Multi-Term Electrical Load Forecasting of Smart Cities Using a New Hybrid Highly Accurate Neural Network-Based Predictive Model

Smart Grids and Sustainable Energy, Journal Year: 2023, Volume and Issue: 9(1)

Published: Dec. 28, 2023

Language: Английский

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Net saving improvement of capacitor banks in power distribution systems by increasing daily size switching number: A comparative result analysis by artificial intelligence

The Journal of Engineering, Journal Year: 2024, Volume and Issue: 2024(2)

Published: Feb. 1, 2024

Abstract This paper studies the effect of number switching (NOS) per day capacitor banks on loss reduction in radial distribution systems. To this aim, daytime (more precisely, 24 h) is divided into different numbers time segments (equal to same NOS) for capacitors’ size switching. The resulting non‐linear programming with discontinuous derivatives (called DNLP) model solved subject related constraints. results reveal impact hourly further (namely 118.4435, 83.7856, and 101.738 MWh three IEEE systems) higher net savings (i.e. k$5.6067, k$4.2772, k$5.3542 systems compared daily Then, hyper‐tuned Random Forest trained based 69‐bus network, fine‐tuned by 10‐bus fitted 33‐bus network have an intelligent multi‐classification task highest accuracy. Numerical simulation, both classic parts, presented demonstrate performance DeepOptaCap. For final step, DeepOptaCast other models Light Gradient Boosting Method (LGBM), Decision Tree, XGBoost, regarding KPIs mean absolute percentage error, root squared coefficient determination model's superiority.

Language: Английский

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Blockchain Integration and Its Impact on Renewable Energy

Computers, Journal Year: 2024, Volume and Issue: 13(4), P. 107 - 107

Published: April 22, 2024

This paper investigates the evolving landscape of blockchain technology in renewable energy. The study, based on a Scopus database search 21 February 2024, reveals growing trend scholarly output, predominantly engineering, energy, and computer science. diverse range source types global contributions, led by China, reflects interdisciplinary nature this field. comprehensive review delves into 33 research papers, examining integration energy systems, encompassing decentralized power dispatching, certificate trading, alternative selection, management applications like intelligent transportation systems microgrids. papers employ theoretical concepts such as dispatching models permissioned blockchains, utilizing methodologies involving advanced algorithms, consensus mechanisms, smart contracts to enhance efficiency, security, transparency. findings suggest that can reduce costs, increase utilization, optimize management. Despite these advantages, challenges including uncertainties, privacy concerns, scalability issues, consumption are identified, alongside legal regulatory compliance market acceptance hurdles. Overcoming resistance change building trust blockchain-based crucial for successful adoption, emphasizing need collaborative efforts among industry stakeholders, regulators, developers unlock full potential blockchains integration.

Language: Английский

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Advanced Machine Learning-Driven Security and Anomaly Identification in Inverter-Based Cyber-Physical Microgrids

Electric Power Components and Systems, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 18

Published: May 8, 2024

In a landscape dominated by the digitalization of energy networks, safeguarding cyber-physical microgrids and against breaches anomalies emerges as critical imperative. This study pioneers inventive methodologies for Intelligent Detection Intrusions Anomalies in Inverter-centric Cyber-Physical Microgrids, leveraging state-of-the-art machine learning paradigms. Using combined power LSTM Convolutional Neural Networks, our suggested approach finds aberrations microgrid structures with remarkable accuracy efficiency. Additionally, we integrate effectiveness Gradient Boosting Machines to enhance overall detection capabilities. Experimental findings underscore efficacy learning-driven approach, CNN achieving precision 95%, recall 92%, F1-score 93% intrusion detection, while attains 93%, 91%, 92% anomaly identification. Furthermore, GBM contributes an efficiency 96%. Our not only bolsters resilience but also lays foundation secure future. increasingly interconnected world, integration these sophisticated techniques By embracing innovation at convergence systems, stride toward fortified dependable microgrids.

Language: Английский

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NeuroQuMan: quantum neural network-based consumer reaction time demand response predictive management

Neural Computing and Applications, Journal Year: 2024, Volume and Issue: 36(30), P. 19121 - 19138

Published: Aug. 2, 2024

Language: Английский

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Voltage Controller Design for Offshore Wind Turbines: A Machine Learning-Based Fractional-Order Model Predictive Method

Fractal and Fractional, Journal Year: 2024, Volume and Issue: 8(8), P. 463 - 463

Published: Aug. 6, 2024

Integrating renewable energy sources (RESs), such as offshore wind turbines (OWTs), into the power grid demands advanced control strategies to enhance efficiency and stability. Consequently, a Deep Fractional-order Wind turbine eXpert system (DeepFWX) model is developed, representing hybrid proportional/integral (PI) fractional-order (FO) predictive random forest alternating current (AC) bus voltage controller designed explicitly for OWTs. DeepFWX aims address challenges associated with systems, focusing on achieving smooth tracking state estimation of AC voltage. Extensive comparative analyses were performed against other state-of-the-art intelligent models assess effectiveness DeepFWX. Key performance indicators (KPIs) MAE, MAPE, RMSE, RMSPE, R2 considered. Superior across all evaluated metrics was demonstrated by DeepFWX, it achieved MAE [15.03, 0.58], MAPE [0.09, 0.14], RMSE [70.39, 5.64], RMSPE [0.34, 0.85], well [0.99, 0.99] systems states [X1, X2]. The proposed approach anticipates capabilities FO modeling, control, algorithms achieve precise voltage, thereby enhancing overall stability OWTs in evolving sector integration.

Language: Английский

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Design of a Dynamic Feedback LSTM Electricity Price Forecast of Smart Grids

Power systems, Journal Year: 2024, Volume and Issue: unknown, P. 327 - 344

Published: Jan. 1, 2024

Language: Английский

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Hybrid emerging model predictive data-driven forecasting of three-phase electrical signals of photovoltaic systems using GBR-LSTM

e-Prime - Advances in Electrical Engineering Electronics and Energy, Journal Year: 2024, Volume and Issue: 8, P. 100549 - 100549

Published: April 23, 2024

In numerous industrial contexts, precise analysis and forecasting of electrical signals within three-phase systems are indispensable. As a result, this work presents DeepPhase, hybrid framework that combines Long Short-Term Memory (LSTM) neural networks with gradient-boosted regression (GBR) to predict the current, voltage, power signals. The performance model is evaluated in comparison benchmark models, namely Bidirectional LSTM (BiLSTM), K-Nearest Neighbors (KNN), LSTM, which utilize essential Key Performance Indicators (KPIs). demonstrated by its highest Coefficient Determination (R2) 0.999, Mean Absolute Error (MAE) 6.94 × 10−5, Percentage (MAPE) 0.07%, Root Square (RMSE) 0.000156, DeepPhase consistently exhibits predictive precision. For Three-Phase Current, MAE 2.13 10−3, MAPE 0.01%, RMSE 0.062432, R2 0.960596; for Voltage, 9.52E-03, 0.03%, 0.014, 0.999. results study highlight effectiveness analyzing dynamics complex This has significant implications improving decision-making control strategies systems.

Language: Английский

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FaultyVoltaMan: Ensemble Learning Model for Accurate Fault Detection and Classification of PV-Integrated Systems

Published: June 17, 2024

Language: Английский

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DeepEMS: Multimodal optimal energy management of microgrid systems based on a hybrid multi‐stage machine learning model

The Journal of Engineering, Journal Year: 2024, Volume and Issue: 2024(10)

Published: Oct. 1, 2024

Abstract The effective management of microgrids is important towards transition to sustainable energy paradigm. By optimizing the utilization different sources, such as solar photovoltaic panels and storages, it improves reliability grid develops resiliency in dealing with challenges unexpected variations demand. To this end, proposed paper presents DeepEMS, a system developed manage through incorporation diverse intelligent algorithms. DeepEMS provides dynamic microgrid Bidirectional Long Short‐Term Memory (BiLSTM) networks, Sliding Linear Programming (SLP), Random Forest (RF). implementing these methodologies, can optimize consumption throughout by dynamically identifying coordinating needs various sources. achieves precise multimodal optimization facilitates integration storage systems, interactions, renewable sources (RES), demonstrated simulations data analytics. presented performance control, resource allocation, management, utilization. Furthermore, comparative analysis alternative models including XGBoost, Light GBM, RF, Decision Trees, consistently higher measured several key indicators (KPIs).

Language: Английский

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