Review of Dynamic Façade Typologies, Physical Performance and Control Methods: Towards Smarter and Cleaner Zero-Energy Buildings DOI
Mengmeng Wang,

Zhuoying Jia,

Lulu Tao

et al.

Journal of Building Engineering, Journal Year: 2024, Volume and Issue: unknown, P. 111310 - 111310

Published: Nov. 1, 2024

Language: Английский

Evaluation of solar energy potential for residential buildings in urban environments based on a parametric approach DOI
Jia Tian, Ryozo Ooka

Sustainable Cities and Society, Journal Year: 2024, Volume and Issue: 106, P. 105350 - 105350

Published: March 16, 2024

Language: Английский

Citations

20
Machine Learning-Based Forecasting of Temperature and Solar Irradiance for Photovoltaic Systems DOI Creative Commons

Wassila Tercha,

Sid Ahmed Tadjer,

Fathia Chekired

et al.

Energies, Journal Year: 2024, Volume and Issue: 17(5), P. 1124 - 1124

Published: Feb. 27, 2024

The integration of photovoltaic (PV) systems into the global energy landscape has been boosted in recent years, driven by environmental concerns and research renewable sources. accurate prediction temperature solar irradiance is essential for optimizing performance grid PV systems. Machine learning (ML) become an effective tool improving accuracy these predictions. This comprehensive review explores pioneer techniques methodologies employed field ML-based forecasting article presents a comparative study between various algorithms commonly used radiation forecasting. These include regression models such as decision trees, random forest, XGBoost, support vector machines (SVM). beginning this highlights importance weather forecasts operation challenges associated with traditional meteorological models. Next, fundamental concepts machine are explored, highlighting benefits improved estimating power generation integration.

Language: Английский

Citations

18
Forecasting Solar Photovoltaic Power Production: A Comprehensive Review and Innovative Data-Driven Modeling Framework DOI Creative Commons
Sameer Al‐Dahidi, Manoharan Madhiarasan, Loiy Al‐Ghussain

et al.

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

Citations

17
Promoting solar energy utilization: Prediction, analysis and evaluation of solar radiation on building surfaces at city scale DOI

Yingjun Yue,

Zengfeng Yan, Pingan Ni

et al.

Energy and Buildings, Journal Year: 2024, Volume and Issue: 319, P. 114561 - 114561

Published: July 16, 2024

Language: Английский

Citations

12
Forecasting Solar Energy: Leveraging Artificial Intelligence and Machine Learning for Sustainable Energy Solutions DOI Open Access

Taraneh Saadati,

Burak Barutçu

Journal of Economic Surveys, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 21, 2025

ABSTRACT Integrating solar energy into power grids is essential for advancing a low‐carbon economy, but accurate forecasting remains challenging due to output variability. This study comprehensively reviews models, focusing on how Artificial Intelligence (AI) and Machine Learning (ML) enhance forecast accuracy. It examines the current landscape of forecasting, identifies limitations in existing underscores need more adaptable approaches. The primary goals are analyze evolution AI/ML‐based assess their strengths weaknesses, propose structured methodology selecting implementing AI/ML models tailored forecasting. Through comparative analysis, evaluates individual hybrid across different scenarios, identifying under‐explored research areas. findings indicate significant improvements prediction accuracy through advancements, aiding grid management supporting transition. Ensemble methods, deep learning techniques, show great promise enhancing reliability. Combining diverse approaches with advanced techniques results reliable forecasts. suggests that improving model these integrated methods offers substantial opportunities further research, contributing global sustainability efforts, particularly UN SDGs 7 13, promoting economic growth minimal environmental impact.

Language: Английский

Citations

1
Parallel TimesNet-BiLSTM model for ultra-short-term photovoltaic power forecasting using STL decomposition and auto-tuning DOI

Jianqiang Gong,

Zhiguo Qu, Zhiyu Zhu

et al.

Energy, Journal Year: 2025, Volume and Issue: unknown, P. 135286 - 135286

Published: Feb. 1, 2025

Language: Английский

Citations

1
Regression analysis and prediction of monthly wind and solar power generation in China DOI Creative Commons
Xueping Du,

Zhikai Lang,

Menglin Liu

et al.

Energy Reports, Journal Year: 2024, Volume and Issue: 12, P. 1385 - 1402

Published: July 24, 2024

Language: Английский

Citations

7
On the use of sky images for intra-hour solar forecasting benchmarking: Comparison of indirect and direct approaches DOI
Guoping Ruan, Xiaoyang Chen, Eng Gee Lim

et al.

Solar Energy, Journal Year: 2024, Volume and Issue: 276, P. 112649 - 112649

Published: June 6, 2024

Language: Английский

Citations

6
Evaluating electrical power yield of photovoltaic solar cells with k-Nearest neighbors: A machine learning statistical analysis approach DOI Creative Commons
Sameera Sadey Shijer,

Ahmed Hikmet Jassim,

Luttfi A. Al-Haddad

et al.

e-Prime - Advances in Electrical Engineering Electronics and Energy, Journal Year: 2024, Volume and Issue: 9, P. 100674 - 100674

Published: July 4, 2024

The increasing demand for sustainable and renewable energy solutions reflects the critical importance of advancing photovoltaic (PV) technology its operational efficiency. In response, this study introduces a novel application k-Nearest Neighbor (k-NN) algorithm to assess reliability applicability solar panel simulation data which aimed classify current states partial shading, open, short circuit conditions, alongside regression-based analysis predicting specific operating parameters. research, published dataset that involved various PV module configurations under different environmental conditions was tested evaluated. k-NN technique applied both status predict performance metrics modules. diagnosis model demonstrated an accuracy 99.2 % F1 score %, indicating high degree in identifying Concurrently, regression exhibited Root Mean Square Error (RMSE) 0.036 R2 value unity showcased effectiveness parameters based on data. concluded results are further enriching simulation-based generation be endorsed implemented before jumping into real experimental applications, addition highlighting potential machine learning cells productivity statistical analysis.

Language: Английский

Citations

6
Large-scale prediction of solar irradiation, shading impacts, and energy generation on building Façade through urban morphological indicators: A machine learning approach DOI Creative Commons
Hongying Zhao, Chengyang Liu, Rebecca Yang

et al.

Energy and Buildings, Journal Year: 2024, Volume and Issue: unknown, P. 114797 - 114797

Published: Sept. 1, 2024

Language: Английский

Citations

6