Power Load Pattern Classification Based on Threshold and Cloud Improved Fuzzy Clustering

This paper presents selected similar days of short-term power load forecasting model based on fuzzy clustering, the method first meteorological factors subdivided into temperature, barometric pressure, wind speed, rain, etc., and then type the week, date, type of day together constitute similar factors, fuzzy coefficient feature mapping table through fuzzy rules, not only to achieve quantitative impact factors and facilitate real-time to add a new rule. On this basis, the use of fuzzy clustering method for classification, based on the level of clustering similar day is selected to reduce the number of samples to accelerate the selected speed. The model takes into account the full impact of weather and other factors on load forecasting, further weakening the load of randomness. Simulation results show that the method has higher prediction accuracy.

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Research on Power Load Classification Based on Feature Weighted Fuzzy Clustering

10.1109/icisce50968.2020.00244 ◽

2020 ◽

Author(s):

Jiaming Zhang ◽

Yingying Cheng ◽

Jie Du ◽

Yongsheng Shu ◽

Feng Zhou ◽

...

Keyword(s):

Fuzzy Clustering ◽

Power Load

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Research on Reclassification of Power Users for TOU Power Price Based on Fuzzy Clustering

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.229-231.1013 ◽

2012 ◽

Vol 229-231 ◽

pp. 1013-1016

Author(s):

Ling Luo ◽

Bao Chen Jiang ◽

Li Kai Liang

Keyword(s):

Power Consumption ◽

Fuzzy Clustering ◽

Theoretical Basis ◽

Clustering Algorithm ◽

Simulation Analysis ◽

Reference Value ◽

Actual Condition ◽

Load Curve ◽

Fuzzy Clustering Algorithm ◽

Power Load

Study on TOU (Time-of-Use) power price in our country almost treats power users as a unified whole, but different categories of users have the different power consumption and way in the actual condition. Therefore, they have different responses to TOU power price. According to the power load features of all kinds of users, the paper presents a solution to reclassify the users using fuzzy clustering algorithm, and provides theoretical basis for implementing categorized TOU power price. Finally comparatively perfect effect is obtained by simulation analysis, and it has great reference value to perfect TOU power price and improve load curve.

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Pattern Classification of Human Cognitive State in Man-Machine Systems Using Multiple Sources of Pyschophysiological Data and Fuzzy Clustering Approach

IFAC Proceedings Volumes ◽

10.3182/20140824-6-za-1003.00609 ◽

2014 ◽

Vol 47 (3) ◽

pp. 2957-2962

Author(s):

Jian-Hua Zhang

Keyword(s):

Pattern Classification ◽

Fuzzy Clustering ◽

Cognitive State ◽

Multiple Sources ◽

Clustering Approach ◽

Machine Systems

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Use of multiobjective differential fuzzy clustering with ANN classifier for unsupervised pattern classification: Application to microarray analysis

2010 Fourth International Conference on Research Challenges in Information Science (RCIS) ◽

10.1109/rcis.2010.5507278 ◽

2010 ◽

Author(s):

Indrajit Saha

Keyword(s):

Microarray Analysis ◽

Pattern Classification ◽

Fuzzy Clustering ◽

Ann Classifier

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The Influence on Clustering Results of Electricity Load Curves Using Different Distances

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.401-403.1440 ◽

2013 ◽

Vol 401-403 ◽

pp. 1440-1443 ◽

Cited By ~ 1

Author(s):

Tie Feng Zhang ◽

Fei Lv ◽

Rong Gu

Keyword(s):

Clustering Algorithm ◽

Clustering Algorithms ◽

Pattern Extraction ◽

Clustering Techniques ◽

Comparison Results ◽

Power Load ◽

Load Pattern ◽

Electricity Load ◽

Mining Algorithms

Distance choice is an important issue in power load pattern extraction using clustering techniques, so it is necessary to find the influence on clustering result of load curves using different distances in clustering algorithms. In this paper several distances are used in the k-means algorithm for clustering load curves and their influences on the clustering results are analyzed, therefore, the suitable distance for the k-means algorithms is obtained. An example with 147 electricity customers load curves shows distances have different influences on clustering results using the same clustering algorithm. The comparison results indicate that the choice of distances is an important issue in power load pattern extraction using clustering techniques and a suitable distance may improve the accuracy of mining algorithms.

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Predicting the Power Load of EV in Public Transportation Sector Based on Fuzzy Clustering Analysis

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.953-954.1349 ◽

2014 ◽

Vol 953-954 ◽

pp. 1349-1353

Author(s):

Jun Chen ◽

Chun Lin Guo ◽

Wen Chen ◽

Zhe Ci Tang ◽

Zong Feng Li ◽

...

Keyword(s):

Fuzzy Clustering ◽

Public Transportation ◽

Power Distribution ◽

Clustering Analysis ◽

Back Propagation ◽

Influence Factors ◽

Predicting Model ◽

Fuzzy Clustering Analysis ◽

Transportation Sector ◽

Power Load

The electric demand of EV in the public transportation sector is increasingly important to the future city’s power distribution and even infrastructure construction. According to the characteristic of public transportation, this paper analyzed the influence factors of EV power load and they were divided into three parts. Then a predicting model of EV power load in public transportation based on fuzzy clustering analysis method was put forward. We used BP (Back Propagation) neural network algorithm to solve the fuzzy clustering analysis problem. Finally the predicting model was operated in a practical example. Results showed that this predicting model of EV power load in public transportation based on fuzzy clustering analysis could be appropriately applied in reality.

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