Predicting product quality in continuous manufacturing processes using a scalable robust Gaussian Process approach

Echeverria-Rios, Diego; Green, Peter L.

doi:10.1016/j.engappai.2023.107233

Cited by 2 publications

(1 citation statement)

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“…The AV is based mostly on statistical concepts, providing a strong framework for expressing the joint probability distribution of several variables in the VPP. The AV in this study is constructed using the Gaussian Distribution (GD) 44 , with probability distribution in Eq. ( 23), with µ ss the mean, which represents the central location of the distribution, σ as the standard deviation which determines the spread or dispersion of the distribution (17) (17a) www.nature.com/scientificreports/…”

Section: Vpp Data Security Formulationmentioning

confidence: 99%

Developing a three stage coordinated approach to enhance efficiency and reliability of virtual power plants

Amissah,

Abdel-Rahim,

Mansour

et al. 2024

Sci Rep

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A Virtual Power Plant (VPP) is a centralized energy system that manages, and coordinates distributed energy resources, integrating them into a unified entity. While the physical assets may be dispersed across various locations, the VPP integrates them into a virtual unified entity capable of responding to grid demands and market signals. This paper presents a tri-level hierarchical coordinated operational framework of VPP. Firstly, an Improved Pelican Optimization Algorithm (IPOA) is introduced to optimally schedule Distributed Energy Resources (DERs) within the VPP, resulting in a significant reduction in generation costs. Comparative analysis against conventional algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) demonstrates IPOA's superior performance, achieving an average reduction of 8.5% in generation costs across various case studies. The second stage focuses on securing the optimized generation data from rising cyber threats, employing the capabilities of machine learning, preferably, a convolutional autoencoder to learn the normal patterns of the optimized data to detect deviations from the optimized generation data to prevent suboptimal decisions. The model exhibits exceptional performance in detecting manipulated data, with a False Positive Rate (FPR) of 1.92% and a Detection Accuracy (DA) of 98.06%, outperforming traditional detection techniques. Lastly, the paper delves into the dynamic nature of the day ahead market that the VPP participates in. In responding to the grid by selling its optimized generated power via the day-ahead market, the VPP employs the Prophet model, another machine learning technique to forecast the spot market price for the day-ahead to mitigate the adverse effects of price volatility. By utilizing Prophet forecasts, the VPP achieves an average revenue increase of 15.3% compared to scenarios without price prediction, emphasizing the critical role of predictive analytics in optimizing economic gains. This tri-level coordinated approach adopted addresses key challenges in the energy sector, facilitating progress towards achieving universal access to clean and affordable energy.

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Section: Vpp Data Security Formulationmentioning

confidence: 99%