Classification and Clustering of Electricity Demand Patterns in Industrial Parks

Hernández, Leonardo; Baladrón, Carlos; Aguiar, Javier M.; Carro, Belén; Sánchez-Esguevillas, Antonio

doi:10.3390/en5125215

Cited by 98 publications

(56 citation statements)

References 25 publications

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“…Those based on Artificial Neural Networks (ANNs) stand out within the non-linear. They may be of the supervised type Multi-Layer Perception (MLP) (Garcia-Ascanio and Matt [9]), not supervised as Self-Organizing Map (SOM) (Marin et al [10]), or combination of them in several stages (Hernández et al [11]). As [8] present, the models based on ANNs require a less intellectual effort on the part of researchers, since they do not need to obtain the complicated linear equations that attempt to relate the potential nonlinearities associated with the problem of demand prediction; the complexity of the models based on ANNs is in its optimization, with the necessity of having tests that vary the internal topology of the network (number of neurons in the hidden layer and learning function).…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network for Short-Term Load Forecasting in Distribution Systems

et al. 2014

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Abstract:The new paradigms and latest developments in the Electrical Grid are based on the introduction of distributed intelligence at several stages of its physical layer, giving birth to concepts such as Smart Grids, Virtual Power Plants, microgrids, Smart Buildings and Smart Environments. Distributed Generation (DG) is a philosophy in which energy is no longer produced exclusively in huge centralized plants, but also in smaller premises which take advantage of local conditions in order to minimize transmission losses and optimize production and consumption. This represents a new opportunity for renewable energy, because small elements such as solar panels and wind turbines are expected to be scattered along the grid, feeding local installations or selling energy to the grid depending OPEN ACCESSEnergies 2014, 7 1577 on their local generation/consumption conditions. The introduction of these highly dynamic elements will lead to a substantial change in the curves of demanded energy. The aim of this paper is to apply Short-Term Load Forecasting (STLF) in microgrid environments with curves and similar behaviours, using two different data sets: the first one packing electricity consumption information during four years and six months in a microgrid along with calendar data, while the second one will be just four months of the previous parameters along with the solar radiation from the site. For the first set of data different STLF models will be discussed, studying the effect of each variable, in order to identify the best one. That model will be employed with the second set of data, in order to make a comparison with a new model that takes into account the solar radiation, since the photovoltaic installations of the microgrid will cause the power demand to fluctuate depending on the solar radiation.

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Section: Introductionmentioning

confidence: 99%

Artificial Neural Network for Short-Term Load Forecasting in Distribution Systems

et al. 2014

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“…In their work, energy consumption graphs were interpreted as a mixture of Gaussian distributions, and the distance between load profiles was defined by the K-L divergence of the Gaussian mixture distributions. In the literature, other various machine learning techniques such as the self-organizing map (SOM), neural network, support vector clustering, dynamic time warping (DTW), and latent Dirichlet allocation (LDA) have also been applied to cluster electricity customers [9][10][11][12][13][14][15][16]. Furthermore, the transformation of input data to other domains such as frequency domain has been proposed in the literature [17].…”

Section: Introductionmentioning

confidence: 99%

Electricity Customer Clustering Following Experts’ Principle for Demand Response Applications

Kang

Lee

2015

Energies

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Abstract:The clustering of electricity customers might have an effective meaning if, and only if, it is verified by domain experts. Most of the previous studies on customer clustering, however, do not consider real applications, but only the structure of clusters. Therefore, there is no guarantee that the clustering results are applicable to real domains. In other words, the results might not coincide with those of domain experts. In this paper, we focus on formulating clusters that are applicable to real applications based on domain expert knowledge. More specifically, we try to define a distance between customers that generates clusters that are applicable to demand response applications. First, the k-sliding distance, which is a new distance between two electricity customers, is proposed for customer clustering. The effect of k-sliding distance is verified by expert knowledge. Second, a genetic programming framework is proposed to automatically determine a more improved distance measure. The distance measure generated by our framework can be considered as a reflection of the clustering principles of domain experts. The results of the genetic programming demonstrate the possibility of deriving clustering principles.

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“…The usual stated applications range from the design and simulation of demand side management strategies (DSM) [4], [5], load forecasting [6], [7], tariff setting [8,9,10], marketing and bad data detection. The clustering methods found to be used are mostly the K-means algorithm [5,11,12,13,14]. Fuzzy clustering [15] has shown promise in the field.…”

Section: Related Workmentioning

confidence: 99%

Fuzzy clustering and prediction of electricity demand based on household characteristics

Viegas¹,

Vieira²,

Sousa³

2015

Advances in Intelligent Systems Research

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The electricity market has been significantly changing in the last decade. The deployment of smart meters is enabling the logging of huge amounts of data relating to the operations of utilities with the potential of being translated into valuable knowledge on the behaviour of consumers. This work proposes a methodology for predicting the typical daily load profile of electricity usage based on static data using fuzzy clustering and modelling. The methodology intends to: (1) determine consumer segments based on the metering data using the fuzzy c-means clustering algorithm, and (2) develop Takagi-Sugeno fuzzy models in order to predict the demand profile of the consumers.

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Classification and Clustering of Electricity Demand Patterns in Industrial Parks

Cited by 98 publications

References 25 publications

Artificial Neural Network for Short-Term Load Forecasting in Distribution Systems

Artificial Neural Network for Short-Term Load Forecasting in Distribution Systems

Electricity Customer Clustering Following Experts’ Principle for Demand Response Applications

Fuzzy clustering and prediction of electricity demand based on household characteristics

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