkes grid that are designed to measure importance of differences will correlate well with actual effect on clinical care processes. A combination of metrics appeared to explain the most variance between cases. As prediction algorithms move into practice, it will be important to measure actual effects.

scholarly journals Modelling streamflow response to hydro-climatic variables in the Upper Mkomazi River, South Africa

2014 ◽  
Author(s):  
◽  
Oluwaseun Kunle Oyebode
Keyword(s):  
South Africa ◽  
Water Resources ◽  
Genetic Programming ◽  
Predictive Models ◽  
Model Development ◽  
De Algorithm ◽  
Planning And Management ◽  
Ann Models ◽  
Artificial Neural

Streamflow modelling remains crucial to decision-making especially when it concerns planning and management of water resources systems in water-stressed regions. This study proposes a suitable method for streamflow modelling irrespective of the limited availability of historical datasets. Two data-driven modelling techniques were applied comparatively so as to achieve this aim. Genetic programming (GP), an evolutionary algorithm approach and a differential evolution (DE)-trained artificial neural network (ANN) were used for streamflow prediction in the upper Mkomazi River, South Africa. Historical records of streamflow and meteorological variables for a 19-year period (1994- 2012) were used for model development and also in the selection of predictor variables into the input vector space of the models. In both approaches, individual monthly predictive models were developed for each month of the year using a 1-year lead time. Two case studies were considered in development of the ANN models. Case study 1 involved the use of correlation analysis in selecting input variables as employed during GP model development, while the DE algorithm was used for training and optimizing the model parameters. However in case study 2, genetic programming was incorporated as a screening tool for determining the dimensionality of the ANN models, while the learning process was further fine-tuned by subjecting the DE algorithm to sensitivity analysis. Altogether, the performance of the three sets of predictive models were evaluated comparatively using three statistical measures namely, Mean Absolute Percent Error (MAPE), Root Mean-Squared Error (RMSE) and coefficient of determination (R2). Results showed better predictive performance by the GP models both during the training and validation phases when compared with the ANNs. Although the ANN models developed in case study 1 gave satisfactory results during the training phase, they were unable to extensively replicate those results during the validation phase. It was found that results from case study 1 were considerably influenced by the problems of overfitting and memorization, which are typical of ANNs when subjected to small amount of datasets. However, results from case study 2 showed great improvement across the three evaluation criteria, as the overfitting and memorization problems were significantly minimized, thus leading to improved accuracy in the predictions of the ANN models. It was concluded that the conjunctive use of the two evolutionary computation methods (GP and DE) can be used to improve the performance of artificial neural networks models, especially when availability of datasets is limited. In addition, the GP models can be deployed as predictive tools for the purpose of planning and management of water resources within the Mkomazi region and KwaZulu-Natal province as a whole.

GHG emission reduction in energy sector and its abatement cost: Case study of five provinces in Mekong delta region, Vietnam

2018 ◽  
Vol 15 (12) ◽  
pp. 715-723 ◽  
Author(s):  
Quyen Le Luu ◽  
Nam Hoai Nguyen ◽  
Anthony Halog ◽  
Huyen Van Bui
Keyword(s):  
Emission Reduction ◽  
Mekong Delta ◽  
Abatement Cost ◽  
Energy Sector ◽  
Ghg Emission ◽  
Delta Region
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