Load Forecasting Techniques and Their Applications in Smart Grids

Energies, Journal Year: 2023, Volume and Issue: 16(3), P. 1480 - 1480

Published: Feb. 2, 2023

The growing success of smart grids (SGs) is driving increased interest in load forecasting (LF) as accurate predictions energy demand are crucial for ensuring the reliability, stability, and efficiency SGs. LF techniques aid SGs making decisions related to power operation planning upgrades, can help provide efficient reliable services at fair prices. Advances artificial intelligence (AI), specifically machine learning (ML) deep (DL), have also played a significant role improving precision forecasting. It important evaluate different identify most appropriate one use This paper conducts systematic review state-of-the-art techniques, including traditional clustering-based AI-based time series-based provides an analysis their performance results. aim this determine which technique suitable specific applications findings indicate that using ML neural network (NN) models, shown best forecast compared other methods, achieving higher overall root mean squared (RMS) absolute percentage error (MAPE) values.

Language: Английский

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Smart home energy management systems: Research challenges and survey

Alexandria Engineering Journal, Journal Year: 2024, Volume and Issue: 92, P. 117 - 170

Published: March 5, 2024

Electricity is establishing ground as a means of energy, and its proportion will continue to rise in the next generations. Home energy usage expected increase by more than 40% 20 years. Therefore, compensate for demand requirements, proper planning strategies are needed improve home management systems (HEMs). One crucial aspects HEMS load forecasting scheduling utilization. Energy depend heavily on precise scheduling. Considering this scenario, article was divided into two parts. Firstly, gives thorough analysis models HEMs with primary goal determining whichever model most appropriate given situation. Moreover, optimal utilization HEMs, current literature has discussed number optimization approaches. secondly article, these approaches be examined thoroughly develop effective operating make wise judgments regarding techniques HEMs. Finally, paper also presents future technical advancements research gaps how they affect activities near future.

Language: Английский

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Optimal load forecasting and scheduling strategies for smart homes peer-to-peer energy networks: A comprehensive survey with critical simulation analysis

Results in Engineering, Journal Year: 2024, Volume and Issue: 22, P. 102188 - 102188

Published: May 3, 2024

The home energy management (HEM) sector is going through an enormous change that includes important elements like incorporating green power, enhancing efficiency forecasting and scheduling optimization techniques, employing smart grid infrastructure, regulating the dynamics of optimal trading. As a result, ecosystem players need to clarify their roles, develop effective regulatory structures, experiment with new business models. Peer-to-Peer (P2P) trading seems be one viable options in these conditions, where consumers can sell/buy electricity to/from other users prior totally depending on utility. P2P enables exchange between prosumers, thus provide more robust platform for This strategy decentralizes market than it did previously, opening up possibilities improving trade customers Considering above scenarios, this research provides extensive insight structure, procedure, design, platform, pricing mechanism, approaches, topologies possible futuristic while examining characteristics, pros cons primary goal determining whichever approach most appropriate given situation HEMs. Moreover, HEMs load framework simulation model also proposed analyze network critically, paving technical directions scientific researchers. With cooperation, age technological advancements ushering intelligent, interconnected, reactive urban environment are brought life. In sense, path living entails reinventing as well how people interact perceive dwellings larger city. Finally, work comprehensive overview challenges terms strategies, solutions, future prospects.

Language: Английский

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A Review on Socio-technical Transition Pathway to European Super Smart Grid: Trends, Challenges and Way Forward via Enabling Technologies

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 104155 - 104155

Published: Jan. 1, 2025

Language: Английский

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Short-Term Power Load Forecasting Based on PSO-Optimized VMD-TCN-Attention Mechanism

Energies, Journal Year: 2023, Volume and Issue: 16(12), P. 4616 - 4616

Published: June 9, 2023

A new prediction framework is proposed to improve short-term power load forecasting accuracy. The based on particle swarm optimization (PSO)-variational mode decomposition (VMD) combined with a time convolution network (TCN) embedded attention mechanism (Attention). follows two-step process. In the first step, PSO applied optimize VMD method. original electricity sequence decomposed, and fitness function uses sample entropy describe complexity of series. decomposed sub-sequences are relevant features, such as meteorological data, form input model. second TCN selected model, it an above fed model obtain PSO-VMD-TCN-Attention framework. Load datasets various models validate PSO-optimized method TCN-Attention Simulation results demonstrate that enhances model’s accuracy, outperforms other in terms accuracy ability.

Language: Английский

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From Smart Grids to Super Smart Grids: A Roadmap for Strategic Demand Management for Next Generation SAARC and European Power Infrastructure

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 12303 - 12341

Published: Jan. 1, 2023

Due to an increasing of demands electricity in a world on regular basis, different continents will initiate step towards transforming their smart grids infrastructure into super (SSGs), which various countries continent take interconnection power system networks with one another manage futuristic conditions. The concept SSGs is predicated due extensive use modern technology, digital communication, machine learning and information techniques for the present generating be more accurate feature balancing demand supply. uses renewable energy resources support multiple by reducing greenhouse gases emissions. main purpose balance supply between countries, if each country not able own profiles. environmental conditions, lack management, intermittent nature line losses are major hurdles provide This research work focused about form technical challenges that arises case developing European SAARC continents, thus valuable solution it along discussion future directions. Moreover, although ideas have received positive reviews from many experts, but there development still challenging issue simulation based models current literature. To deal this issue, finally fuzzy logic using hybrid cluster model consisting two clusters wind successfully presented paper. can utilized prospective any network continents. simulations performed MATLAB. suggested eighteen bus provides interconnecting clusters, whenever or both lies region faced some kind fault.

Language: Английский

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SAARC super smart grid: Navigating the future - unleashing the power of an energy-efficient integration of renewable energy resources in the saarc region

Computers & Electrical Engineering, Journal Year: 2024, Volume and Issue: 118, P. 109405 - 109405

Published: July 9, 2024

Language: Английский

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Probabilistic optimal power flow in power systems with Renewable energy integration using Enhanced walrus optimization algorithm

Ain Shams Engineering Journal, Journal Year: 2024, Volume and Issue: unknown, P. 102663 - 102663

Published: Feb. 1, 2024

This paper presents a novel approach to solve the Probabilistic Optimal Power Flow (POPF) problem using Enhanced Walrus Optimization (EWO) Algorithm. The proposed EWO is applied 30 and 118-bus IEEE systems, demonstrating its effectiveness in handling complexities of grid with renewable energy sources (RESs). algorithm effectively addresses uncertainties associated RES generation, ensuring system reliability minimizing generation costs. optimization method performs better than existing algorithms, achieving smooth speedy convergence high solution accuracy. research findings demonstrate that an efficient tool for tackling POPF power systems RESs. Moreover, methodology extensively clarified by sensitivity analyses. work demonstrates potential as viable integration-assisted optimization, providing opportunities more study into cutting-edge techniques.

Language: Английский

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A Comprehensive Survey on Load Forecasting Hybrid Models: Navigating the Futuristic Demand Response Patterns through Experts and Intelligent Systems

Results in Engineering, Journal Year: 2024, Volume and Issue: 23, P. 102773 - 102773

Published: Aug. 24, 2024

Language: Английский

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Optimization of power system load forecasting and scheduling based on artificial neural networks

Energy Informatics, Journal Year: 2025, Volume and Issue: 8(1)

Published: Jan. 8, 2025

Abstract This study seeks to enhance the accuracy and economic efficiency of power system load forecasting (PSLF) by leveraging Artificial Neural Networks. A predictive model based on a Residual Connection Bidirectional Long Short Term Memory Attention mechanism (RBiLSTM-AM) is proposed. In this model, normalized time series data used as input, with network capturing bidirectional dependencies residual connections preventing gradient vanishing. Subsequently, an attention applied capture influence significant steps, thereby improving prediction accuracy. Based forecasting, Particle Swarm Optimization (PSO) algorithm employed quickly determine optimal scheduling strategy, ensuring safety system. Results show that proposed RBiLSTM-AM achieves 96.68%, precision 91.56%, recall 90.51%, F1-score 91.37%, significantly outperforming other models (e.g., Recurrent Network which has 69.94%). terms error metrics, reduces root mean square 123.70 kW, absolute 104.44 percentage (MAPE) 5.62%, all are lower than those models. Economic cost analysis further demonstrates PSO strategy costs at most points compared Genetic Algorithm (GA) Simulated Annealing (SA) strategies, being 689.17 USD in first hour 2214.03 fourth hour, both GA SA. Therefore, demonstrate benefits PSLF, providing effective technical support for optimizing scheduling.

Language: Английский

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