Review and prospect of data-driven techniques for load forecasting in integrated energy systems

Applied Energy, Journal Year: 2022, Volume and Issue: 321, P. 119269 - 119269

Published: June 5, 2022

Language: Английский

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Machine Learning and Deep Learning in Energy Systems: A Review

Sustainability, Journal Year: 2022, Volume and Issue: 14(8), P. 4832 - 4832

Published: April 18, 2022

With population increases and a vital need for energy, energy systems play an important decisive role in all of the sectors society. To accelerate process improve methods responding to this increase demand, use models algorithms based on artificial intelligence has become common mandatory. In present study, comprehensive detailed study been conducted applications Machine Learning (ML) Deep (DL), which are newest most practical Artificial Intelligence (AI) systems. It should be noted that due development DL algorithms, usually more accurate less error, these ability model solve complex problems field. article, we have tried examine very powerful problem solving but received attention other studies, such as RNN, ANFIS, RBN, DBN, WNN, so on. This research uses knowledge discovery databases understand ML systems’ current status future. Subsequently, critical areas gaps identified. addition, covers efficient used field; optimization, forecasting, fault detection, investigated. Attempts also made cover their evaluation metrics, including not only important, newer ones attention.

Language: Английский

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Building energy prediction using artificial neural networks: A literature survey

Energy and Buildings, Journal Year: 2021, Volume and Issue: 262, P. 111718 - 111718

Published: Nov. 26, 2021

Language: Английский

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Load Forecasting Techniques for Power System: Research Challenges and Survey

IEEE Access, Journal Year: 2022, Volume and Issue: 10, P. 71054 - 71090

Published: Jan. 1, 2022

The main and pivot part of electric companies is the load forecasting. Decision-makers think tank power sectors should forecast future need electricity with large accuracy small error to give uninterrupted free shedding consumers. demand can be forecasted amicably by many Machine Learning (ML), Deep (DL) Artificial Intelligence (AI) techniques among which hybrid methods are most popular. present technologies forecasting work regarding combination various ML, DL AI algorithms reviewed in this paper. comprehensive review single models functions; advantages disadvantages discussed comparison between performance terms Mean Absolute Error (MAE), Root Squared (RMSE), Percentage (MAPE) values compared literature different support researchers select best model for prediction. This validates fact that will provide a more optimal solution.

Language: Английский

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Deep Learning in Smart Grid Technology: A Review of Recent Advancements and Future Prospects

IEEE Access, Journal Year: 2021, Volume and Issue: 9, P. 54558 - 54578

Published: Jan. 1, 2021

The current electric power system witnesses a significant transition into Smart Grids (SG) as promising landscape for high grid reliability and efficient energy management. This ongoing undergoes rapid changes, requiring plethora of advanced methodologies to process the big data generated by various units. In this context, SG stands tied very closely Deep Learning (DL) an emerging technology creating more decentralized intelligent paradigm while integrating intelligence in supervisory operational decision-making. Motivated outstanding success DL-based prediction methods, article attempts provide thorough review from broad perspective on state-of-the-art advances DL systems. Firstly, bibliometric analysis has been conducted categorize review's methodology. Further, we taxonomically delve mechanism behind some trending algorithms. We then showcase enabling technologies SG, such federated learning, edge intelligence, distributed computing. Finally, challenges research frontiers are provided serve guidelines future work futuristic domain. study's core objective is foster synergy between these two fields decision-makers researchers accelerate DL's practical deployment

Language: Английский

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Distributed load forecasting using smart meter data: Federated learning with Recurrent Neural Networks

International Journal of Electrical Power & Energy Systems, Journal Year: 2021, Volume and Issue: 137, P. 107669 - 107669

Published: Nov. 9, 2021

Language: Английский

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Electrical Load Forecasting Using LSTM, GRU, and RNN Algorithms

Energies, Journal Year: 2023, Volume and Issue: 16(5), P. 2283 - 2283

Published: Feb. 27, 2023

Forecasting the electrical load is essential in power system design and growth. It critical from both a technical financial standpoint as it improves performance, reliability, safety, stability well lowers operating costs. The main aim of this paper to make forecasting models accurately estimate based on measurements current loads electricity company. importance having predicting future loads, which will lead reducing costs resources, better electric distribution for companies. In paper, deep learning algorithms are used forecast loads; namely: (1) Long Short-Term Memory (LSTM), (2) Gated Recurrent Units (GRU), (3) Neural Networks (RNN). were tested, GRU model achieved best performance terms accuracy lowest error. Results show that an R-squared 90.228%, Mean Square Error (MSE) 0.00215, Absolute (MAE) 0.03266.

Language: Английский

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Forecasting Renewable Energy Generation with Machine Learning and Deep Learning: Current Advances and Future Prospects

Sustainability, Journal Year: 2023, Volume and Issue: 15(9), P. 7087 - 7087

Published: April 23, 2023

This article presents a review of current advances and prospects in the field forecasting renewable energy generation using machine learning (ML) deep (DL) techniques. With increasing penetration sources (RES) into electricity grid, accurate their becomes crucial for efficient grid operation management. Traditional methods have limitations, thus ML DL algorithms gained popularity due to ability learn complex relationships from data provide predictions. paper reviews different approaches models that been used discusses strengths limitations. It also highlights challenges future research directions field, such as dealing with uncertainty variability generation, availability, model interpretability. Finally, this emphasizes importance developing robust enable integration RES facilitate transition towards sustainable future.

Language: Английский

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Random vector functional link neural network based ensemble deep learning for short-term load forecasting

Expert Systems with Applications, Journal Year: 2022, Volume and Issue: 206, P. 117784 - 117784

Published: June 11, 2022

Language: Английский

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Load Forecasting Models in Smart Grid Using Smart Meter Information: A Review

Energies, Journal Year: 2023, Volume and Issue: 16(3), P. 1404 - 1404

Published: Jan. 31, 2023

The smart grid concept is introduced to accelerate the operational efficiency and enhance reliability sustainability of power supply by operating in self-control mode find resolve problems developed time. In grid, use digital technology facilitates with an enhanced data transportation facility using sensors known as meters. Using these meters, various functionalities can be enhanced, such generation scheduling, real-time pricing, load management, quality enhancement, security analysis enhancement system, fault prediction, frequency voltage monitoring, forecasting, etc. From bulk generated a architecture, precise predicted before time support energy market. This supports operation maintain balance between demand generation, thus preventing system imbalance outages. study presents detailed review on forecasting category, calculation performance indicators, analyzing process for conventional meter information, used conduct task its challenges. Next, importance meter-based discussed along available approaches. Additionally, merits conducted over are articulated this paper.

Language: Английский

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