Digital technologies for a net-zero energy future: A comprehensive review

Renewable and Sustainable Energy Reviews, Journal Year: 2024, Volume and Issue: 202, P. 114681 - 114681

Published: July 2, 2024

Language: Английский

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Forecasting Solar Photovoltaic Power Production: A Comprehensive Review and Innovative Data-Driven Modeling Framework

Energies, Journal Year: 2024, Volume and Issue: 17(16), P. 4145 - 4145

Published: Aug. 20, 2024

The intermittent and stochastic nature of Renewable Energy Sources (RESs) necessitates accurate power production prediction for effective scheduling grid management. This paper presents a comprehensive review conducted with reference to pioneering, comprehensive, data-driven framework proposed solar Photovoltaic (PV) generation prediction. systematic integrating comprises three main phases carried out by seven modules addressing numerous practical difficulties the task: phase I handles aspects related data acquisition (module 1) manipulation 2) in preparation development scheme; II tackles associated model 3) assessment its accuracy 4), including quantification uncertainty 5); III evolves towards enhancing incorporating context change detection 6) incremental learning when new become available 7). adeptly addresses all facets PV prediction, bridging existing gaps offering solution inherent challenges. By seamlessly these elements, our approach stands as robust versatile tool precision real-world applications.

Language: Английский

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Techno-economic feasibility analysis of a commercial grid-connected photovoltaic plant with battery energy storage-achieving a net zero energy system

Journal of Energy Storage, Journal Year: 2023, Volume and Issue: 77, P. 109984 - 109984

Published: Dec. 14, 2023

Language: Английский

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Evaluation and integration of photovoltaic (PV) systems in Albanian energy landscape

Solar Compass, Journal Year: 2024, Volume and Issue: 10, P. 100070 - 100070

Published: Feb. 21, 2024

As a country situated in region with abundant solar resources, Albania has enormous potential for using energy through photovoltaic (PV) systems. With the crisis repeating itself over years, now more than ever is moment to assess and fully use this opportunity. This paper studies current state of PV usage Albania's sector opportunities challenges coming together technology. Economic, social, environmental benefits are discussed, as well existing policies renewable energy. It evaluates technology's role country's sustainable transition analyzes various integration models like net-metering feed-in tariffs. Successful projects case highlighted, while such regulatory complexities public awareness discussed. The study also assesses large-scale feasibility emphasizes need integrated planning. research aims offer relevant information Albanian policymakers, stakeholders, investors support effective implementation systems cleaner, future. Furthermore, there lack about renewables reality. Enhancing sustainability reducing greenhouse gas emissions important.

Language: Английский

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Forecasting building energy demand and on-site power generation for residential buildings using long and short-term memory method with transfer learning

Applied Energy, Journal Year: 2024, Volume and Issue: 368, P. 123500 - 123500

Published: May 23, 2024

Language: Английский

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Experimental investigation of a novel smart energy management system for performance enhancement of conventional solar photovoltaic microgrids

Discover Energy, Journal Year: 2023, Volume and Issue: 3(1)

Published: Nov. 14, 2023

Abstract Solar photovoltaic microgrids are reliable and efficient systems without the need for energy storage. However, during power outages, generated solar cannot be used by consumers, which is one of major limitations conventional microgrids. This results in disruption, developing hotspots PV modules, significant loss power, thus affecting efficiency system. These issues can resolved implementing a smart management system such In this study, proposed microgrids, consists two stages. First production forecasting done using an artificial neural network technique then load demand controller uses Grey Wolf optimiser to optimize consumption. To demonstrate system, experimental microgrid setup established simulate evaluate its performance under real outdoor conditions. The show promising reducing losses 100% clear sunny conditions 42.6% cloudy study relevance further develop Industry achieve targets sustainable development goals.

Language: Английский

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InfoCAVB-MemoryFormer: Forecasting of wind and photovoltaic power through the interaction of data reconstruction and data augmentation

Applied Energy, Journal Year: 2024, Volume and Issue: 371, P. 123745 - 123745

Published: June 20, 2024

Language: Английский

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Interval prediction of short‐term photovoltaic power based on an improved GRU model

Energy Science & Engineering, Journal Year: 2024, Volume and Issue: 12(7), P. 3142 - 3156

Published: July 1, 2024

Abstract The accurate prediction of photovoltaic (PV) power is crucial for planning, constructing, and scheduling high‐penetration distributed PV systems. Traditional point methods suffer from instability lack reliability, which can be effectively addressed through interval prediction. This study proposes a short‐term method based on the framework sparrow search algorithm (SSA)‐variational mode decomposition (VMD)‐convolutional neural network (CNN)‐gate recurrent unit (GRU). First, data undergo similar day clustering permutation entropy VMD applied to solar radiation signals with high correlation. Then, hyperparameters GRU are optimized by SSA according comprehensive evaluation indicator proposed in this study. Subsequently, quantile results obtained CNN‐GRU using optimal parameters optimization. Finally, composed multiple results. A MATLAB R2022b program developed compare different methods. demonstrate that compared single methods, improves coverage width‐based criterion. In sunny rainy days, indicators only 54.3% 37.4% GRU, respectively, indicating significantly improved accuracy.

Language: Английский

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A Deep Learning Quantile Regression Photovoltaic Power-Forecasting Method under a Priori Knowledge Injection

Energies, Journal Year: 2024, Volume and Issue: 17(16), P. 4026 - 4026

Published: Aug. 14, 2024

Accurate and reliable PV power probabilistic-forecasting results can help grid operators market participants better understand cope with energy volatility uncertainty improve the efficiency of dispatch operation, which plays an important role in application scenarios such as trading, risk management, scheduling. In this paper, innovative deep learning quantile regression ultra-short-term power-forecasting method is proposed. This employs a two-branch architecture to forecast conditional power; one branch QR-based stacked conventional convolutional neural network (QR_CNN), other temporal (QR_TCN). The CNN used focus on short-term local dependencies sequences, TCN learn long-term constraints between multi-feature data. These two branches extract different features from input data prior knowledge. By jointly training branches, model able probability distribution obtain discrete forecasts ultra-short term. Then, based these forecasts, kernel density estimation estimate function. proposed innovatively ways priori knowledge injection: constructing differential sequence historical feature provide more information about ultrashort-term dynamics and, at same time, dividing it, together all features, into sets inputs that contain according demand forecasting task; dual-branching designed deeply match corresponding branching computational mechanisms. injection methods effective for performance understandability model. point forecasting, interval probabilistic comprehensively evaluated through case real plant. experimental show performs well task outperforms state-of-the-art models field combined QR. paper technical support scheduling, management time scale system.

Language: Английский

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Daily power generation forecasting for a grid-connected solar power plant using transfer learning technique

Applied Intelligence, Journal Year: 2025, Volume and Issue: 55(6)

Published: Jan. 28, 2025

Language: Английский

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