scholarly journals Evaluation of the effective forecast and decision horizon in optimal hydropower generation considering medium-range precipitation forecasts

2019 ◽  
Vol 19 (7) ◽  
pp. 2147-2155
Author(s):  
Xu Wei ◽  
Yang Xun
Keyword(s):  
Rolling Horizon ◽  
Hydropower Generation ◽  
Forecast Horizon ◽  
Medium Range ◽  
Quantitative Precipitation Forecasts ◽  
Effective Decision ◽  
Northeast Of China ◽  
Hydropower Reservoir ◽  
Efficiency And Reliability

Abstract This paper presents a rolling horizon control (RHC) model to evaluate the effective forecast horizon (EFH) of 10-day forecast inflows derived from quantitative precipitation forecasts (QPFs) and the effective decision horizon (EDH) for hydropower generation. This paper takes the Huanren hydropower reservoir located in the northeast of China as a case study. Firstly, the 10-day forecast inflows are derived from the QPFs. Then the hydropower generation processes are simulated by the RHC model, and the performances of hydropower generation with different EFHs and EDHs are evaluated, respectively. The results show that: (1) the RHC can adapt to varying conditions by re-optimizing the decisions during the EFH; (2) with the EFH increasing, the hydroelectric reliability increases and the efficiency decreases, while the efficiency and reliability are improved with shortened the EDH.

scholarly journals Nonhomogeneous Boosting for Predictor Selection in Ensemble Postprocessing

2016 ◽  
Vol 145 (1) ◽  
pp. 137-147 ◽  
Author(s):  
Jakob W. Messner ◽  
Georg J. Mayr ◽  
Achim Zeileis
Keyword(s):  
Regression Coefficients ◽  
Maximum Temperature ◽  
Ensemble Forecasts ◽  
Forecast Performance ◽  
Weather Forecasts ◽  
Medium Range ◽  
Important Input ◽  
Boosting Algorithm ◽  
Input Variables

Abstract Nonhomogeneous regression is often used to statistically postprocess ensemble forecasts. Usually only ensemble forecasts of the predictand variable are used as input, but other potentially useful information sources are ignored. Although it is straightforward to add further input variables, overfitting can easily deteriorate the forecast performance for increasing numbers of input variables. This paper proposes a boosting algorithm to estimate the regression coefficients, while automatically selecting the most relevant input variables by restricting the coefficients of less important variables to zero. A case study with ensemble forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) shows that this approach effectively selects important input variables to clearly improve minimum and maximum temperature predictions at five central European stations.

scholarly journals An Integrated Fuzzy Model for Selecting Resilient Suppliers in Electronics Industry of Iran

Logistics ◽  
2021 ◽  
Vol 5 (4) ◽  
pp. 71
Author(s):  
Hamzeh Aghababayi ◽  
Mohsen Shafiei Shafiei Nikabadi
Keyword(s):  
Real Life ◽  
Fuzzy Model ◽  
Electronics Industry ◽  
Electronic Industry ◽  
Chain Management ◽  
Fuzzy Environment ◽  
Proposed Model ◽  
Delay Times ◽  
Effective Decision

Selecting appropriate and resilient suppliers is an important issue in supply chain management (SCM) literature. Making an effective decision on this issue can decrease external risks and disruptions, purchase costs, and delay times and also guarantees business continuity in the event of disruptions and, consequently, increases company competitiveness and customer satisfaction. This paper aims to provide a model based on identifying and investigating related criteria to evaluate suppliers’ resilience and select the most resilient suppliers in Iran’s electronic industry. To this purpose, the screening technique, the best–worst methodology (BWM), and goal programming (GP) have been applied in the fuzzy environment. The proposed model has been implemented and demonstrated by a case study of the electronic industry, as a real-life example. The results show that agility (0.227), compatibility (0.153), and vulnerability (0.102) are the most important factors for a resilient supplier.

Designing a Business Analytics Culture in Organizations in India

2021 ◽  
pp. 670-678
Author(s):  
Tanushri Banerjee ◽  
Arindam Banerjee
Keyword(s):  
Decision Making ◽  
Decision Makers ◽  
Data Organization ◽  
Business Analytics ◽  
Domain Specific ◽  
Consumer Lending ◽  
Effective Decision ◽  
Effective Decision Making

There are several challenges faced by decision makers while deploying Business Analytics in their organization. There may not be one resolution approach that is suitable for creating a Business Analytics culture in all organizations. However, it is easy to perceive that most India-based organizations may have similar issues of data organization that may be impeding their progression in the field of Analytics. Based on their research, the authors have proposed a framework for adoption of Analytics in Indian firms in their book “Weaving Analytics for Effective Decision Making” by SAGE. They propose to use that model for explaining certain domain specific adoption of Business Analytics in organizations in India. They have used a case study of a Global Bank which is in the process of establishing its consumer lending USA operations, an offshore captive operation, in India to describe the process of building an Analytics team in an organization in India. Data processed using R has been added as screenshots for supporting the findings.

A descriptive case study of effective decision-making of a local government organization in the United States of America

2020 ◽  
Vol 16 (3) ◽  
pp. 279-297
Author(s):  
Jennifer Capler
Keyword(s):  
United States ◽  
Decision Making ◽  
Local Government ◽  
United States Of America ◽  
The United States ◽  
Content Type ◽  
Government Organizations ◽  
Effective Decision ◽  
Effective Decision Making

PurposeThis article details a qualitative descriptive case study of affective factors of effective decision-making of one local government organization in the United States of America. The specific problem was that many elected American local government representatives lack effective decision-making strategies. This research focus indicated a lack of qualitative research on the real-world experience of factors that were taken into consideration during decision-making within American local government organizations.Design/methodology/approachUsing a local government organization in southwest Illinois, elected representatives were interviewed and observed. The interviews and observations surfaced how the representatives made decisions. Data were analyzed using manual coding and theming to determine themes and patterns.FindingsThe results produced six themes about factors, including emotional intelligence, which impacted decision-making. They are: (1) remembering the past, (2) communication and respect, (3) spurring economic growth and development, (4) fairness, (5) recognizing and removing emotions and bias and (6) accountability.Research limitations/implicationsBeing a single case study, this research is limited in generalization. The research was limited to the identification of current, real-world experience of elected local government representatives.Practical implicationsThe findings of this research can be used to create more effective decision-making practices for local government organizations of similar size.Originality/valueThis is the first study to review, in-depth, the decision-making and emotional intelligence factors of local government organizations in the United States of America. The conceptual background, discussion, implications to local government organizations, limitations and recommendations for future studies are discussed.

Artificial neural networks approaches for predicting the potential for hydropower generation: a case study for Amazon region

2019 ◽  
Vol 36 (6) ◽  
pp. 5757-5772 ◽  
Author(s):  
Márcio Nirlando Gomes Lopes ◽  
Brígida Ramati Pereira da Rocha ◽  
Alen Costa Vieira ◽  
José Alberto Silva de Sá ◽  
Pedro Alberto Moura Rolim ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Amazon Region ◽  
Hydropower Generation ◽  
Artificial Neural
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