Daily and seasonal heat usage patterns analysis in heat networks

Dang, L. Minh; Li, Yanfen; Oh, Chanmi; Nguyen, Tan N.; Song, Hyoung‐Kyu; Moon, Hyeonjoon

doi:10.1038/s41598-022-13030-6

Cited by 3 publications

(2 citation statements)

References 37 publications

(40 reference statements)

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“…The heat usage pattern analysis has become increasingly essential as the number of end-users increases, because it greatly impacts the entire network's efficiency. Variations in the heat usage behavior from the consumers' side lead to variations in the heat usage pattern of a single substation, which is a major matter for accurate and efficient DH management and operation 6 . For example, the substantial temperature difference between the summer and the winter significantly influences the users' heat demand.…”

Section: Introductionmentioning

confidence: 99%

“…Despite the numerous issues addressed and methods discussed in existing literature on heat load prediction in DH networks, further research is needed to explore important external factors such as holiday and weather conditions, which could be utilized as input to improve the models' accuracy 6 . Additionally, while previous work has showed the high predictive performance of ML algorithms for heat demand, they have not provided a clear explanation of why the model achieved good performance, as well as which features are important and their correlation with the models 10 .…”

Section: Introductionmentioning

confidence: 99%

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Toward explainable heat load patterns prediction for district heating

Dang

Shin

et al. 2023

Sci Rep

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Heat networks play a vital role in the energy sector by offering thermal energy to residents in certain countries. Effective management and optimization of heat networks require a deep understanding of users' heat usage patterns. Irregular patterns, such as peak usage periods, can exceed the design capacities of the system. However, previous work has mostly neglected the analysis of heat usage profiles or performed on a small scale. To close the gap, this study proposes a data-driven approach to analyze and predict heat load in a district heating network. The study uses data from over eight heating seasons of a cogeneration DH plant in Cheongju, Korea, to build analysis and forecast models using supervised machine learning (ML) algorithms, including support vector regression (SVR), boosting algorithms, and multilayer perceptron (MLP). The models take weather data, holiday information, and historical hourly heat load as input variables. The performance of these algorithms is compared using different training sample sizes of the dataset. The results show that boosting algorithms, particularly XGBoost, are more suitable ML algorithms with lower prediction errors than SVR and MLP. Finally, different explainable artificial intelligence approaches are applied to provide an in-depth interpretation of the trained model and the importance of input variables.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Toward explainable heat load patterns prediction for district heating

Dang

Shin

et al. 2023

Sci Rep

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Vision-Based White Radish Phenotypic Trait Measurement with Smartphone Imagery

et al. 2023

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White radish is a nutritious and delectable vegetable that is enjoyed globally. Conventional techniques for monitoring radish growth are arduous and time-consuming, encouraging the development of novel methods for quicker measurements and greater sampling density. This research introduces a mathematical model working on high-resolution images to measure radish’s biophysical properties automatically. A color calibration was performed on the dataset using a color checker panel to minimize the impact of varying light conditions on the RGB images. Subsequently, a Mask-RCNN model was trained to effectively segment different components of the radishes. The observations of the segmented results included leaf length, leaf width, root width, root length, leaf length to width, root length to width, root shoulder color, and root peel color. The automated real-life measurements of these observations were then conducted and compared with actual results. The validation results, based on a set of white radish samples, demonstrated the models’ effectiveness in utilizing images for quantifying phenotypic traits. The average accuracy of the automated method was confirmed to be 96.2% when compared to the manual method.

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VPBR: An Automatic and Low-Cost Vision-Based Biophysical Properties Recognition Pipeline for Pumpkin

Dang

Nadeem

Nguyen

et al. 2023

Plants

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Pumpkins are a nutritious and globally enjoyed fruit for their rich and earthy flavor. The biophysical properties of pumpkins play an important role in determining their yield. However, manual in-field techniques for monitoring these properties can be time-consuming and labor-intensive. To address this, this research introduces a novel approach that feeds high-resolution pumpkin images to train a mathematical model to automate the measurement of each pumpkin’s biophysical properties. Color correction was performed on the dataset using a color-checker panel to minimize the impact of varying light conditions on the RGB images. A segmentation model was then trained to effectively recognize two fundamental components of each pumpkin: the fruit and vine. Real-life measurements of various biophysical properties, including fruit length, fruit width, stem length, stem width and fruit peel color, were computed and compared with manual measurements. The experimental results on 10 different pumpkin samples revealed that the framework obtained a small average mean absolute percentage error (MAPE) of 2.5% compared to the manual method, highlighting the potential of this approach as a faster and more efficient alternative to conventional techniques for monitoring the biophysical properties of pumpkins.

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Daily and seasonal heat usage patterns analysis in heat networks

Cited by 3 publications

References 37 publications

Toward explainable heat load patterns prediction for district heating

Toward explainable heat load patterns prediction for district heating

Vision-Based White Radish Phenotypic Trait Measurement with Smartphone Imagery

VPBR: An Automatic and Low-Cost Vision-Based Biophysical Properties Recognition Pipeline for Pumpkin

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