Customer Classification and Load Profiling Method for Distribution Systems

Mutanen, Antti; Ruska, Maija; Repo, Sami; Järventausta, Pertti

doi:10.1109/tpwrd.2011.2142198

“…For example, in [14] data mining techniques are applied to extract load profiles from individual load data of a set of low voltage Portuguese customers, and then supervised classification methods are used to allocate customers to the different classes. In [15], load profiles are obtained by iterative self-organizing data analysis on metered data and demonstrated on a set of 660 hourly metered customers in Finland. Ref.…”

Section: Individual Electrical Consumption Data: a State-of-the-artmentioning

confidence: 99%

Scalable Clustering of Individual Electrical Curves for Profiling and Bottom-Up Forecasting

Auder

¹

,

Cugliari

²

,

Goude

³

et al. 2018

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Smart grids require flexible data driven forecasting methods. We propose clustering tools for bottom-up short-term load forecasting. We focus on individual consumption data analysis which plays a major role for energy management and electricity load forecasting. The first section is dedicated to the industrial context and a review of individual electrical data analysis. Then, we focus on hierarchical time-series for bottom-up forecasting. The idea is to decompose the global signal and obtain disaggregated forecasts in such a way that their sum enhances the prediction. This is done in three steps: identify a rather large number of super-consumers by clustering their energy profiles, generate a hierarchy of nested partitions and choose the one that minimize a prediction criterion. Using a nonparametric model to handle forecasting, and wavelets to define various notions of similarity between load curves, this disaggregation strategy gives a 16% improvement in forecasting accuracy when applied to French individual consumers. Then, this strategy is implemented using R-the free software environment for statistical computing-so that it can scale when dealing with massive datasets. The proposed solution is to make the algorithm scalable combine data storage, parallel computing and double clustering step to define the super-consumers. The resulting software is openly available.

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“…The authors apply it to the daily load curves of active and reactive load of a high-voltage consumer. The ISODATA algorithm is employed in Reference [62] for clustering the load curves of 660 hourly metered consumers. The results are compared with the existing load profiles classes of a Finnish utility.…”

Section: Literature Survey and Contributionsmentioning

confidence: 99%

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

Panapakidis

¹

,

Christoforidis

²

2018

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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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“…For example, in [14] data mining techniques are applied to extract load profiles from individual load data of a set of low voltage Portuguese customers, and then supervised classification methods are used to allocate customers to the different classes. In [15], load profiles are obtained by iterative self-organizing data analysis on metered data and demonstrated on a set of 660 hourly metered customers in Finland. [16] proposes an unsupervised clustering approach based on k-means on features obtained by average seasonal curves using minute metered data from 103 homes in Austin, TX.…”

Section: Individual Electrical Consumption Data: a State-of-the-artmentioning

confidence: 99%

Scalable Clustering of Individual Electrical Curves for Profiling and Bottom-up Forecasting

Auder¹,

Cugliari²,

Goude³

et al. 2018

Preprint

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Smart grids require flexible data driven forecasting methods. We propose clustering tools for bottom-up short-term load forecasting. We focus on individual consumption data analysis which plays a major role for energy management and electricity load forecasting. The two first sections are dedicated to the industrial context and a review of individual electrical data analysis. We are interested in hierarchical time-series for bottom-up forecasting. The idea is to disaggregate the signal in such a way that the sum of disaggregated forecasts improves the direct prediction. The 3-steps strategy defines numerous super-consumers by curve clustering, builds a hierarchy of partitions and selects the best one minimizing a forecast criterion. Using a nonparametric model to handle forecasting, and wavelets to define various notions of similarity between load curves, this disaggregation strategy applied to French individual consumers leads to a gain of 16% in forecast accuracy. We then explore the upscaling capacity of this strategy facing massive data and implement proposals using R, the free software environment for statistical computing. The proposed solutions to make the algorithm scalable combines data storage, parallel computing and double clustering step to define the super-consumers.Keywords: Clustering; Forecasting; Hierarchical Time-Series; Individual Electrical Consumers; Scalable; Short Term; Smart Meters; Wavelets Industrial contextEnergy systems are facing a revolution and many challenges. On the one hand, electricity production is moving to more intermittency and complexity with the increase of renewable energy and the development of small distributed production units such as photovoltaic panels or wind farms. On the other hand, consumption is also changing with plug-in (hybrid) electric vehicles, heat pumps, the development of new technologies such as smart phones, computers, robots that often come with batteries. To maintain the electricity quality, energy stakeholders are developing smart grids (see [1,2] This results into new opportunities such as local optimisation of the grid, demand side management and smart control of storage devices. Exploiting the smart grid efficiently requires advanced data analytics and optimisation techniques to improve forecasting, unit commitment, and load planning at different geographical scales. Massive data sets are and will be produced as Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted:

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Customer Classification and Load Profiling Method for Distribution Systems

Cited by 162 publications

References 9 publications

Scalable Clustering of Individual Electrical Curves for Profiling and Bottom-Up Forecasting

Scalable Clustering of Individual Electrical Curves for Profiling and Bottom-Up Forecasting

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

Scalable Clustering of Individual Electrical Curves for Profiling and Bottom-up Forecasting

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