Hybrid deep learning CNN-LSTM model for forecasting direct normal irradiance: a study on solar potential in Ghardaia, Algeria DOI Creative Commons

Boumediene Ladjal,

Mohamed Nadour, Mohcene Bechouat

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: May 2, 2025

Language: Английский

ARIMA Models in Solar Radiation Forecasting in Different Geographic Locations DOI Creative Commons
Ewa Chodakowska, Joanicjusz Nazarko, Łukasz Nazarko

et al.

Energies, Journal Year: 2023, Volume and Issue: 16(13), P. 5029 - 5029

Published: June 28, 2023

The increasing demand for clean energy and the global shift towards renewable sources necessitate reliable solar radiation forecasting effective integration of into system. Reliable has become crucial design, planning, operational management systems, especially in context ambitious greenhouse gas emission goals. This paper presents a study on application auto-regressive integrated moving average (ARIMA) models seasonal different climatic conditions. performance prediction capacity ARIMA are evaluated using data from Jordan Poland. essence modeling analysis use both as reference model evaluating other approaches basic generation presented. current state source utilization selected countries adopted transition strategies to more sustainable system investigated. two time series (for monthly hourly data) built locations forecast is developed. research findings demonstrate that suitable can contribute stable long-term countries’ systems. However, it develop location-specific due variability characteristics. provides insights highlights their potential supporting planning operation

Language: Английский

Citations

52

Optimizing renewable energy systems through artificial intelligence: Review and future prospects DOI Creative Commons
Kingsley Ukoba, Kehinde O. Olatunji,

Eyitayo Adeoye

et al.

Energy & Environment, Journal Year: 2024, Volume and Issue: 35(7), P. 3833 - 3879

Published: May 22, 2024

The global transition toward sustainable energy sources has prompted a surge in the integration of renewable systems (RES) into existing power grids. To improve efficiency, reliability, and economic viability these systems, synergistic application artificial intelligence (AI) methods emerged as promising avenue. This study presents comprehensive review current state research at intersection AI, highlighting key methodologies, challenges, achievements. It covers spectrum AI utilizations optimizing different facets RES, including resource assessment, forecasting, system monitoring, control strategies, grid integration. Machine learning algorithms, neural networks, optimization techniques are explored for their role complex data sets, enhancing predictive capabilities, dynamically adapting RES. Furthermore, discusses challenges faced implementation such variability, model interpretability, real-time adaptability. potential benefits overcoming include increased yield, reduced operational costs, improved stability. concludes with an exploration prospects emerging trends field. Anticipated advancements explainable reinforcement learning, edge computing, discussed context impact on Additionally, paper envisions AI-driven solutions smart grids, decentralized development autonomous management systems. investigation provides important insights landscape applications

Language: Английский

Citations

48

Advances in solar forecasting: Computer vision with deep learning DOI Creative Commons
Quentin Paletta, Guillermo Terrén-Serrano, Yuhao Nie

et al.

Advances in Applied Energy, Journal Year: 2023, Volume and Issue: 11, P. 100150 - 100150

Published: Aug. 7, 2023

Renewable energy forecasting is crucial for integrating variable sources into the grid. It allows power systems to address intermittency of supply at different spatiotemporal scales. To anticipate future impact cloud displacements on generated by solar facilities, conventional modeling methods rely numerical weather prediction or physical models, which have difficulties in assimilating information and learning systematic biases. Augmenting computer vision with machine overcomes some these limitations fusing real-time cover observations surface measurements acquired from multiple sources. This Review summarizes recent progress multisensor Earth a focus deep learning, provides necessary theoretical framework develop architectures capable extracting relevant data ground-level sky cameras, satellites, stations, sensor networks. Overall, has potential significantly improve accuracy robustness meteorology; however, more research realize this its limitations.

Language: Английский

Citations

43

From biochar to battery electrodes: A pathway to green lithium and sodium-ion battery systems DOI

Junaid Aslam,

Muhammad Waseem,

Xiaomeng Lü

et al.

Chemical Engineering Journal, Journal Year: 2025, Volume and Issue: unknown, P. 159556 - 159556

Published: Jan. 1, 2025

Language: Английский

Citations

3

A multistep short-term solar radiation forecasting model using fully convolutional neural networks and chaotic aquila optimization combining WRF-Solar model results DOI
Jikai Duan, Hongchao Zuo, Yulong Bai

et al.

Energy, Journal Year: 2023, Volume and Issue: 271, P. 126980 - 126980

Published: Feb. 20, 2023

Language: Английский

Citations

23

State of global solar energy market: Overview, China's role, Challenges, and Opportunities DOI Creative Commons
Assia Chadly, Karim Moawad, Khaled Salah

et al.

Sustainable Horizons, Journal Year: 2024, Volume and Issue: 11, P. 100108 - 100108

Published: April 18, 2024

Solar energy is the most common, cheapest, and mature renewable technology. With solar photovoltaics taking over recently, an in-depth look into their supply chain shows a surprising dependency on Chinese market from raw materials to assembled PVs. This article tackles main challenges in sheds light opportunities that industry. The research results show China controls of primary materials, manufacturing, installed capacity, recycling capacity. alone produces at least 80 % components Also, more than 30 cumulative capacity China, top exporter manufactured PVs World with competitive manufacturing costs reached less $0.24/W. However, value panels some issues might pose potential risks future such as trade barriers material shortage possibility. also presents opportunities.

Language: Английский

Citations

14

Comparative analysis of single and hybrid machine learning models for daily solar radiation DOI Creative Commons
Erdem Küçüktopçu, Bilal Cemek, Halis Şimşek

et al.

Energy Reports, Journal Year: 2024, Volume and Issue: 11, P. 3256 - 3266

Published: March 11, 2024

This study investigates the estimation of daily solar radiation (SR) through various machine learning (ML) models, including k-nearest neighbor algorithm (KNN), support vector regression (SVR), and random forest (RF), both individually in combination with wavelet transform (WT). The assessment these models is based on meteorological data spanning three decades (1981–2010) from province Kütahya Türkiye. To address inherent uncertainty data-driven quantile method employed for analysis. Statistical metrics, such as mean absolute error (MAE), root square (RMSE), coefficient determination (R2), prediction interval (MPI), coverage probability (PICP), are utilized to evaluate effectiveness uncertainties models. SVR KNN exhibit comparable performances concerning predictive accuracy levels. However, hybrid KNN-WT, RF-WT, SVR-WT, display enhanced compared individual ML indicated by statistical performance criteria. Notably, SVR-WT model, incorporating inputs sunshine duration, air temperature, wind speed, relative humidity, outperforms other terms RMSE (2.174 MJ/m2), MAE (1.721 R2 (0.923), MPI (28.55), PICP (0.80) testing dataset. In conclusion, integration WT significantly improves providing valuable insights design operation energy systems, where precise SR critical optimal cost-efficient operation.

Language: Английский

Citations

10

Solar Radiation Forecasting: A Systematic Meta-Review of Current Methods and Emerging Trends DOI Creative Commons
Ewa Chodakowska, Joanicjusz Nazarko, Łukasz Nazarko

et al.

Energies, Journal Year: 2024, Volume and Issue: 17(13), P. 3156 - 3156

Published: June 26, 2024

Effective solar forecasting has become a critical topic in the scholarly literature recent years due to rapid growth of photovoltaic energy production worldwide and inherent variability this source energy. The need optimise systems, ensure power continuity, balance supply demand is driving continuous development methods approaches based on meteorological data or plant characteristics. This article presents results meta-review literature, including current state knowledge methodological discussion. It comprehensive set methods, evaluates classifications, proposes new synthetic typology. emphasises increasing role artificial intelligence (AI) machine learning (ML) techniques improving forecast accuracy, alongside traditional statistical physical models. explores challenges hybrid ensemble models, which combine multiple enhance performance. paper addresses emerging trends research, such as integration big advanced computational tools. Additionally, from perspective, outlines rigorous approach research procedure, scientific associated with conducting bibliometric highlights best practices principles. article’s relevance consists providing up-to-date forecasting, along insights trends, future directions, anticipating implications for theory practice.

Language: Английский

Citations

10

Spatial-temporal characteristics analysis of solar irradiance forecast errors in Europe and North America DOI
Mingliang Bai,

Peng Yao,

Haiyu Dong

et al.

Energy, Journal Year: 2024, Volume and Issue: 297, P. 131187 - 131187

Published: April 9, 2024

Language: Английский

Citations

9

Performance analysis of different flow rates and dust accumulation in rectangular micro heat pipe Photovoltaic/Thermal parallel system DOI
Rui Li,

Zijiao Jia,

Xiaohua Sun

et al.

Applied Thermal Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 125550 - 125550

Published: Jan. 1, 2025

Language: Английский

Citations

1