Classification for consumption data in smart grid based on forecasting time series

Tornai, Kálmán; Kovács, Lóránt; Oláh, András; Drenyovszki, Rajmund; Pintér, István; Tisza, Dávid; Levendovszky, János

doi:10.1016/j.epsr.2016.07.018

Cited by 21 publications

(11 citation statements)

References 22 publications

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“…While dealing with time series data, efficiency and effectiveness are the main targets of representation methods and similarity measures [98]. Tornai et al [99] argue that the distance between two sequences as a measurement plays an important role in the quality of clustering and classification algorithms. The accuracy of such algorithms can be significantly impacted by the choice of similarity measures.…”

Section: A Raw Data Similaritymentioning

confidence: 99%

“…Discrete Wavelet Transform (DWT) has also been used as a technique to transform original time series and obtain low-dimensional features that efficiently represent the original time series data [99], [125]. Chan and Fu [126] use Haar Wavelet Transform for time series indexing, which shows the technique's effectiveness with regards to the decomposition and reconstruction of time series.…”

Section: B Feature Extractionmentioning

confidence: 99%

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Clustering and Classification for Time Series Data in Visual Analytics: A Survey

et al. 2019

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Visual analytics for time series data has received a considerable amount of attention. Different approaches have been developed to understand the characteristics of the data and obtain meaningful statistics in order to explore the underlying processes, identify and estimate trends, make decisions and predict the future. The machine learning and visualization areas share a focus on extracting information from data. In this paper, we consider not only automatic methods but also interactive exploration. The ability to embed efficient machine learning techniques (clustering and classification) in interactive visualization systems is highly desirable in order to gain the most from both humans and computers. We present a literature review of some of the most important publications in the field and classify over 60 published papers from six different perspectives. This review intends to clarify the major concepts with which clustering or classification algorithms are used in visual analytics for time series data and provide a valuable guide for both new researchers and experts in the emerging field of integrating machine learning techniques into visual analytics.

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Section: A Raw Data Similaritymentioning

confidence: 99%

Section: B Feature Extractionmentioning

confidence: 99%

Clustering and Classification for Time Series Data in Visual Analytics: A Survey

et al. 2019

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“…Saab, et al [23] determined energy consumption of Lebanon with univariate model. Tornai, et al [24] predicted power consumption in Hungary using smart grid. Pao [25] forecasted energy consumption in Taiwan by a hybrid nonlinear model combining a linear model and an artificial neural network.…”

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confidence: 99%

Forecasting Energy Consumption of Turkey by Arima Model

Öztürk

2018

Journal of Asian Scientific Research

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Article HistoryReceived: 5 December 2017 Revised: 3 January 2018 Accepted: 9 January 2018 Published: 15 January 2018 KeywordsForecasting ARIMA Renewable energy consumption Coal consumption Oil consumption Natural gas consumption Turkey.Forecasting energy consumption has an important role in planning energy strategies for both policy makers and related organizations in any country. In this study, coal, oil, natural gas, renewable and total energy consumption data for 1970-2015 is used to forecast energy consumption of Turkey for the next 25 years, using autoregressive integrated moving average (ARIMA) model. The ARIMA models are determined to be ARIMA(1, 1, 1) for coal consumption, ARIMA (0, 1, 0) for oil consumption, ARIMA (0, 0, 0) for natural gas consumption, ARIMA (1, 1, 0) for renewable energy consumption and ARIMA (0, 1, 2) for total energy consumption. The results indicate that Turkey's energy consumption will continue to increase by the end of 2040. Consumption of coal, oil, natural gas, renewable energy and total energy will continue to increase at an annual average rate of 4.87 %, 3.92 %, 4.39 %, 1.64 % and 4.20 %, respectively in the next 25 years. Contribution/ Originality:This study is one of very few studies, which have forecasted energy consumption of Turkey. Moreover, the forecasted results provide a reference for Turkey to develop energy strategy to solve prevenient energy supply shortage in the future.

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“…Deep Time-Series Clustering data, efficiency and effectiveness are the main targets of representation methods and similarity measures[366]. Tornai et al[367] argue that the distance between two sequences as a measurement plays an important role in the quality of clustering algorithms. The accuracy of such algorithms can be significantly impacted by the choice of similarity measures.…”

mentioning

confidence: 99%

“…It has been used to transform original time-series data into low-dimensionality time-frequency characteristics and index them to obtain an effective similarity search[390].DFT is used to perform dimensionality reduction and extract features into an index used for similarity searching. This technique is continually under improvement and some of its limitations have been overcome[377,391,392].Discrete Wavelet Transform (DWT): has also been used as a technique to transform original time-series and obtain low-dimensional features that efficiently represent the original time-series data[367,393]. Chan et al[394] use Haar Wavelet Transform for time-series indexing, which shows the technique's effectiveness with regard to the decomposition and reconstruction of time-series.…”

mentioning

confidence: 99%

Deep Clustering and Deep Network Compression

Alqahtani¹

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The use of deep learning has grown increasingly in recent years, thereby becoming a much-discussed topic across a diverse range of fields, especially in computer vision, text mining, and speech recognition. Deep learning methods have proven to be robust in representation learning and attained extraordinary achievement. Their success is primarily due to the ability of deep learning to discover and automatically learn feature representations by mapping input data into abstract and composite representations in a latent space. Deep learning’s ability to deal with high-level representations from data has inspired us to make use of learned representations, aiming to enhance unsupervised clustering and evaluate the characteristic strength of internal representations to compress and accelerate deep neural networks.Traditional clustering algorithms attain a limited performance as the dimensionality in-creases. Therefore, the ability to extract high-level representations provides beneficial components that can support such clustering algorithms. In this work, we first present DeepCluster, a clustering approach embedded in a deep convolutional auto-encoder. We introduce two clustering methods, namely DCAE-Kmeans and DCAE-GMM. The DeepCluster allows for data points to be grouped into their identical cluster, in the latent space, in a joint-cost function by simultaneously optimizing the clustering objective and the DCAE objective, producing stable representations, which is appropriate for the clustering process. Both qualitative and quantitative evaluations of proposed methods are reported, showing the efficiency of deep clustering on several public datasets in comparison to the previous state-of-the-art methods.Following this, we propose a new version of the DeepCluster model to include varying degrees of discriminative power. This introduces a mechanism which enables the imposition of regularization techniques and the involvement of a supervision component. The key idea of our approach is to distinguish the discriminatory power of numerous structures when searching for a compact structure to form robust clusters. The effectiveness of injecting various levels of discriminatory powers into the learning process is investigated alongside the exploration and analytical study of the discriminatory power obtained through the use of two discriminative attributes: data-driven discriminative attributes with the support of regularization techniques, and supervision discriminative attributes with the support of the supervision component. An evaluation is provided on four different datasets.The use of neural networks in various applications is accompanied by a dramatic increase in computational costs and memory requirements. Making use of the characteristic strength of learned representations, we propose an iterative pruning method that simultaneously identifies the critical neurons and prunes the model during training without involving any pre-training or fine-tuning procedures. We introduce a majority voting technique to compare the activation values among neurons and assign a voting score to evaluate their importance quantitatively. This mechanism effectively reduces model complexity by eliminating the less influential neurons and aims to determine a subset of the whole model that can represent the reference model with much fewer parameters within the training process. Empirically, we demonstrate that our pruning method is robust across various scenarios, including fully-connected networks (FCNs), sparsely-connected networks (SCNs), and Convolutional neural networks (CNNs), using two public datasets.Moreover, we also propose a novel framework to measure the importance of individual hidden units by computing a measure of relevance to identify the most critical filters and prune them to compress and accelerate CNNs. Unlike existing methods, we introduce the use of the activation of feature maps to detect valuable information and the essential semantic parts, with the aim of evaluating the importance of feature maps, inspired by novel neural network interpretability. A majority voting technique based on the degree of alignment between a se-mantic concept and individual hidden unit representations is utilized to evaluate feature maps’ importance quantitatively. We also propose a simple yet effective method to estimate new convolution kernels based on the remaining crucial channels to accomplish effective CNN compression. Experimental results show the effectiveness of our filter selection criteria, which outperforms the state-of-the-art baselines.To conclude, we present a comprehensive, detailed review of time-series data analysis, with emphasis on deep time-series clustering (DTSC), and a founding contribution to the area of applying deep clustering to time-series data by presenting the first case study in the context of movement behavior clustering utilizing the DeepCluster method. The results are promising, showing that the latent space encodes sufficient patterns to facilitate accurate clustering of movement behaviors. Finally, we identify state-of-the-art and present an outlook on this important field of DTSC from five important perspectives.

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Classification for consumption data in smart grid based on forecasting time series

Cited by 21 publications

References 22 publications

Clustering and Classification for Time Series Data in Visual Analytics: A Survey

Clustering and Classification for Time Series Data in Visual Analytics: A Survey

Forecasting Energy Consumption of Turkey by Arima Model

Deep Clustering and Deep Network Compression

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