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A training pattern recognition algorithm based on weight clustering for improving cooling load prediction accuracy of HVAC system

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JOURNAL OF BUILDING ENGINEERING 2022年 52卷

作者： Chen, Sihao Wang, Liangzhu (Leon) Li, Jing Zhou, Guang Zhou, Xiaoqing Guangzhou Univ Sch Civil Engn Guangdong Prov Key Lab Bldg Energy Efficiency & A Acad Bldg Energy Efficiency Guangzhou 510006 Peoples R China Concordia Univ Dept Bldg Civil & Environm Engn Ctr Zero Energy Bldg Studies Montreal PQ H3G 1M8 Canada Zhongkai Univ Agr & Engn Guangzhou 510225 Peoples R China

The cooling load-based optimal control is an advanced technology for the efficient operation of heating, ventilation, and air conditioning (HVAC). Thus, the prediction reliability of cooling load plays a key role in HVAC's optimal control. Current publications primarily focused on the structure optimization of prediction models, while less on the clustering-based cooling load prediction. However, the data quality determines the upper limit of the model's prediction performance. Thus, a training pattern recognition algorithm based on weight clustering is proposed for improving cooling load prediction accuracy. Compared with the existing clustering-based prediction methods, the main innovations of the proposed method are: (i) considering the input variables' weights on cooling load in the clustering process;and (ii) investigating the matching between the various prediction models and the K-means clustering algorithm. The case studies showed that the proposed method achieves a significant improvement in the prediction performance, such as MAPEs of the MLR, MNR, and ANN decrease by 34.67%, 35.56%, and 14.53% on average, respectively. Compared with the non-weights clustering method, the introduction of the weights can further improve the above models' prediction accuracy, such as their MAPEs decrease by 6.30%, 7.59%, and 3.07% on average, respectively. These results also demonstrated that the clustering-based prediction method is more suitable for the regression models (e.g., MLR and MNR) with low complexity compared to the ANN. When the clustering number is about 4, the models' prediction performances were more robust. Applying the proposed method to the time-series models (i.e., AR, ARX, and ANN) resulted in their MAPEs as low as 1.79%, 1.78%, and 2.06%, respectively. the proposed method can provide a new idea for improving the accuracy of cooling load prediction.

关键词： Cooling load prediction Pattern recognition clustering algorithm Data preprocessing Mode identification

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Unsupervised clustering identifies thermohaline staircases in the Canada Basin of the Arctic Ocean

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ENVIRONMENTAL DATA SCIENCE 2024年 3卷 e13-e13页

作者： Schee, Mikhail G. Rosenblum, Erica Lilly, Jonathan M. Grisouard, Nicolas Univ Toronto Dept Phys Toronto ON Canada Univ Manitoba Ctr Earth Observat Sci Winnipeg MB Canada Planetary Sci Inst Tucson AZ USA

Thermohaline staircases are a widespread stratification feature that impacts the vertical transport of heat and nutrients and are consistently observed throughout the Canada Basin of the Arctic Ocean. Observations of staircases from the same time period and geographic region form clusters in temperature-salinity (T-S) space. Here, for the first time, we use an automated clustering algorithm called the hierarchical density-based spatial clustering of applications with noise to detect and connect individual well-mixed staircase layers across profiles from ice-tethered profilers. Our application only requires an estimate of the typical layer thickness and expected salinity range of staircases. We compare this method to two previous studies that used different approaches to detect layers and reproduce several results, including the mean lateral density ratio R-L and that the difference in salinity between neighboring layers is a magnitude larger than the salinity variance within a layer. We find that we can accurately and automatically track individual layers in coherent staircases across time and space between different profiles. In evaluating the algorithm's performance, we find evidence of different physical features, namely splitting or merging layers and remnant intrusions. Further, we find a dependence of R-L on pressure, whereas previous studies have reported constant R-L. Our results demonstrate that clustering algorithms are an effective and parsimonious method of identifying staircases in ocean profile data. Impact Statement clustering algorithms are unsupervised machine learning methods that are used across many areas of data science. The use of such methods can automate the identification of certain features, thus allowing for the analysis of very large datasets. Here, we show that a particular clustering algorithm called hierarchical density-based spatial clustering of applications with noise (HDBSCAN) can be used to automatically identify thermohaline stai

关键词： Arctic Ocean clustering algorithm ice-tethered profilers machine learning thermohaline staircases

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Power Load Curve clustering Based on IGWOK-medoids 7

Power Load Curve Clustering Based on IGWOK-medoids

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7th International Conference on Energy, Electrical and Power Engineering (CEEPE)

作者： Zhang, Weifeng Wu, Binfeng Zhao, Xinyi Xu, Jing Zhang, Yihan Wu, Pingping Wang, Li Chen, Xi State Grid Zhejiang Elect Power Co Ltd Lishui Power Supply Co Lishui Peoples R China

ISBN: (纸本)9798350375794;9798350375800

This paper introduces an advanced clustering algorithm for power load curves, named IGWO-Kmedoids, addressing challenges faced by traditional clustering methods like k-means and DBSCAN in handling power system load data. These challenges include difficulty in parameter setting, slow clustering speeds, and suboptimal results. The proposed algorithm begins with data preprocessing, including outlier removal and filling missing values through interpolation, followed by standardization. It then employs Principal Component Analysis (PCA) for dimensionality reduction, easing the computational load for clustering. The core innovation lies in optimizing the K-medoids clustering algorithm with an Improved Grey Wolf Optimization (IGWO) approach, enhancing clustering accuracy and speed. This optimization significantly boosts the power system's operational efficiency and the optimization of scheduling policies.

关键词： clustering algorithm IGWO-Kmedoids PCA

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A NOVEL clustering algorithm BASED ON P SYSTEMS

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INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL 2014年第2期10卷 753-765页

作者： Jiang, Yang Peng, Hong Huang, Xiaoli Zhang, Jiarong Shi, Peng Xihua Univ Ctr Radio Adm & Technol Dev Chengdu 610039 Sichuan Peoples R China Harbin Engn Univ Coll Automat Harbin 150001 Heilongjiang Peoples R China Victoria Univ Sch Engn & Sci Melbourne Vic 8001 Australia

Membrane computing (known as P systems) is a novel class of distributed parallel computing models. In this paper, a partition-based clustering algorithm under the framework of membrane computing is proposed. The clustering algorithm is based on a tissue-like P system, which is used to exploit the optimal cluster centers for a data set. Each object in the tissue-like P system represents a group of candidate cluster centers and is evolved through simulated annealing mechanism and mutation mechanism. Meanwhile, communication rules are used to exchange and share the objects between different elementary membranes and between elementary membranes and the environment. The proposed clustering algorithm is evaluated over two artificial data sets and two real-life data sets and is further compared with k-means algorithm and GA-based k-means algorithm respectively. The comparison results reveal the superiority of the proposed clustering algorithm in terms of clustering quality and stability.

关键词： Membrane computing Tissue-like P systems clustering algorithm Simulated annealing K-means

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Robust optimization-based energy management system for integrating renewable energy resources and smart grid technology

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SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS 2025年 75卷

作者： Loganathan, N. Mayurappriyan, P. S. Lakshmi, K. Madhankumar, S. Sri Krishna Coll Engn & Technol Dept Elect & Elect Engn Coimbatore 641008 India Kumaraguru Coll Technol Dept Elect & Instrumentat Engn Coimbatore 641049 India KSR Coll Engn Dept Elect & Elect Engn Tiruchengode 637215 India Sri Krishna Coll Engn & Technol Dept Mechatron Engn Coimbatore 641008 India

This study focuses on enhancing Energy Management (EM) by integrating Renewable Energy Resources (RERs) like Wind Energy (WE) and Solar Energy (SE) with grid technology. To analyze the proposed integration technology, an academic campus with interconnected group, cost-responsive demands was considered. These demands can be met using both the grid and RERs;however, EM challenges arise when integrating RERs with the grid. The Evolutionary Energy Preserve Rate clustering algorithm (EEPRCA) was utilized to analyze the EM, and the Robust Optimization (RO) strategy was used to predict the unpredictability associated with the power output of RERs, as well as the cost of power produced and energy received from or sold to the main grid. Additionally, Smart Grid (SG) technology was employed to enable bidirectional connectivity between the Energy Management System (EMS) and the main grid, as well as between the EMS and RERs. The EMS plans energy consumption at the start of the working day without SG. However, with SG, the EMS uses bidirectional communication to reschedule utilization based on energy prices and WE and SE generation. Using SG technology leads to a 32% improvement in total utility grid efficiency compared to an EMS without SG. Also, an EMS with SG maximizes the use of available wind and solar RERs, whereas without SG only 55% of these resources are utilized. From these outcomes, it is suggested that the proposed EMS integration strategy can efficiently manage energy, increase the consumption of RERs, and reduce the energy utilization cost for the academic campus.

关键词： Energy management system Renewable energy resources Smart grid clustering algorithm Robust optimization

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Design and operation of future low-voltage community microgrids: An AI-based approach with real case study

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APPLIED ENERGY 2025年第PartC期377卷

作者： Alam, Md Morshed Hossain, M. J. Zamee, Muhammad Ahsan Al-Durra, Ahmed Univ Technol Sydney Sch Elect & Data Engn Ultimo NSW 2007 Australia Khalifa Univ Dept Elect Engn & Comp Sci Abu Dhabi U Arab Emirates

The utilization of artificial intelligence in the design and operation of a microgrid (MG) can contribute to improve its energy efficiency, resiliency, and cost of energy supply. This research proposes a new approach to conduct a comprehensive analysis for transforming existing low-voltage networks into MGs to achieve the net-zero goal by 2050. A data-driven machine learning-based clustering and profiling approach is designed and implemented to extract the data, constraints, and dependencies from the historical data. Furthermore, the constraints and dependencies are utilized for determining the renewable energy sources' capacity. A Bi-level optimization technique is developed to ensure appropriate coordination of cost and renewable energy source (RES) capacity. A comprehensive analysis is carried out utilizing real historical demand and generation data of an energy community in Australia. Based on the clustered analysis, the consecutive day's data are considered for the analysis. The findings reveal that the proposed microgrids achieve higher renewable RES utilization and lower electricity costs compared to grid-connected systems, with the potential to reduce carbon emissions by up to 98.23% when transitioning from coal-based grid systems to the proposed microgrid system. Additionally, a transformation from a grid time-of-use tariff-based system to the proposed microgrid setup can lead to a cost reduction of 65.45%. These case studies will also assist the researcher in identifying new, potential ideas and industries to accelerate the implementation of remote community microgrids.

关键词： Microgrid Energy management system Artificial intelligence clustering algorithm MILP optimization PSO optimization Hydrogen-based electrical vehicle

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Machine learning assisted fast optical identification of 2D materials

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APPLIED SURFACE SCIENCE 2025年 686卷

作者： He, Junqi Zhou, Yi Zhang, Pan Zhai, Huifei China Jiliang Univ Coll Sci Hangzhou 310018 Zhejiang Peoples R China Arizona State Univ Sch Mol Sci Tempe AZ 85287 USA

Machine learning has driven research and development in the field of materials. We investigate the thickness identification of few-layer tungsten ditelluride (WTe2) flakes based on optical microscopy images, combined with machine learning, and found a fast method for identifying 1- to 4-layers WTe2. We only use a single channel to obtain features. Green channel values of color are used to obtain the optical contrast. Unsupervised learning algorithms, including nearest neighbor assignment clustering, k-means clustering, and Mean-Shift clustering, are employed to extract data features and then build a thickness database of few-layer WTe2. Our results show that via machine learning analysis, the correlation between layer number counts and optical contrast values can be derived, and the layer-number-dependent optical reflection can be well described by the Beer-Lambert law. This provides a fast and reliable way to determine the thickness of WTe2 flakes, which will greatly improve the efficiency of identifying atomically thin samples. After obtaining the classification and labeling, we use the deep learning YOLOv8 model to segment optical microscope images. We have enhanced the model's accuracy by improving the algorithm, resulting in excellent generalization ability and robustness. Our proposed method is faster, more accurate, and more convenient than usual methods. This method can also reduce costs and time, avoiding the hassle of batch measurement.

关键词： 2D materials Machine learning YOLO clustering algorithm Instance segmentation

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MPC: A Novel Internal clustering Validity Index Based on Midpoint-Involved Distance 1

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23rd International Conference on algorithms and Architectures for Parallel Processing (ICA3PP)

作者： Zuo, Yating Ma, Zhujuan Zhu, Erzhou Anhui Univ Sch Comp Sci & Technol Hefei 230601 Peoples R China Anhui Xinhua Univ Sch Big Data & Artificial Intelligence Hefei 230088 Peoples R China

ISBN: (数字)9789819707980

ISBN: (纸本)9789819707973;9789819707980

As one of the most import machine learning technique, clustering is widely used in many data classification areas. Due to the unsupervised learning feature, the quality of the clustering results needed to be evaluated. In this paper, the MPdist (midpoint-involved distance) based on the midpoint of centers between two clusters is firstly defined to measure the inter-cluster separation. Then, the MPC (MPdist based clustering validity index), a novel internal clustering validity index based on the combination of the new defined inner-cluster compactness and the inter-cluster separation, is proposed to effectively evaluate the validity of the clustering results of many clustering algorithms. Experimental results on testing many types of datasets have demonstrated that the MPC index proposed in this paper is able to quickly handle datasets like spherical datasets, non-spherical datasets and real large-scale datasets.

关键词： clustering validity index clustering algorithm data mining

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Advancing Image-to-Image Translation Model Assessment with clustering Technique 11

Advancing Image-to-Image Translation Model Assessment with C...

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11th IEEE International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan) - Empower of Innovative Consumer Technology

作者： Chen, Rung-Ching Sub-r-pa, Chayanon Chaoyang Univ Technol Dept Informat Management Taichung Taiwan

ISBN: (纸本)9798350386851;9798350386844

The Frechet Inception Distance (FID) is a metric commonly used to assess the quality of images generated by generative models. However, FID may not be suitable for evaluating Image-to-Image (I2I) translations since the results may sometimes have defects or may not be in the target domain. We proposed a new method to assess I2I translations, which involves calculating a new scoring system. Single Image Inception Distance (SiID), a modified version of FID, assigns a quality score to each generated image and CNN-based classification model to determine its classification probability. SiID and classification probability are then used in a clustering algorithm to gain more insight into evaluating the I2I Model. Our proposed method has been demonstrated on existing I2I translation models for translating male-to-female images, and the results have shown that our method can classify the generated images into three groups: successfully translated, lacking realism, and failing to translate.

关键词： Evaluation method GAN CycleGAN Image-to-image CNN clustering algorithm

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γ-SUP: A clustering algorithm FOR CRYO-ELECTRON MICROSCOPY IMAGES OF ASYMMETRIC PARTICLES

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ANNALS OF APPLIED STATISTICS 2014年第1期8卷 259-285页

作者： Chen, Ting-Li Hsieh, Dai-Ni Hung, Hung Tu, I-Ping Wu, Pei-Shien Wu, Yi-Ming Chang, Wei-Hau Huang, Su-Yun Acad Sinica Inst Stat Sci Taipei 11529 Taiwan Natl Taiwan Univ Inst Epidemiol & Prevent Med Taipei 10055 Taiwan Duke Univ Dept Biostat & Bioinformat Durham NC 27710 USA

Cryo-electron microscopy (cryo-EM) has recently emerged as a powerful tool for obtaining three-dimensional (3D) structures of biological macromolecules in native states. A minimum cryo-EM image data set for deriving a meaningful reconstruction is comprised of thousands of randomly orientated projections of identical particles photographed with a small number of electrons. The computation of 3D structure from 2D projections requires clustering, which aims to enhance the signal to noise ratio in each view by grouping similarly oriented images. Nevertheless, the prevailing clustering techniques are often compromised by three characteristics of cryo-EM data: high noise content, high dimensionality and large number of clusters. Moreover, since clustering requires registering images of similar orientation into the same pixel coordinates by 2D alignment, it is desired that the clustering algorithm can label misaligned images as outliers. Herein, we introduce a clustering algorithm gamma-SUP to model the data with a q-Gaussian mixture and adopt the minimum gamma-divergence for estimation, and then use a self-updating procedure to obtain the numerical solution. We apply gamma-SUP to the cryo-EM images of two benchmark macromolecules, RNA polymerase II and ribosome. In the former case, simulated images were chosen to decouple clustering from alignment to demonstrate gamma-SUP is more robust to misalignment outliers than the existing clustering methods used in the cryo-EM community. In the latter case, the clustering of real cryo-EM data by our gamma-SUP method eliminates noise in many views to reveal true structure features of ribosome at the projection level.

关键词： clustering algorithm cryo-EM images gamma-divergence k-means mean-shift algorithm multilinear principal component analysis q-Gaussian distribution robust statistics self-updating process

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