Univariate Time Series Prediction of Reactive Power Using Deep Learning Techniques

Hossain, Naheed; Hossain, Syed Raihan; Azad, Ferdous S.

doi:10.1109/icrest.2019.8644160

Cited by 6 publications

(3 citation statements)

References 17 publications

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“…Moreover, the model's efficiency is evaluated by employing a dataset that it has not encountered during training. Finally, the ultimate outcome of a random forest regressor Êi is the average estimate of M trees, as illustrated in Equation (10).…”

Section: Random Forest Regressormentioning

confidence: 99%

“…Consequently, DL models have been posited to address the challenges encountered by ML models. Various applications of DL models for active/reactive energy predictions are made possible by high-resolution measurements [10]. Models based on DL capture complicated time-series features and produce adaptable forecasting by leveraging sophisticated computational capabilities and processing inclusive data inputs.…”

Section: Introductionmentioning

confidence: 99%

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Improved Active and Reactive Energy Forecasting Using a Stacking Ensemble Approach: Steel Industry Case Study

Mubarak,

Sanjari,

Stegen

et al. 2023

Energies

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The prevalence of substantial inductive/capacitive loads within the industrial sectors induces variations in reactive energy levels. The imbalance between active and reactive energy within the network leads to heightened losses, diminished network efficiency, and an associated escalation in operating costs. Therefore, the forecasting of active and reactive energy in the industrial sector confers notable advantages, including cost reduction, heightened operational efficiency, safeguarding of equipment, enhanced energy consumption management, and more effective assimilation of renewable energy sources. Consequently, a range of specialized forecasting methods for different applications have been developed to address these challenges effectively. This research proposes a stacked ensemble methodology, denoted as Stack-XGBoost, leveraging three distinct machine learning (ML) methods: extra trees regressor (ETR), adaptive boosting (AdaBoost), and random forest regressor (RFR), as foundational models. Moreover, the incorporation of an extreme gradient boosting (XGBoost) algorithm as a meta-learner serves to amalgamate the predictions generated by the base models, enhancing the precision of the active/reactive energy consumption forecasting using real time data for steel industry. To assess the efficacy of the proposed model, diverse performance metrics were employed. The results show that the proposed Stack-XGBoost model outperformed other forecasting methods. Additionally, a sensitivity analysis was conducted to assess the robustness of the proposed method against variations in input parameters.

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Section: Random Forest Regressormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improved Active and Reactive Energy Forecasting Using a Stacking Ensemble Approach: Steel Industry Case Study

Mubarak,

Sanjari,

Stegen

et al. 2023

Energies

View full text Add to dashboard Cite

show abstract

“…Using edge detection, segmentation, and object tracking techniques, the system correctly detected accidents. In the year 2019, the research was done in a few papers involving papers by author Muhammad Rizwan et al [8], S. M. Sabrin et al [9], Jihong Wang et al [10], and J. Lee et al [11] have all conducted research in the field of Real-time Vehicle Crash Detection Using ML, based on various techniques such as Deep Learning Techniques and CNN. They discovered ML algorithms are robust for completing the task of vehicle detection, as well as for improving traffic systems around the world.…”

Section: Literature Review (2014-2022)mentioning

confidence: 99%

Real-Time Vehicle Accident Recognition from Traffic Video Surveillance using YOLOV8 and OpenCV

Mane¹,

Sangve

Kandhare

et al. 2023

IJRITCC

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The automatic detection of traffic accidents is a significant topic in traffic monitoring systems. It can reduce irresponsible driving behavior, improve emergency response, improve traffic management, and encourage safer driving practices. Computer vision can be a promising technique for automatic accident detection because it provides a reliable, automated, and speedy accident detection system that can improve emergency response times and ultimately save lives. This paper proposed an ensemble model that uses the YOLOv8 approach for efficient and precise event detection. The model framework's robustness is evaluated using YouTube video sequences with various lighting circumstances. The proposed model has been trained using the open-source dataset Crash Car Detection Dataset, and its produced precision, recall, and mAP are 93.8% and 98%, 96.1%, respectively, which is a significant improvement above the prior precision, recall, and mAP figures of 91.3%, 87.6%, and 93.8%. The effectiveness of the proposed approach in real-time traffic surveillance applications is proved by experimental results using actual traffic video data.

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Rainfall Prediction in Flood Prone Area Using Deep Learning Approach

Ramlan

Deni

2021

Communications in Computer and Information Science

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Univariate Time Series Prediction of Reactive Power Using Deep Learning Techniques

Cited by 6 publications

References 17 publications

Improved Active and Reactive Energy Forecasting Using a Stacking Ensemble Approach: Steel Industry Case Study

Improved Active and Reactive Energy Forecasting Using a Stacking Ensemble Approach: Steel Industry Case Study

Real-Time Vehicle Accident Recognition from Traffic Video Surveillance using YOLOV8 and OpenCV

Rainfall Prediction in Flood Prone Area Using Deep Learning Approach

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