Big Data and Monitoring the Grid

Phan, Sonal Kothari; Chen, Cathy

doi:10.1016/b978-0-12-805321-8.00009-4

Cited by 9 publications

(2 citation statements)

References 29 publications

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“…Energy consumption and generation forecast has been a topic of research for many years, especially at a macro level, considering the energy provision needs of the public grids and their interconnections across different countries. In order to properly plan the activation of different electricity supply sources, namely dispatchable generation, there was always a strong requirement for the prediction of energy consumption in electricity grids [8]. The same concerns are present for the electricity retailers that need to negotiate future energy supply for their clients, e.g., in day-ahead markets.…”

Section: Related Workmentioning

confidence: 99%

A Machine Learning-Based Electricity Consumption Forecast and Management System for Renewable Energy Communities

Matos,

Almeida,

Gonçalves

et al. 2024

Energies

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The energy sector is currently undergoing a significant shift, driven by the growing integration of renewable energy sources and the decentralization of electricity markets, which are now extending into local communities. This transformation highlights the pivotal role of prosumers within these markets, and as a result, the concept of Renewable Energy Communities is gaining traction, empowering their members to curtail reliance on non-renewable energy sources by facilitating local energy generation, storage, and exchange. Also in a community, management efficiency depends on being able to predict future consumption to make decisions regarding the purchase, sale and storage of electricity, which is why forecasting the consumption of community members is extremely important. This study presents an innovative approach to manage community energy balance, relying on Machine Learning (ML) techniques, namely eXtreme Gradient Boosting (XGBoost), to forecast electricity consumption. Subsequently, a decision algorithm is employed for energy trading with the public grid, based on solar production and energy consumption forecasts, storage levels and market electricity prices. The outcomes of the simulated model demonstrate the efficacy of incorporating these techniques, since the system showcases the potential to reduce both the community electricity expenses and its dependence on energy from the centralized distribution grid. ML-based techniques allowed better results specially for bi-hourly tariffs and high storage capacity scenarios with community bill reductions of 9.8%, 2.8% and 5.4% for high, low, and average photovoltaic (PV) generation levels, respectively.

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Section: Related Workmentioning

confidence: 99%

A Machine Learning-Based Electricity Consumption Forecast and Management System for Renewable Energy Communities

Matos,

Almeida,

Gonçalves

et al. 2024

Energies

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“…In the next step, the Hilbert transform (HT) includes each IMF's orthogonal pair with 90 degrees difference in the phase [26]. As a result, each IMF set and the corresponding pair can evaluate instant variations of signal magnitude and frequency concerning time.…”

Section: Introductionmentioning

confidence: 99%

A data-driven approach for linear and nonlinear damage detection using variational mode decomposition and GARCH model

Gharehbaghi

Kalbkhani

Farsangi

et al. 2022

Engineering with Computers

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In this article, an original data-driven approach is proposed to detect both linear and nonlinear damages in structures using output-only responses. The method deploys variational mode decomposition (VMD) and generalized autoregressive conditional heteroscedasticity (GARCH) model for signal processing and feature extraction. To this end, VMD decomposes the responsesignals are first decomposed to intrinsic mode functions (IMFs), and then, GARCH model is utilized to represent the statistics of IMFs. The model coefficients' of IMFs construct the primary feature vector. Kernel-based principal component analysis (PCA) and linear discriminant analysis (LDA) are utilized to reduce the redundancy from the primary features by mapping them to the new feature space. The informative features are then fed separately into three supervised classifiers: support vector machine (SVM), k-nearest neighbor (kNN), and fine tree. The performance of the proposed method is evaluated on two experimental scaled models in terms of linear and nonlinear damage assessment. Kurtosis and ARCH tests proved the compatibility of GARCH model. The results demonstrate that the proposed technique reaches the accuracy of 100% and 98.82% in classifying linear and nonlinear damages, respectively. Also, its accuracy is higher than 80% in the presence of noise with a signal-to-noise ratio (SNR) higher than 10 dB.

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Environment 4.0: How digitalization and machine learning can improve the environmental footprint of the steel production processes

Colla

Pietrosanti

Malfa³

et al. 2020

Matériaux & Techniques

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The concepts of Circular Economy and Industrial Symbiosis are nowadays considered by policy makers a key for the sustainability of the whole European Industry. However, in the era of Industry4.0, this results into an extremely complex scenario requiring new business models and involve the whole value chain, and representing an opportunity as well. Moreover, in order to properly consider the environmental pillar of sustainability, the quality of available information represents a challenge in taking appropriate decisions, considering inhomogeneity of data sources, asynchronous nature of data sampling in terms of clock time and frequency, and different available volumes. In this sense, Big Data techniques and tools are fundamental in order to handle, analyze and process such heterogeneity, to provide a timely and meaningful data and information interpretation for making exploitation of Machine Learning and Artificial Intelligence possible. Handling and fully exploiting the complexity of the current monitoring and automation systems calls for deep exploitation of advanced modelling and simulation techniques to define and develop proper Environmental Decision Support Systems. Such systems are expected to extensively support plant managers and operators in taking better, faster and more focused decisions for improving the environmental footprint of production processes, while preserving optimal product quality and smooth process operation. The paper describes a vision from the steel industry on the way in which the above concepts can be implemented in the steel sector through some application examples aimed at improving socio-economic and environmental sustainability of production cycles.

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Big Data and Monitoring the Grid

Cited by 9 publications

References 29 publications

A Machine Learning-Based Electricity Consumption Forecast and Management System for Renewable Energy Communities

A Machine Learning-Based Electricity Consumption Forecast and Management System for Renewable Energy Communities

A data-driven approach for linear and nonlinear damage detection using variational mode decomposition and GARCH model

Environment 4.0: How digitalization and machine learning can improve the environmental footprint of the steel production processes

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