Exploring distributed energy generation for sustainable development: A data mining approach

Gawusu, Sidique; Mensah, Rhoda Afriyie; Das, Oisik

doi:10.1016/j.est.2022.104018

Cited by 21 publications

(5 citation statements)

References 108 publications

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“…One is the improper selection of load density index in spatial load forecasting. Second, the time scale for checking saturated load forecasting with short-term and medium-term total load forecasting is not correct [6].…”

Section: Traditional Distribution Network Power Balance Methodsmentioning

confidence: 99%

Distribution Network Balance Method under High Proportion of Distributed Power Penetration Scenario

Tian

Liang

Feng-xiao

et al. 2023

J. Phys.: Conf. Ser.

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With the rapid development of various subjects represented by distributed generation, how to implement power balance in a distribution network has become a research direction. The utilization efficiency of a distribution network can be improved by the bidirectional power flow of flexible resources. However, the traditional distribution network power balance method relies on historical data, making it challenging to determine the principle of new subject participation. It does not meet the requirements of bidirectional power flow balance and loses critical information such as complementary load characteristics. We believe that distribution network power prediction needs to transform from single load prediction to multi-resource prediction, from quantitative prediction to quantitative plus characteristic prediction. In comparison to the traditional methods, curve superposition can be better compatible with the research results of different new load forecasting topics and meet the needs of efficiency and precise network planning.

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Section: Traditional Distribution Network Power Balance Methodsmentioning

confidence: 99%

Distribution Network Balance Method under High Proportion of Distributed Power Penetration Scenario

Tian

Liang

Feng-xiao

et al. 2023

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

show abstract

“…Furthermore, this approach has been extensively tested in noisy environments, and the pyramidal UWT-SGBT method demonstrates superior noise immunity compared to other machine learning methods that utilize wavelet transform (WT)-based techniques. In Gawusu et al (2022) The study delves into the exploration of patterns and trends in dispersed generation through data mining (DG). While still at an early stage, this research provides valuable insights into ongoing research trends and patterns.…”

Section: Related Workmentioning

confidence: 99%

Optimal placement of distributed generation in power distribution system and evaluating the losses and voltage using machine learning algorithms

Jain,

Gupta

2024

Front. Energy Res.

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As the modern power system continues to grow in size, complexity, and uncertainty, traditional methods may occasionally prove insufficient in addressing the associated challenges. The improper location of distributed generation varies the voltage profile, increases losses and compromises network capacity. Machine learning algorithms predict accurate site positions, and network reconfiguration improves the capacity of the power system. The proposed algorithm is a hybrid of machine learning and deep learning algorithms. It cascades Support Vector Machine as the main model and uses Random Forest and Radial Neural Networks as classification algorithms for accurately predicting DG position. The non-linearity characteristics of the DG problem are directly mapped to the proposed algorithms. The proposed algorithm is employed on familiar test setups like the IEEE 33-bus and 69-bus distribution systems using MATLAB R2017 as simulation software. The R-squared (R2) values for all parameters yield a value of 1, while the MAPE values are minimal for the proposed cascaded algorithm in contrast to other algorithms of LSTM, CNN, RNN and DQL.

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“…This type of operation is what is known technologically as "data mining". (Gawusu, Mensah & Das, 2022).…”

Section: Data Analytics and Dashboard Presentationmentioning

confidence: 99%

Sustainable manufacturing in the fourth industrial revolution: A big data application proposal in the textile industry

González

Hernández²,

Vásquez³

et al. 2022

JIEM

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Purpose: Design an industrial production model with a focus on industry 4.0 (Big Data) and decision-making analysis for small and medium-sized enterprises (SMEs) in the clothing sector that allows improving procedures, jobs and related costs within the study organization Develop a sustainable manufacturing proposal for the industrial textile sector with a focus on Big data (entry, transformation, data loading and analysis) in organizational decision making, in search of time and cost optimization and environmental impact mitigation related.Design/methodology/approach: The present research, of an applied nature, raises a value proposition focused on the planning, design and structuring of an industrial model focused on Big Data, specifically in the apparel manufacturing sector for decision-making in a structured and automated way with the methodological approach to follow: 1) Approach of production strategies oriented in Big Data for the textile sector; 2) Definition of the production model and configuration of the operational system; 3) Data science and industrial analysis, 4) Production model approach (Power BI) and 5) model validation. Methodological design of the investigation. 1) Presentation of the case study, where the current situational analysis of the company is carried out, formulation of the problem and proposal of solution for the set of data analyzed; 2) Presentation of a solution proposal focused on Big Data, on the identification of the industrial ecosystem and integration with the company's information systems, as well as the solution approach in the study and science of data in real time; 3) Presentation of the Model proposal for SQL structured databases in the loading, transformation and loading of important information for this study; 4) Information processing, in the edition of data in the M language of Power BI software, construction and elaboration of the model; 5) Presentation of the related databases, in the integration with the foreign key of the Master table and the transactional Tables; 6) Data analysis and presentation of the Dashboard, in the design, construction and analysis of the related study variables, as well as the approach of solution scenarios in the correct organizational decision makingFindings: The results obtained show an improvement in operational efficiency from the value-added proposal. Research limitations/implications: Currently, the number of studies applying Big Data technology for organizations in the textile and manufacturing sector in organizational decision making are limited. If analyzed from the local scene, there are few cases of Big Data implementation in the textile sector, as a consequence of the lack of projects and financing of value propositions. Another limiting factor in this research is the absence of digital information of high relevance for study and analysis, which leads to longer times in data entry and placement in information systems in real time. Finally, there is no data organizational culture, where there are processes and/or procedures for data registration and its transformation into clean data.Originality/value: This research integrates, as well as the correct organizational decision making For the verification of originality, the project search and systematic review of literature in the main online search engines are carried out for this research; In addition, the percentages of coincidence with online reviewers such as turnitin and plag.es are reviewed in the transparency of this study project.

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Exploring distributed energy generation for sustainable development: A data mining approach

Cited by 21 publications

References 108 publications

Distribution Network Balance Method under High Proportion of Distributed Power Penetration Scenario

Distribution Network Balance Method under High Proportion of Distributed Power Penetration Scenario

Optimal placement of distributed generation in power distribution system and evaluating the losses and voltage using machine learning algorithms

Sustainable manufacturing in the fourth industrial revolution: A big data application proposal in the textile industry

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