Multi-scene design analysis of integrated energy system based on feature extraction algorithm

Huang, Sihua; Ali, Noor Azizi Mohd; Shaari, Nazlina; Noor, Mohd Jamaludin Md

doi:10.1016/j.egyr.2022.03.161

Cited by 4 publications

(3 citation statements)

References 18 publications

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“…The main challenge in heat load forecasting is the translation of historical data into a predictive model and the accuracy of the predictive model. To address this problem, Huang et al [34] used a convolutional neural network to extract the feature vectors of environmental factors, and then the K-means clustering algorithm was used to establish the feature clustering model of various energy loads, which in turn led to the load prediction results of multi-energy systems. Gu et al [35] used outdoor temperatures and historical heat loaders as influencing factors.…”

Section: Machine Learning-based Methodsmentioning

confidence: 99%

Bayesian Optimization-Based LSTM for Short-Term Heating Load Forecasting

Li,

Shao,

Lian

et al. 2023

Energies

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With the increase in population and the progress of industrialization, the rational use of energy in heating systems has become a research topic for many scholars. The accurate prediction of heat load in heating systems provides us with a scientific solution. Due to the complexity and difficulty of heat load forecasting in heating systems, this paper proposes a short-term heat load forecasting method based on a Bayesian algorithm-optimized long- and short-term memory network (BO-LSTM). The moving average data smoothing method is used to eliminate noise from the data. Pearson’s correlation analysis is used to determine the inputs to the model. Finally, the outdoor temperature and heat load of the previous period are selected as inputs to the model. The root mean square error (RMSE) is used as the main evaluation index, and the mean absolute error (MAE), mean bias error (MBE), and coefficient of determination (R2) are used as auxiliary evaluation indexes. It was found that the RMSE of the asynchronous length model decreased, proving the general practicability of the method. In conclusion, the proposed prediction method is simple and universal.

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Section: Machine Learning-based Methodsmentioning

confidence: 99%

Bayesian Optimization-Based LSTM for Short-Term Heating Load Forecasting

Li,

Shao,

Lian

et al. 2023

Energies

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“…Conversely, some studies utilize the K-means algorithm to extract new features based on consumption similarities, ultimately building a unified model for all clusters [223], [225], [232], [233], [234]. In this case, the new features derived from K-means serve as additional inputs to the model.…”

Section: ) Load Forecastingmentioning

confidence: 99%

K-Means and Alternative Clustering Methods in Modern Power Systems

Miraftabzadeh,

Colombo,

Longo

et al. 2023

IEEE Access

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As power systems evolve by integrating renewable energy sources, distributed generation, and electric vehicles, the complexity of managing these systems increases. With the increase in data accessibility and advancements in computational capabilities, clustering algorithms, including K-means, are becoming essential tools for researchers in analyzing, optimizing, and modernizing power systems. This paper presents a comprehensive review of over 440 articles published through 2022, emphasizing the application of K-means clustering, a widely recognized and frequently used algorithm, along with its alternative clustering methods within modern power systems. The main contributions of this study include a bibliometric analysis to understand the historical development and wide-ranging applications of K-means clustering in power systems. This research also thoroughly examines K-means, its various variants, potential limitations, and advantages. Furthermore, the study explores alternative clustering algorithms that can complete or substitute K-means. Some prominent examples include K-medoids, Time-series K-means, BIRCH, Bayesian clustering, HDBSCAN, CLIQUE, SPECTRAL, SOMs, TICC, and swarm-based methods, broadening the understanding and applications of clustering methodologies in modern power systems. The paper highlights the wide-ranging applications of these techniques, from load forecasting and fault detection to power quality analysis and system security assessment. Throughout the examination, it has been observed that the number of publications employing clustering algorithms within modern power systems is following an exponential upward trend. This emphasizes the necessity for professionals to understand various clustering methods, including their benefits and potential challenges, to incorporate the most suitable ones into their studies.INDEX TERMS Clustering algorithms, K-means clustering, power systems. FIGURE 3. K-means clustering and power system bibliometric trend per country.

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“…These algorithms can be applied to various areas of medical imaging including image enhancement, segmentation, feature extraction, classification, alignment and visualisation. Common optimization algorithms for medical image analysis include: image enhancement algorithms [29,30], image segmentation algorithms [31,32], feature extraction algorithms [33,34], classification and recognition algorithms [35], image alignment algorithms [36,37], visualization algorithms [38,39], deep learning algorithms [40,41], uncertainty modelling algorithms, and so on. The development and application of these algorithms have made medical image analysis more accurate, automated and efficient.…”

Section: Introductionmentioning

confidence: 99%

<strong>Recent Optimization Methods and Techniques for Medical Image Analysis</strong>

wang

2023

Preprint

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Medical image analysis is an important branch in the field of medicine, which mainly uses image processing and analysis techniques to interpret and diagnose medical image data. Medical image data helps doctors to effectively observe and diagnose patients' body structures, tissues and lesions. Medical image analysis has been an important research area in the medical field, and it is important for disease diagnosis, treatment planning, and condition monitoring. In recent years, the rapid development of deep learning and computer vision technologies has contributed greatly to the automation, multimodal data fusion, real-time application, and accuracy improvement of medical image analysis. In addition, the development of deep learning has given rise to some new research areas in medical image analysis, such as Generative Adversarial Networks (GANs) for synthetic medical images, self-supervised learning for unsupervised feature learning, and neural network interpretability. In this paper, we will introduce some optimisation methods for medical images which are effective in improving the accuracy, efficiency and reliability of medical image analysis.

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Multi-scene design analysis of integrated energy system based on feature extraction algorithm

Cited by 4 publications

References 18 publications

Bayesian Optimization-Based LSTM for Short-Term Heating Load Forecasting

Bayesian Optimization-Based LSTM for Short-Term Heating Load Forecasting

K-Means and Alternative Clustering Methods in Modern Power Systems

<strong>Recent Optimization Methods and Techniques for Medical Image Analysis</strong>

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