Cited by A satellite-based novel method to forecast short-term (10 min − 4 h) solar radiation by combining satellite-based cloud transmittance forecast and physical clear-sky radiation model

Improved satellite-based intra-day solar forecasting with a chain of deep learning models DOI

Shanlin Chen, Chengxi Li, Roland B. Stull

et al.

Energy Conversion and Management, Journal Year: 2024, Volume and Issue: 313, P. 118598 - 118598

Published: May 30, 2024

Language: Английский

Citations

AI-Driven precision in solar forecasting: Breakthroughs in machine learning and deep learning DOI

Ayesha Nadeem, Muhammad Farhan Hanif,

Muhammad Sabir Naveed

et al.

AIMS Geosciences, Journal Year: 2024, Volume and Issue: 10(4), P. 684 - 734

Published: Jan. 1, 2024

<p>The need for accurate solar energy forecasting is paramount as the global push towards renewable intensifies. We aimed to provide a comprehensive analysis of latest advancements in forecasting, focusing on Machine Learning (ML) and Deep (DL) techniques. The novelty this review lies its detailed examination ML DL models, highlighting their ability handle complex nonlinear patterns Solar Irradiance (SI) data. systematically explored evolution from traditional empirical, including machine learning (ML), physical approaches these advanced delved into real-world applications, discussing economic policy implications. Additionally, we covered variety image-based, statistical, ML, DL, foundation, hybrid models. Our revealed that models significantly enhance accuracy, operational efficiency, grid reliability, contributing benefits supporting sustainable policies. By addressing challenges related data quality model interpretability, underscores importance continuous innovation techniques fully realize potential. findings suggest integrating with offers most promising path forward improving forecasting.</p>

Language: Английский

Citations

Improving cross-site generalisability of vision-based solar forecasting models with physics-informed transfer learning DOI

Quentin Paletta, Yuhao Nie, Yves‐Marie Saint‐Drenan

et al.

Energy Conversion and Management, Journal Year: 2024, Volume and Issue: 309, P. 118398 - 118398

Published: April 17, 2024

Forecasting solar energy from cloud cover observations is crucial to truly anticipate future changes in power supply. On an intra-hour timescale, ground-level sky cameras located near a site offer the most valuable source of information on incoming clouds. In literature, analysis these hyperlocal for modelling increasingly performed by deep learning algorithms trained and tested years' worth local data. However, this approach not suitable industrial applications since producers cannot wait years data collection start generating reliable forecasts. they might own relevant multi-location collected other sites over time. This study thus explores capability such generalise beyond their training location two scarce conditions: zero-shot (i.e. direct application model new without fine-tuning) few-shot calibration pre-trained based very limited as day observations). Zero-shot results show that using clear-sky models normalise output variables (e.g. irradiance or production values) facilitates cross-dataset transfer learning. Compared previous methods, resulting forecast skill increases close 25% cloudy conditions more than 700% conditions. An additional gain observed when overcast weather are used via The corresponding neural networks achieve comparable performance expert These promising shed light potential large-scale image datasets improve generalisation skills forecasting algorithms.

Language: Английский

Citations

A Review of Recent Hardware and Software Advances in GPU-Accelerated Edge-Computing Single-Board Computers (SBCs) for Computer Vision DOI

Umair Iqbal, Tim Davies, Pascal Perez

et al.

Sensors, Journal Year: 2024, Volume and Issue: 24(15), P. 4830 - 4830

Published: July 25, 2024

Computer Vision (CV) has become increasingly important for Single-Board Computers (SBCs) due to their widespread deployment in addressing real-world problems. Specifically, the context of smart cities, there is an emerging trend developing end-to-end video analytics solutions designed address urban challenges such as traffic management, disaster response, and waste management. However, deploying CV on SBCs presents several pressing (e.g., limited computation power, inefficient energy real-time processing needs) hindering use at scale. Graphical Processing Units (GPUs) software-level developments have emerged recently these enable elevated performance SBCs; however, it still active area research. There a gap literature comprehensive review recent rapidly evolving advancements both software hardware fronts. The presented provides detailed overview existing GPU-accelerated edge-computing including algorithm optimization techniques, packages, development frameworks, specific packages. This subjective comparative analysis based critical factors help applied Artificial Intelligence (AI) researchers demonstrating state art selecting best suited combinations use-case. At end, paper also discusses potential limitations highlights future research directions this domain.

Language: Английский

Citations

Extending intraday solar forecast horizons with deep generative models DOI

Alberto Carpentieri, Doris Folini, Jussi Leinonen

et al.

Applied Energy, Journal Year: 2024, Volume and Issue: 377, P. 124186 - 124186

Published: Sept. 5, 2024

Language: Английский

Citations

A graph attention network framework for generalized-horizon multi-plant solar power generation forecasting using heterogeneous data DOI

Md Abul Hasnat, Somayeh Asadi, Negin Alemazkoor

et al.

Renewable Energy, Journal Year: 2025, Volume and Issue: unknown, P. 122520 - 122520

Published: Feb. 1, 2025

Language: Английский

Citations

Synthesizing Images with Aligned Masks Using Text-to-Image Based Generative Ai for Robust Pv Segmentation DOI

Hongjun Tan, Zhiling Guo, Jiaze Li

et al.

Published: Jan. 1, 2025

Language: Английский

Citations

Monthly and Quarter-Hourly Hydro Scheduling for Assessing Retrofit of Conventional to Reversible Turbines with Wind and PV Integration DOI

Pengcheng Wang, Hao Zheng,

Guoyuan Qian

et al.

Energy, Journal Year: 2025, Volume and Issue: unknown, P. 135223 - 135223

Published: Feb. 1, 2025

Language: Английский

Citations

AI Powered Renewable Energy Balancing, Forecasting and Global Trend Analysis using ANN-LSTM Integration DOI

Sanjana Murgod,

Kartik Garg,

Triveni Magadum

et al.

Published: March 3, 2025

Abstract The instability of renewable energy sources like solar and wind places significant hurdles on distribution grid stability, thus hampering the race towards sustainable solutions. These instabilities, mainly due to fluctuating weather conditions, may lead surpluses or shortages energy-with inevitable effects grid's reliability. It is proposed that an AI-enabled system based ANN LSTM solutions be developed analyse global trends, predict generation accurately, enhance resilience. new model resides historical real-time data adequately captures long-range transition short-range fluctuations in energy, allowing better management. Along with that, intelligent forecasting will also optimize storage minimize overreliance normal fossil fuel energy. insights drawn out by this provide considerable assistance decision-makers, suppliers, operators their drive for a more stable, efficient, dependable, infrastructure. This research highlights role AI-driven predictive analytics should play facilitating transitions toward while addressing some critical operational challenges reliability distribution.

Language: Английский

Citations

Application of Artificial Intelligence and Machine Learning in Assessing Solar Energy Potential DOI

Ajay Mittal

Published: March 21, 2025

Language: Английский

Citations