Photovoltaic module temperature prediction using various machine learning algorithms: Performance evaluation

Applied Energy, Journal Year: 2024, Volume and Issue: 363, P. 123064 - 123064

Published: March 23, 2024

Language: Английский

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Identification of a different design of a photovoltaic thermal collector based on fuzzy logic control and the ARMAX model

Thermal Science and Engineering Progress, Journal Year: 2024, Volume and Issue: 48, P. 102395 - 102395

Published: Jan. 11, 2024

Language: Английский

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Dust deposition characteristics on photovoltaic arrays investigated through wind tunnel experiments

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

Published: Jan. 10, 2025

Optimizing the installation parameters of photovoltaic panels in a array to reduce dust accumulation, thereby enhancing their power generation, is crucial research topic construction solar stations desert regions. Utilizing series wind tunnel experiments on comprising four equally sized panels, this study assessed how variations tilt angle, mounting height, spacing, and incoming flow direction influence both accumulation mass particle size distribution array. The results indicate that first panel exponential growth with increasing angles, while subsequent displayed trend initial increase followed by decrease, maximum ratio achieved at specific configurations, difference each can even be several times. Notably, when spacing between exceeds twice mutual deposition becomes negligible, providing quantifiable threshold for optimal spacing. Additionally, significant differences exist characteristics array, influenced flow. This not only enhances understanding energy systems but also offers practical recommendations optimizing strategies, improving economic viability stations, particularly

Language: Английский

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Techno-economic evaluation of a hybrid photovoltaic system with hot/cold water storage for poly-generation in a residential building

Applied Energy, Journal Year: 2022, Volume and Issue: 331, P. 120391 - 120391

Published: Nov. 28, 2022

Language: Английский

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Analysis of Geological Hazard Susceptibility of Landslides in Muli County Based on Random Forest Algorithm

Sustainability, Journal Year: 2023, Volume and Issue: 15(5), P. 4328 - 4328

Published: Feb. 28, 2023

Landslides seriously threaten human life and property. The rapid accurate prediction of landslide geological hazard susceptibility is the key to disaster prevention mitigation. Traditional evaluation methods have disadvantages in terms factor classification subjective weight determination. Based on this, this paper uses a random forest model built using Python language predict Muli County western Sichuan outputs accuracy. results show that (1) three most important factors are elevation, distance from road, average annual rainfall, sum their weights 67.54%; (2) model’s performance good, with ACC = 99.43%, precision 99.3%, recall 99.48%, F1 99.39%; (3) development zoning basically same. Therefore, can effectively accurately evaluate regional susceptibility. However, there some limitations: information statistical table incomplete; demanding requirements training concentration relating definition non-landslide point sets, range should be delineated according field surveys.

Language: Английский

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Building integrated photovoltaic/thermal technologies in Middle Eastern and North African countries: Current trends and future perspectives

Renewable and Sustainable Energy Reviews, Journal Year: 2023, Volume and Issue: 182, P. 113370 - 113370

Published: May 24, 2023

Language: Английский

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On the enhancement of heat transport and entropy generation of the thin film flow of partially ionized non-Newtonian hybrid nanofluid

Journal of the Taiwan Institute of Chemical Engineers, Journal Year: 2024, Volume and Issue: 157, P. 105412 - 105412

Published: March 4, 2024

Language: Английский

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Recent advances in data mining and machine learning for enhanced building energy management

Energy, Journal Year: 2024, Volume and Issue: 307, P. 132636 - 132636

Published: July 29, 2024

Due to the recent advancements in Internet of Things and data science techniques, a wide range studies have investigated use mining (DM) machine learning (ML) algorithms enhance building energy management (BEM). However, different classes DM ML feature mechanisms capabilities, resulting their distinct roles performance BEM. Appropriate integration categories BEM is essential promote application provide guidance for new topic areas. This study presents literature review techniques key areas BEM, including evaluation, usage prediction, demand flexibility optimization. The categorizes into three main categories, supervised DM, unsupervised reinforcement (RL). Unsupervised are primarily used assessment, while mainly employed benchmarking prediction. RL has been utilized optimal control improve efficiency, flexibility, indoor thermal comfort. strengths, shortcomings, these methods terms applications discussed, along with some suggestions future research this field.

Language: Английский

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Recent Advances in Machine Learning for Building Envelopes: From Prediction to Optimization

Published: Jan. 1, 2025

Nowadays, advanced building envelopes not only need to meet traditional design requirements but also address emerging demands, such as achieving low-carbon transition of buildings and mitigating the urban heat island (UHI) effect. Given intricacy indoor conditions complexity variables, approaches can hardly keep pace with evolving demands. Therefore, integrating Artificial Intelligence (AI) into envelope is trending in recent years. This paper provides a holistic review research on machine learning (ML) design. Popular ML algorithms, data input requirements, output generation are first elucidated, aiming shed light selection appropriate algorithms for specific datasets achieve optimal outcomes. ML-involved studies related types (e.g., building-integrated photovoltaic (BIPV), green roofs, PCM-integrated walls, glazing systems, etc.) discussed. The further highlights capabilities AI technologies predicting parameters material properties, environmental impact) optimizing criteria minimizing energy consumption), from micro-scope (i.e., microenvironment) macro-scope impact heat). work anticipated yield valuable insights promoting AI-driven solutions tackle both conventional challenges sustainable development.

Language: Английский

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AI- aided surrogate model for prediction of HVAC optimization strategies in future conditions in the face of climate change

Energy Reports, Journal Year: 2025, Volume and Issue: 13, P. 1834 - 1845

Published: Jan. 25, 2025

Language: Английский

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