Consumers' load profile determination based on different classification methods

Gerbec, D.; Gasperic, S.; Smon, I.; Gubina, F.

doi:10.1109/pes.2003.1270445

Cited by 19 publications

(14 citation statements)

References 11 publications

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“…The performance of a clustering algorithm is checked either with qualitative or quantitative criteria [17,18]. In the qualitative assessment, different algorithms are compared based on the shapes of the generated load profiles and the clustering compositions, i.e., the number of patterns that belong to each cluster.…”

Section: Solution Approachmentioning

confidence: 99%

Optimal Selection of Clustering Algorithm via Multi-Criteria Decision Analysis (MCDA) for Load Profiling Applications

Panapakidis

Christoforidis

2018

Applied Sciences

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Due to high implementation rates of smart meter systems, considerable amount of research is placed in machine learning tools for data handling and information retrieval. A key tool in load data processing is clustering. In recent years, a number of researches have proposed different clustering algorithms in the load profiling field. The present paper provides a methodology for addressing the aforementioned problem through Multi-Criteria Decision Analysis (MCDA) and namely, using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). A comparison of the algorithms is employed. Next, a single test case on the selection of an algorithm is examined. User specific weights are applied and based on these weight values, the optimal algorithm is drawn.

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Section: Solution Approachmentioning

confidence: 99%

Optimal Selection of Clustering Algorithm via Multi-Criteria Decision Analysis (MCDA) for Load Profiling Applications

Panapakidis

Christoforidis

2018

Applied Sciences

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“…In the case of consumers' typical load profile studies, the electrical consumers may be represented by its typical daily load diagram. During this step, the normalization can be made to enable the comparison of the load diagram shape among the electrical consumers [25][26][27].…”

Section: Data Mining Techniques To Characterize Electrical Consumentioning

confidence: 99%

Typical load profiles in the smart grid context — A clustering methods comparison

Ramos

Duarte

Soares

et al. 2012

2012 IEEE Power and Energy Society General Meeting

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The present research paper presents five different clustering methods to identify typical load profiles of medium voltage (MV) electricity consumers. These methods are intended to be used in a smart grid environment to extract useful knowledge about customer's behaviour. The obtained knowledge can be used to support a decision tool, not only for utilities but also for consumers. Load profiles can be used by the utilities to identify the aspects that cause system load peaks and enable the development of specific contracts with their customers. The framework presented throughout the paper consists in several steps, namely the pre-processing data phase, clustering algorithms application and the evaluation of the quality of the partition, which is supported by cluster validity indices. The process ends with the analysis of the discovered knowledge. To validate the proposed framework, a case study with a real database of 208 MV consumers is used.

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“…But, it also proved to be the case that there are field indices derived from load shapes that are highly correlated with load profiles. Gerbec [27] maintained that the load profile allocation should be simple and straightforward in order to avoid problems and unnecessary expense. Accordingly, he used business activity codes as indices in his research.…”

Section: Loading Conditionsmentioning

confidence: 99%

Customer Information System Data Pre-Processing with Feature Selection Techniques for Non-Technical Losses Prediction in an Electricity Market

Nizar

Zhao

Zhang

2006

2006 International Conference on Power System Technology

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Non-technical losses (NTL) identification and prediction are important tasks for many utilities. Data from customer information system (CIS) can be used for NTL analysis. However, in order to accurately and efficiently perform NTL analysis, the original data from CIS need to be pre-processed before any detailed NTL analysis can be carried out. In this paper, we propose a feature selection based method for CIS data pre-processing in order to extract the most relevant information for further analysis such as clustering and classifications. By removing irrelevant and redundant features, feature selection is an essential step in data mining process in finding optimal subset of features to improve the quality of result by giving faster time processing, higher accuracy and simpler results with fewer features. Detailed feature selection analysis is presented in the paper. Both time-domain and load shape data are compared based on the accuracy, consistency and statistical dependencies between features.

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Consumers' load profile determination based on different classification methods

Cited by 19 publications

References 11 publications

Optimal Selection of Clustering Algorithm via Multi-Criteria Decision Analysis (MCDA) for Load Profiling Applications

Optimal Selection of Clustering Algorithm via Multi-Criteria Decision Analysis (MCDA) for Load Profiling Applications

Typical load profiles in the smart grid context — A clustering methods comparison

Customer Information System Data Pre-Processing with Feature Selection Techniques for Non-Technical Losses Prediction in an Electricity Market

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Consumers' load profile determination based on different classification methods

Cited by 19 publications

References 11 publications

Optimal Selection of Clustering Algorithm via Multi-Criteria Decision Analysis (MCDA) for Load Profiling Applications

Optimal Selection of Clustering Algorithm via Multi-Criteria Decision Analysis (MCDA) for Load Profiling Applications

Typical load profiles in the smart grid context &#x2014; A clustering methods comparison

Customer Information System Data Pre-Processing with Feature Selection Techniques for Non-Technical Losses Prediction in an Electricity Market

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Typical load profiles in the smart grid context — A clustering methods comparison