Arquitetura Para Identificar E Estimar Regiões De Faltas Permanentes Em Média Tensão: Uma Contribuição Da Plataforma Smatlvgrid / Architecture to Identify and Estimate Regions of Permanent Faults in Medium Voltage: A Contribution of the Smatlvgrid Platform

Silva, Daniel Sousa da; Nascimento, Lennon Brandão Freitas do; Fernandes, Rubens de Andrade; Gomes, Raimundo Cláudio Souza; Torné, Israel Gondres

doi:10.34117/bjdv7n3-272

Cited by 1 publication

(3 citation statements)

References 5 publications

(8 reference statements)

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“…The literature presents applications of the SmartLVGrid metamodel used for the management, control, and energy monitoring of power distribution systems and building systems [3,4,12]. In [5], we presented a data-driven energy management strategy by monitoring real-time energy demand in each circuit of a building installation based on the aforementioned metamodel.…”

Section: Proposed Demand Forecast Strategymentioning

confidence: 99%

“…In this algorithm, the maximum number of instances possible is considered within a margin of , with the aim of determining weights and biases, that provides the generalization for the model. To achieve this, the objective is to minimize the error J(w, w 0 , ξ, ξ) given by Equation (12), where ξ n and ξn are the slack variables corresponding to a deviation from the margin, with the penalty control given by C, constrained by Equations ( 13)- (15).…”

Section: Support Vector Regression (Svr)mentioning

confidence: 99%

“…However, to perform retrofitting systematically, allowing flexibility, scalability, and standardized integration with legacy systems, a reference model with well-defined protocols and interfaces is required. The SmartLVGrid metamodel enables the digital convergence of electrical systems to the smart grids paradigm [3,12]. In the literature, this metamodel has been used to achieve smart building convergence in legacy buildings to promote energy efficiency through resources for managing energy demand and electrical parameters in building installations [4,5].…”

Section: Introductionmentioning

confidence: 99%

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A Demand Forecasting Strategy Based on a Retrofit Architecture for Remote Monitoring of Legacy Building Circuits

et al. 2023

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Energy demand forecasting is crucial for planning and optimizing the use of energy resources in building facilities. However, integrating digital solutions and learning techniques into legacy buildings presents significant challenges due to limited or outdated resources, hampering predictive analytics in these buildings and their circuits. To fill this gap, this article proposes an innovative demand forecasting strategy using an AIoT retrofit architecture based on the SmartLVGrid metamodel. This architecture allows remote monitoring of legacy building circuits, facilitating the collection, processing and storage of data in the cloud. We use several learning algorithms, including linear regression, support vector regressor, random forest regressor, XGBoost regressor, and long short-term memory (LSTM) neural network, to predict energy demand 15 min ahead, identifying potential overruns of contracted demand in accordance with Brazilian regulations. After Bayesian optimization, the LSTM neural network outperformed other models for most of the selected datasets and detected 32 out of 38 demand overruns on the test set. XGBoost and random forest followed closely, detecting 30 demand overruns. Overall, our cost-effective solution optimizes energy usage and efficiently mitigates potential demand exceedances in building installations. This is achieved through a step-by-step approach to upgrading existing aging facilities, which promotes energy efficiency and sustainability.

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