Random Forest model to predict solar water heating system performance

Lillo-Bravo, I.; Vera-Medina, J.; Fernández-Peruchena, Carlos M.; Pérez-Aparicio, Elena; López-Álvarez, José A.; Delgado-Sanchez, J.M.

doi:10.1016/j.renene.2023.119086

Cited by 16 publications

(3 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the feature selection process, this study employed both the Random Forest method and the Kendall Coefficient method, as each method offers insights into feature importance. The Random Forest approach gives insight into how features contribute to the performance of a specific model, and the Kendall rank correlation coefficient provides a quantify strength of the monotonic relationship between variables ( Lillo-Bravo et al., 2023 ). Figure 7 is an exploration on the significance of lychee traits, which consists of two y-axis referring to the two methods.…”

Section: Resultsmentioning

confidence: 99%

Advanced deep learning models for phenotypic trait extraction and cultivar classification in lychee using photon-counting micro-CT imaging

Xue,

Huang,

et al. 2024

Front. Plant Sci.

View full text Add to dashboard Cite

IntroductionIn contemporary agronomic research, the focus has increasingly shifted towards non-destructive imaging and precise phenotypic characterization. A photon-counting micro-CT system has been developed, which is capable of imaging lychee fruit at the micrometer level and capturing a full energy spectrum, thanks to its advanced photon-counting detectors.MethodsFor automatic measurement of phenotypic traits, seven CNN-based deep learning models including AttentionUNet, DeeplabV3+, SegNet, TransUNet, UNet, UNet++, and UNet3+ were developed. Machine learning techniques tailored for small-sample training were employed to identify key characteristics of various lychee species.ResultsThese models demonstrate outstanding performance with Dice, Recall, and Precision indices predominantly ranging between 0.90 and 0.99. The Mean Intersection over Union (MIoU) consistently falls between 0.88 and 0.98. This approach served both as a feature selection process and a means of classification, significantly enhancing the study's ability to discern and categorize distinct lychee varieties.DiscussionThis research not only contributes to the advancement of non-destructive plant analysis but also opens new avenues for exploring the intricate phenotypic variations within plant species.

show abstract

Section: Resultsmentioning

confidence: 99%

Advanced deep learning models for phenotypic trait extraction and cultivar classification in lychee using photon-counting micro-CT imaging

Xue,

Huang,

et al. 2024

Front. Plant Sci.

View full text Add to dashboard Cite

show abstract

“…Meanwhile, Ref. [14] presents a control mechanism for solar heating systems aimed at preventing excessive water temperature rise while ensuring a sufficient supply of hot water for individual households.…”

Section: Introductionmentioning

confidence: 99%

Control and Managing of Individual Solar Water Heating Systems in an Apartment Complex

Krinitsky,

Averbukh

2024

Electronics

View full text Add to dashboard Cite

Managing solar energy utilization and water heating in multi-apartment buildings presents formidable challenges due to limited space for solar collector installation. Optimizing heat energy distribution among communal consumers is crucial, necessitating precise regulation of hot water flow from the main system line to individual thermal storage tanks. The objective is to minimize heat and electricity losses while maximizing temperature levels in each tank. An electronic control system, centered around a microcontroller, has been developed with an efficient algorithm, data storage memory, and communication capabilities. The implemented solar heating system is tailored to each individual consumer, incorporating precise measurements for every apartment. This customization enhances efficiency not only for the individual consumer but also benefits the overall solar system within the building. This article explores the development and past outcomes of implementing such a control system, highlighting its significant advantages, particularly in multi-story buildings.

show abstract

“…The parameters selected in this study include n_estimators, max_depth, min_sample_leaf, max_features, criterion, and min_samples_split. In order to obtain the optimal random forest model, the mesh search parameter optimization algorithm was adopted in this study [44], and the grid search interval range of each parameter was set as shown in Table 3. In the process of building a random forest model, two important parameters, max_features and n_estimators, will directly determine the accuracy of the model.…”

mentioning

confidence: 99%

Spatial Characteristics and Influencing Factors of Multi-Scale Urban Living Space (ULS) Carbon Emissions in Tianjin, China

Zhang,

Liao,

Yin

et al. 2023

Buildings

View full text Add to dashboard Cite

Urban living space (ULS) is known to be a significant contributor to carbon emissions. However, there is a lack of studies that have considered the impact of spatial organization indexes (SOIs) of various scales on urban living space carbon emissions (ULSCE), and so far, no definitive conclusions have been reached. To address this gap, taking Tianjin as an example, the measurement methods of ULSCE and SOI at different scales were proposed, and a random forest model was constructed to explore the effects of SOI on ULSCE. The results indicated that on the district scale, Beichen had the highest carbon emissions and absorption in 2021, with carbon emissions reaching 1.43 × 108 t and carbon absorption at 7.29 × 105 kg. In terms of area scale, the comprehensive service area had the highest carbon emissions at 3.57 × 108 t, accounting for 47.70%, while the green leisure area had the highest carbon absorption at 5.76 × 105 kg, accounting for 32.33%. At the block scale, the industrial block had the highest carbon emissions at 1.82 × 108 t, accounting for 54.02%, while the forest block had the highest carbon absorption at 1.25 × 106 kg, accounting for 91.33%. Each SOI had varying impacts, with the industrial land ratio (ILR) having the highest order of importance at the area scale, followed by road network density (RND), residential land ratio (RLR), bus station density (BSD), public service facilities land ratio (PLR), land mixing degree (LMD), open space ratio (OSR), and commercial land ratio (CLR). ILR, RND, and RLR were particularly important, each exceeding 10%, with importance values of 50.66%, 17.79%, and 13.17%, respectively. At the block scale, building area (BA) had the highest importance, followed by building density (BD), building height (BH), land area (LA), and floor area ratio (FAR). BA and BD were particularly important, with values of 27.31% and 21.73%, respectively. This study could serve as both theoretical and practical guidance for urban planning to aid the government in developing differentiated carbon emissions reduction strategies that can mitigate the heat island effect and promote low-carbon healthy urban planning.

show abstract

Random Forest model to predict solar water heating system performance

Cited by 16 publications

References 29 publications

Advanced deep learning models for phenotypic trait extraction and cultivar classification in lychee using photon-counting micro-CT imaging

Advanced deep learning models for phenotypic trait extraction and cultivar classification in lychee using photon-counting micro-CT imaging

Control and Managing of Individual Solar Water Heating Systems in an Apartment Complex

Spatial Characteristics and Influencing Factors of Multi-Scale Urban Living Space (ULS) Carbon Emissions in Tianjin, China

Contact Info

Product

Resources

About