Allocation of the Load Profiles to Consumers Using Probabilistic Neural Networks

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

doi:10.1109/tpwrs.2005.846236

Cited by 136 publications

(63 citation statements)

References 13 publications

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“…On the other hand in [23][24], Probabilistic Neural Networks (PNNs) and Fuzzy C Means (FCM) clustering algorithm were used to determine and allocate the typical LPs to consumers. Unsupervised learning based on SOMs have been used in [25][26][27] to classify, filter and identify customers' consumption patterns in order to learn both their distribution and topology, and segment the demand patterns for electrical customers.…”

Section: Related Workmentioning

confidence: 99%

A Mobile-based Platform for Big Load Profiles Data Analytics in Non-Advanced Metering Infrastructures

Moussa

2016

MATEC Web Conf.

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Abstract. With the rapidly increase of electricity demand around the world due to industrialization and urbanization, this turns the availability of precise knowledge about the consumption patterns of consumers to a valuable asset for electricity providers, given the current competitive electricity market. This would allow them to provide satisfactory services in time of load peaks and to control fraud and abuse cases. Despite of this crucial necessity, this is currently very hard to achieve in many developing countries since smart meters or advanced metering infrastructures (AMIs) are not yet settled there to monitor and report energy usages. Whereas the communication and information technologies have widely emerged in such nations, allowing the enormous spread of smart devices among population. In this paper, we present mobile-based BLPDA, a novel platform for big data analytics of consumers' load profiles (LPs) in the absence of AMIs' establishment. The proposed platform utilizes mobile computing in order to collect the consumptions of consumers, build their LPs, and analyze the aggregated usages data. Thus, allowing electricity providers to have better vision for an enhanced decision making process. The experimental results emphasize the effectiveness of our platform as an adequate alternative for AMIs in developing countries with minimal cost.

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Section: Related Workmentioning

confidence: 99%

A Mobile-based Platform for Big Load Profiles Data Analytics in Non-Advanced Metering Infrastructures

Moussa

2016

MATEC Web Conf.

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“…The studies [2,7], and [8] use error distance to evaluate the performance of TLP. The error distance is calculated by Euclidean distance between TLP of each cluster and RLP of the customer included in each cluster.…”

Section: Fig 1 Example Of Load Analysis Using Rlp and Vlpmentioning

confidence: 99%

“…Load analysis using the virtual load profile (VLP) has been studied as an alternative [2]. VLP is generated by assigning the monthly usage of a non-automatic meter reading (non-AMR) customer to the real load profile (RLP) of an automatic meter reading (AMR) customer that has a similar load pattern.…”

Section: Introductionmentioning

confidence: 99%

Repeated Clustering to Improve the Discrimination of Typical Daily Load Profile

Kim¹,

Ko²,

Song³

et al. 2012

Journal of Electrical Engineering and Technology

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-The customer load profile clustering method is used to make the TDLP (Typical Daily Load Profile) to estimate the quarter hourly load profile of non-AMR (Automatic Meter Reading) customers. This study examines how the repeated clustering method improves the ability to discriminate among the TDLPs of each cluster. The k-means algorithm is a well-known clustering technology in data mining. Repeated clustering groups the cluster into sub-clusters with the k-means algorithm and chooses the sub-cluster that has the maximum average error and repeats clustering until the final cluster count is satisfied.

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“…FCM has been occupied in load profiling tasks as a sole algorithm [53,55,[57][58][59][60][61] or as a part of a comparison among many algorithms [48,50,51,[62][63][64][65][66]. In [48], the utilized data set includes 234 non-residential consumers.…”

mentioning

confidence: 99%

“…This fact is observed in all validity indicators used in the paper. Gerbec et al [60] apply the same concept as [58]. The FCM generates the load profiles that are used as part of the training vector of the probabilistic neural network.…”

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confidence: 99%

An Improved Fuzzy C-Means Algorithm for the Implementation of Demand Side Management Measures

et al. 2017

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Abstract:Load profiling refers to a procedure that leads to the formulation of daily load curves and consumer classes regarding the similarity of the curve shapes. This procedure incorporates a set of unsupervised machine learning algorithms. While many crisp clustering algorithms have been proposed for grouping load curves into clusters, only one soft clustering algorithm is utilized for the aforementioned purpose, namely the Fuzzy C-Means (FCM) algorithm. Since the benefits of soft clustering are demonstrated in a variety of applications, the potential of introducing a novel modification of the FCM in the electricity consumer clustering process is examined. Additionally, this paper proposes a novel Demand Side Management (DSM) strategy for load management of consumers that are eligible for the implementation of Real-Time Pricing (RTP) schemes. The DSM strategy is formulated as a constrained optimization problem that can be easily solved and therefore, making it a useful tool for retailers' decision-making framework in competitive electricity markets.

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Allocation of the Load Profiles to Consumers Using Probabilistic Neural Networks

Cited by 136 publications

References 13 publications

A Mobile-based Platform for Big Load Profiles Data Analytics in Non-Advanced Metering Infrastructures

A Mobile-based Platform for Big Load Profiles Data Analytics in Non-Advanced Metering Infrastructures

Repeated Clustering to Improve the Discrimination of Typical Daily Load Profile

An Improved Fuzzy C-Means Algorithm for the Implementation of Demand Side Management Measures

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