Optimal Participation of the Renewable Energy in Microgrids with Load Management Strategy

Candra, Oriza; Chammam, Abdeljelil; Rahardja, Untung; Ramirez-Coronel, Andrés Alexis; Al-Jaleel, Abdullah Abdul; Al‐Kharsan, Ibrahim H.; Muda, Iskandar; Derakhshani, Ghasem B; Rezai, Mohamad Mehdi

doi:10.2478/rtuect-2023-0005

Cited by 8 publications

(3 citation statements)

References 36 publications

(26 reference statements)

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“…However, with the advancement of technology, new modeling types known as ensemble learning have emerged (Bintoro et al, 2023). Many studies have been conducted to investigate whether these ensemble learning models can accurately predict crises (Candra et al, 2023).…”

Section: Literature Review and Hypotheses Developmentmentioning

confidence: 99%

Ensemble learning with imbalanced data handling in the early detection of capital markets

Mukhlashin,

Fitrianto,

Soleh

et al. 2023

JAI

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Research aims: This study aims to create an early detection model to predict events in the Indonesian capital market.Design/Methodology/Approach: A quantitative study comparing ensemble learning models with imbalanced data handling detected early capital market events. This study used five ensemble learning models—Random Forest, ExtraTrees, CatBoost, XGBoost, and LightGBM—to detect early events in the Indonesian capital market by handling imbalanced data, such as under sampling (RUS), oversampling (SMOTE, SMOTE-Broder, ADASYN), and over-under sampling (SMOTE-Tomek, SMOTE-ENN), weighted (class weight). Global and regional stock markets, commodities, exchange rates, technical indicators, sectoral indices, JCI leaders, MSCI, net buys of foreign stocks, national securities, and national share ownership all predicted the lowest return of Crisis Management Protocol (CMP) binary responses.Research findings: Hyperparameters and thresholds were tuned to produce the optimum model. The best model had the highest G-mean. ExtraTrees with SMOTE-ENN predicted the highest number of one-day events, with a G-Mean of 96.88%. LightGBM with SMOTE handling best predicted five-day events with an 89.21% G-Mean. With a G-Mean of 89.49%, CatBoost with SMOTE-Border handling was the best for a 15-day event. In addition, LightGBM with SMOTE-Tomek handling and 68.02% G-Mean was best for 30-day events. Further, performance evaluation scores decreased with increased prediction time.Theoretical contribution/Originality: This work relates more imbalance handling methods and ensemble learning to capital market early detection cases.Practitioner/Policy implication: Capital markets can indicate economic stability. Maintaining capital market efficacy and economic value requires a system to detect pressure.Research limitation/Implication: This study used ensemble learning models to predict capital market events 1, 5, 15, and 30 days ahead, assuming Indonesian working days. The model's forecast results are expected to be utilized to monitor the capital market and take precautions.

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Section: Literature Review and Hypotheses Developmentmentioning

confidence: 99%

Ensemble learning with imbalanced data handling in the early detection of capital markets

Mukhlashin,

Fitrianto,

Soleh

et al. 2023

JAI

View full text Add to dashboard Cite

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“…The platform's regulatory framework categorizes products into distinct groups: "Prohibited Products" that are banned due to local laws, "Restricted Products" requiring TikTok Shop's prior approval, "Unsupported Products" that are not yet allowed, and "Invitation Only Products" available exclusively to a select group of sellers. This system ensures a controlled and quality-assured marketplace [13] [14].…”

Section: Introductionmentioning

confidence: 99%

Examining Influencers Role in TikTok Shop’s Promotional Strategies and Consumer Purchases

Ronal Aprianto,

Ade Famalika,

Irma Idayati

et al. 2024

ATT

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TikTok Shop, a new feature by TikTok, facilitates direct transactions within the app, representing an innovative entrepreneurship venture. This study assesses how product quality and free shipping promotions influence purchase decisions, with influencers as mediators, a key element in digital entrepreneurship. Survey- ing 352 students from PTS Lubuklinggau City through online questionnaires, the research adopted a quantitative approach using SEM-PLS analysis via Smart- PLS software. Findings indicate that product quality significantly positively affects influencers, encouraging them to endorse products, a testament to the entrepreneurship success of quality management. Similarly, free shipping pro- motions positively impact influencers, highlighting their effectiveness in influ- encer marketing and entrepreneurship marketing strategies. Importantly, product quality significantly influences consumer purchasing decisions, showcasing its critical role in sales success and entrepreneurship value creation. However, the impact of free shipping on purchase decisions, while positive, is not significant, suggesting that it alone may not drive consumer buying behavior. Influencers significantly affect purchasing decisions, directly and indirectly, demonstrating their crucial role in marketing strategies and entrepreneurship ecosystems. They enhance the effect of product quality and free shipping on consumer choices, acting as a bridge between the product features and the consumers’ decisions to buy. This study reveals the complex dynamics of influencer marketing within the TikTok Shop ecosystem, emphasizing the importance of product quality and strategic promotions in driving purchase decisions and fostering entrepreneur- ship growth.

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“…By leveraging mobile crowdsourcing, we minimize the resources required for extensive forensic analyses and engage a diverse range ❒ E-ISSN: 2656-8888 | P-ISSN: 2655-8807 of participants in the forensic process. Such forged audio files can cause significant problems when used as critical data such as court evidence [6]. In such situations, experts expend significant time and effort to determine whether audio recording files have been tampered with [7].…”

Section: Introductionmentioning

confidence: 99%

Automated Detection of Container-based Audio Forgery Using Crowdsourcing for Dataset Building

Son,

Baek,

Park

2024

ATT

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This research paper introduces a new approach for detecting digital audio file forgeries, highlighting entrepreneurship spirit through innovation and market adaptability. It utilizes a cyclical system aided by mobile crowdsourcing to collect a comprehensive dataset of smartphone recordings, demonstrating an entrepreneurship approach to leveraging technology for scalable solutions. Digital forgery, an area ripe for entrepreneurship investment, is becoming increasingly accessible, making it challenging to detect manipulated audio files. This new method emphasizes the utility of metadata and file structure analysis and is scalable, reflecting an entrepreneurship mindset in creating adaptable and sustainable solutions. The researchers developed a mobile web-based prototype system to collect diverse audio data and automatically detect forgeries, showcasing their initiative and innovative thinking. They also conducted scenario-based testing to validate the effectiveness of their methodology, a step that underscores the entrepreneurship value of practical validation. This approach has the potential to significantly advance digital forensic practices by enabling broader detection of manipulated audio files, opening avenues for entrepreneurship ventures in digital security. The collected dataset will be made publicly available and serve as a valuable resource for future forensic investigations, encouraging entrepreneurship collaboration and knowledge sharing. The paper lays the groundwork for future research in expanding the scope of digital forensics, fostering technological innovation, and enhancing participatory models for data collection, all of which are essential elements in entrepreneurship ecosystems.

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Optimal Participation of the Renewable Energy in Microgrids with Load Management Strategy

Cited by 8 publications

References 36 publications

Ensemble learning with imbalanced data handling in the early detection of capital markets

Ensemble learning with imbalanced data handling in the early detection of capital markets

Examining Influencers Role in TikTok Shop’s Promotional Strategies and Consumer Purchases

Automated Detection of Container-based Audio Forgery Using Crowdsourcing for Dataset Building

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