A factor analysis and self-organizing map based evaluation approach for the renewable energy heating potentials at county level: A case study in China

Zheng, Xuejing; Yang, Xueqing; Miao, Hongfei; Liu, Huzhen; Yu, Yanzhe; Wang, Yaran; Zhang, Huan; You, Shijun

doi:10.1016/j.rser.2022.112597

Cited by 7 publications

(5 citation statements)

References 40 publications

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“…User preferences and current utilization rate were derived from a questionnaire distributed to residents. RE heating potentials can be estimated using the method proposed by (Zheng et al, 2022).…”

Section: Data Sourcementioning

confidence: 99%

“…How to evaluate the best RE project efficiently is a strategic and significant problem that decision makers need to face (Zheng et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, a novel evaluation model is proposed and applied to the selection of RE heating systems. Solar, biomass and geothermal energy are discussed as they are commonly used for heating (Cansino et al, 2011;Zheng et al, 2022). Five RE heating systems are selected to be evaluated and ranked in terms of economic, technical, environmental, social and resource criteria.…”

Section: Introductionmentioning

confidence: 99%

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A novel multilevel decision-making evaluation approach for the renewable energy heating systems: A case study in China

Yang

Zheng

Zhou

et al. 2023

Journal of Cleaner Production

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Section: Data Sourcementioning

confidence: 99%

“…How to evaluate the best RE project efficiently is a strategic and significant problem that decision makers need to face (Zheng et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A novel multilevel decision-making evaluation approach for the renewable energy heating systems: A case study in China

Yang

Zheng

Zhou

et al. 2023

Journal of Cleaner Production

Self Cite

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“…Additionally, these methods often lack a systematic approach to assigning weights to different indicators, which is critical for generating meaningful and actionable insights. To address the limitations inherent in existing evaluation methodologies, there is a growing demand for a refined approach that can effectively identify key performance indicators and rationally allocate weights to these indicators [11][12][13] .…”

Section: Introduction 1importance Of Performance Evaluation In Distri...mentioning

confidence: 99%

Integrative Performance Assessment of Electrical Distribution Networks: An Integrated Indicators and Weight Processing Strategy

Hu,

Chai,

Jing

et al. 2024

Preprint

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This study presents a novel framework This study proposes a new framework for evaluating the performance of distribution networks using a comprehensive approach. This method combines Principal Component Analysis (PCA) for extracting key performance indicators, Improved Analytic Hierarchy Process (IAHP) for determining objective weights of these indicators, and CRITIC method for determining subjective weights. Then, aggregate weights are used to calculate the comprehensive performance score of each part of the distribution network. On the premise of reducing computing resources, K-means clustering analysis is applied to classify the status of distribution network projects, providing data reference for project maintenance and repair. Finally, a case study and corresponding analysis of the evaluation results were presented, providing detailed insights into the practical application of the proposed framework. The limitations of current research and future research approaches were also discussed. The results indicate that the designed strategy performs well in terms of accuracy and computational convenience, and has potential application prospects

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“…Their performance, however, depends heavily on the appropriate selection of underlying models and model parameters. SOM is widely used among the algorithms of this class [16]- [19]. There are densitybased clustering algorithms (e.g., DBSCAN [20]) which perform clustering in real hyper-space with the number of clusters generated from the data without any prior knowledge.…”

Section: Introductionmentioning

confidence: 99%

Piecemeal Clustering: a Self-Driven Data Clustering Algorithm

et al. 2022

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Various approaches have been discussed in the literature for the clustering of data, such as partitioning, hierarchical, and machine learning methods. Most of the approaches require some prior knowledge about the clusters, such as their total number. Furthermore, some previous algorithms are not robust enough to process higher-dimensional data or require a large amount of memory for computations. We propose, herein, a data clustering algorithm, Piecemeal Clustering, that successfully clusters data without prior knowledge of the number of clusters. The proposed clustering algorithm uses the similarity and density of the data to identify the number of clusters in the data set and works with both low-and high-dimensional data. We demonstrate the power of the proposed Piecemeal Clustering algorithm with two real-world data sets. It is found that the proposed algorithm outperforms seven other state-of-the-art algorithms on both of these data sets.

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A factor analysis and self-organizing map based evaluation approach for the renewable energy heating potentials at county level: A case study in China

Cited by 7 publications

References 40 publications

A novel multilevel decision-making evaluation approach for the renewable energy heating systems: A case study in China

A novel multilevel decision-making evaluation approach for the renewable energy heating systems: A case study in China

Integrative Performance Assessment of Electrical Distribution Networks: An Integrated Indicators and Weight Processing Strategy

Piecemeal Clustering: a Self-Driven Data Clustering Algorithm

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