Novel Approach towards Dynamic Discovery of the Electricity Consumption in Large Data Sets

iJournals: International Journal of Software & Hardware Research in Engineering ISSN-2347-4890 Volume 6 Issue 10 October, 2018

Novel Approach towards Dynamic Discovery of the Electricity Consumption in Large Data Sets Author: G. Karthick1; Dr. R. Saminathan2 Affiliation: Research Scholar, Department of Computer Science & Engineering, Annamalai University, Annamalainagar – 608 002, Tamil Nadu, India1; Assistant Professor, Department of Computer Science & Engineering, Annamalai University, Annamalainagar – 608 002, Tamil Nadu, India2 Email: karthickaucse@gmail.com1; samiaucse@yahoo.com2 ABSTRACT In a competitive retail market, large volumes of smart meter data provide opportunities for Load Serving Entities (LSEs) to enhance their knowledge of customers' electricity consumption behaviors via load profiling. Instead of focusing on the shape of the load curves, this paper proposes a novel approach for clustering of electricity consumption behavior dynamics, where “dynamics” refer to transitions and relations between consumption behaviors, or rather consumption levels, in adjacent periods. First, for each individual customer, Symbolic Aggregate Approximation (SAX) is performed to reduce the scale of the data set, and time-based Markov model is applied to model the dynamic of electricity consumption, transforming the large data set of load curves to several state transition matrixes. Second, a clustering technique using k-means is primarily carried out to obtain the typical dynamics of consumption behavior, with the difference between any two consumption patterns measured and to classify the customers into several clusters. To tackle the challenges of big data, the k-means technique is integrated into a divide-and-conquer approach toward big data applications. A numerical case verifies the effectiveness of the proposed models and approaches.

Keywords: SAX, LSE, AMI, CDI, ToU, MIA, DWT, HMM, DFT, CCA, K-Means

1. INTRODUCTION Countries around the world have set aggressive goals for the restructuring of monopolistic power system towards liberalized markets especially on the demand side. In a competitive retail market, Load Serving

Entities (LSEs) will be developed in great numbers.

Having a better understanding of electricity consumption patterns and realizing personalized power managements are effective ways to enhance the competitiveness of LSEs. Meanwhile, smart grids have been revolutionizing the electrical generation and consumption through a two-way flow of power and information. As an important information source from the demand side, Advanced Metering Infrastructure (AMI), has gained increasing popularity worldwide; AMI allows LSEs to obtain electricity consumption data at high frequency, e.g., minutes to hours. Large volumes of electricity consumption data reveal information of customers that can potentially be used

by LSEs to manage their generation and demand resources efficiently and provide personalized service.

Load profiling, which refers to electricity consumption behaviors of customers over a specific period, e.g., one day, can help LSEs understand how electricity is actually used for different customers and obtain the customers’ load profiles or load patterns.

Load profiling plays a vital role in the Time of Use (ToU) tariff design, nodal or customer scale load forecasting, demand response and energy efficiency targeting, and Non-Technical Loss (NTL) detection.

The core of load profiling is clustering which can be classified into two categories: direct clustering and indirect clustering. Direct clustering means that clustering methods are applied directly to load data.

Heretofore, there are a large number of clustering techniques that are widely studied, including k-means, fuzzy k-means, hierarchical clustering, SelfOrganizing Maps (SOM), support vector clustering, subspace clustering, ant colony clustering and etc. The

G. Karthick; Dr. R. Saminathan, vol 6 Issue 10, pp 39-48, October 2018

iJournals: International Journal of Software & Hardware Research in Engineering ISSN-2347-4890 Volume 6 Issue 10 October, 2018 performance of each clustering technique could be evaluated and quantified using various criteria, including the Clustering Dispersion Indicator (CDI), the Scatter Index (SI), the Davies-Bouldin Index (DBI), and the Mean Index Adequacy (MIA).

The deluge of electricity consumption data with the widespread and high-frequency collection of smart meters introduces great challenges for data storage, communication and analysis. In this context, dimension reduction methods can be effectively applied to reduce the size of the load data before clustering, which is defined as indirect clustering. Such clustering can be categorized into two sub-categories, Feature Extraction-based Clustering and Time Seriesbased Clustering. Feature extraction which transforms the data in the high-dimensional space into a space of fewer dimensions, is often used to reduce the scale of the input data. Principal Component Analysis (PCA) is a frequently used linear dimension reduction method.

It tries to retain most of the covariance of the data features with the fewest artificial variables. Some nonlinear dimension reduction methods including Sammon maps, Curvilinear Component Analysis (CCA), and deep learning have also been applied to electricity consumption data. Moreover, as electricity consumption data are essentially a time series. A variety of mature analytical methods such as Discrete Fourier Transform (DFT), Discrete Wavelet Transforms (DWT) Symbolic Aggregate Approximation (SAX), and the Hidden Markov Model (HMM) have been discussed in the literature. These methods are capable of reducing the dimensionality of time series and of maintaining some of the original character of the electrical consumption data. In a competitive retail market, large volumes of smart meter data provide opportunities for Load Serving Entities (LSEs) to enhance their knowledge of customers' electricity consumption behaviors via load profiling. By considering the high frequency and dimensionality of the data contained in the load curves, data sets in the multi-petabyte range will be analyzed.

In load profiling mainly focus on individual large industrial/commercial customer, medium or low voltage feeder, or a combination of small customers, load profiles of which shows much more regularity. It should be noted that although these dynamic characteristics are always “deluged” in a combination of customers, they could be described by several typical load patterns. However, with regard to residential customers, at least two new challenges will be faced. One challenge is the high variety and variability of the load patterns.

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As indicated by there are clear differences in the electricity consumption patterns of the two residents. Peak loads have different amplitudes and occur at different times of day, for example. Electricity consumption patterns also vary on a daily basis even for the same customer. In this case, several typical daily load patterns are not fine enough to reveal the actual consumption behaviors. The daily profile should be decomposed into more fine-grained fragments, which are dynamically changed and identified.

Moreover, as the consumption behavior of a specific customer is essentially a state-dependent, stochastic process, it is important to explore the dynamic characteristics, e.g., switching and maintaining, of the consumption states and the corresponding probabilities.

To tackle the challenges of big data, the help of aggregation and clustering technique is integrated into a divide-and-conquer approach toward this large data’s to identify behavior pattern and predict the need of power in future. This assumption is reasonable as various electricity consumption behaviors would last for different periods of time before being capable of change, as could be abstracted from historical performances. Profiling of the dynamics could provide useful information for understanding the consumption patterns of customers, forecasting the consumption trends in short time periods, and identifying the potential demand response targets. Moreover, this approach formulates the large data set of load curves as several state transition matrixes, greatly reducing the dimensionality and scale. Finally, the potential applications of the proposed method to demand response targeting, abnormal consumption behavior detecting and load forecasting are analyzed and discussed. Especially, entropy analysis is conducted based on the clustering results to evaluate the variability of consumption behavior for each cluster, which can be used to quantify the potential of pricebased and incentive-based demand response.

2. LITERATURE SURVEY Load profiling, which refers to electricity consumption behaviors of customers over a specific period, e.g., one day, can help LSEs understand how electricity is actually used for different customers and obtain the customers’ load profiles or load patterns [8].

Load profiling plays a vital role in the Time of Use (ToU) tariff design [3], nodal or customer scale load forecasting [5], demand response and energy efficiency targeting [6], and Non-Technical Loss (NTL) detection [7].

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iJournals: International Journal of Software & Hardware Research in Engineering ISSN-2347-4890 Volume 6 Issue 10 October, 2018 The core of load profiling is clustering which can be classified into two categories: Direct Clustering and Indirect Clustering [16]. Direct clustering means that clustering methods are applied directly to load data. Heretofore, there are a large number of clustering techniques that are widely studied, including k-means [9], fuzzy k-means [10], hierarchical clustering [11], Self-Organizing Maps (SOM) [12], Support Vector Clustering [13], Subspace Clustering [14], ant Colony Clustering [15] and etc. The performance of each clustering technique could be evaluated and quantified using various criteria, including the Clustering Dispersion Indicator (CDI), the Scatter Index (SI), the Davies-Bouldin Index (DBI), and the Mean Index Adequacy (MIA) [16].

The deluge of electricity consumption data with the widespread and high-frequency collection of smart meters introduces great challenges for data storage, communication and analysis. In this context, dimension reduction methods can be effectively applied to reduce the size of the load data before clustering, which is defined as indirect clustering. Such clustering can be categorized into two sub-categories, feature extraction-based clustering and time seriesbased clustering. Feature extraction which transforms the data in the high-dimensional space into a space of fewer dimensions [17], is often used to reduce the scale of the input data. Principal Component Analysis (PCA) [18] [19] is a frequently used linear dimension reduction method. It tries to retain most of the covariance of the data features with the fewest artificial variables. Some nonlinear dimension reduction methods including Sammon maps, Curvilinear Component Analysis (CCA) [20], and deep learning [21] have also been applied to electricity consumption data.

Moreover, as electricity consumption data are essentially a time series. A variety of mature analytical methods such as Discrete Fourier Transform (DFT) [22] [23], Discrete Wavelet Transforms (DWT) [24], Symbolic Aggregate Approximation (SAX) [25], and the Hidden Markov Model (HMM) [26] have been discussed in the literature.

In addition to the Markov model, this work tries to address the "data deluge" issue in three other ways. First, applying SAX to transform the load curves into a symbolic string to reduce the storage space and ease the communication traffic between smart meters and data centers. Second, a recently reported effective Clustering technique by Fast Search and Find of Density Peaks (CFSFDP) is first utilized to profile the electricity consumption behaviors, which has the

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advantages of low time complexity and robustness to noise points [28]. The dynamics of electricity consumptions are described by the differences between every two consumption patterns, as measured by the Kullback–Liebler (K-L) distance [29]. Third, to tackle the challenges of big and dispersed data, the CFSFDP technique is integrated into a divide-and-conquer approach to further improve the efficiency of data processing, where adaptive k-means is applied to obtain the representative customers at the local sites and a modified CFSFDP method is performed at the global sites. The approach could be further applied toward big data applications.

Machine Learning (ML) has been attracting renewed attention with the emergence of Big Data as it has been seen as a way of extracting value from data.

ML platforms for Big Data started with disk-based approaches such as Apache Mahout [7] which inherits disk orientation from the underlying Hadoop architecture. Because disk access is slow, new memory-based approaches have been developed.

Apache Spark and Oxdata H2O are examples of memory-based platforms, and even Mahout machine learning algorithms are transitioning to these platforms. Tom white et al. [1] reviewed inmemory Big Data management and processing. They distinguished two types of in-memory systems: batchoriented systems such as Spark and H2O, and real time or stream processing systems such as Storm. The systems relevant to energy consumption prediction primarily belong to the batch category.

N.Mahmoudi-Kohan et al. [6] reviewed energy efficient machine learning approaches and new approaches with reduced memory requirements. They saw local learning as one of the key mechanisms for machine learning[27] with Big Data because of its ability to reduce computation cost. Our work takes advantage of parallel and distributed processing and performs dimensionality reduction, but only after the training data has been partitioned into clusters.

3. BIG DATA AND HADOOP Big data means really a big data; it is a collection of large datasets that cannot be processed using traditional computing techniques. Big data is not merely a data; rather it has become a complete subject, which involves various tools, techniques and frameworks. Big data involves the data produced by different devices and applications. Big data technologies are important in providing more accurate analysis, which may lead to more concrete decisionmaking resulting in greater operational efficiencies,

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iJournals: International Journal of Software & Hardware Research in Engineering ISSN-2347-4890 Volume 6 Issue 10 October, 2018 cost reductions, and reduced risks for the business. To hardness the power of big data, you would require an infrastructure that can manage and process huge volumes of structured and unstructured data in realtime and can protect data privacy and security. In this approach, an enterprise will have a computer to store and process big data. In Fig.1 data will be stored in an RDBMS like Oracle Database, MS SQL Server or DB2 and sophisticated software’s can be written to interact with the database, process the required data and present it to the users for analysis purpose.

Centralised System

Relational Data base

Commodity Hardware

Commodity Hardware

Centalised System

User

Commodity Hardware

Fig 2: Stored data in Commodity Hardware

Big Data

Fig 1: Data Storage in RDBMS This approach works well where less volume of data that can be accommodated by standard database servers, or up to the limit of the processor which is processing the data. But when it comes to dealing with huge amounts of data, it is really a tedious task to process such data through a traditional database server. Google solved this problem using an algorithm called MapReduce. This algorithm divides the task into small parts and assigns those parts to many computers connected over the network, and collects the results to form the final result dataset as shown in the Fig.2.

3.1 Hadoop: Doug Cutting, Mike Cafarella and team took the solution provided by Google and started an Open Source Project called HADOOP in 2005 and Doug named it after his son's toy elephant. Now Apache Hadoop is a registered trademark of the Apache Software Foundation. Hadoop runs applications using the MapReduce algorithm, where the data is processed in parallel on different CPU nodes. In short, Hadoop framework is capable enough to develop applications capable of running on clusters of computers and they could perform complete statistical analysis for huge amounts of data as shown in Fig.3.

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MR

Media Data

MR

Media Data

MR

Media Data

Fig 3: Process of MapReduce Hadoop is an Apache open source framework written in java that allows distributed processing of large datasets across clusters of computers using simple programming models. A Hadoop frame-worked application works in an environment that provides distributed storage and computation across clusters of computers. Hadoop is designed to scale up from single server to thousands of machines, each offering local computation and storage.

3.2 Hadoop Architecture: Hadoop framework includes following four modules namely Hadoop Common, Hadoop YARN, Hadoop Distributed File System (HDFS) and Hadoop MapReduce as shown in Fig. 4.

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iJournals: International Journal of Software & Hardware Research in Engineering ISSN-2347-4890 Volume 6 Issue 10 October, 2018

Hadoop MapReduce (Distributed Computation)

HDFS (Distributed Storage)

YARN Framework

Common Utilities

(GFS) and provides a distributed file system that is designed to run on large clusters (thousands of computers) of small computer machines in a reliable, fault-tolerant manner. HDFS uses a master/slave architecture where master consists of a singleNameNode that manages the file system metadata and one or more slaveDataNodes that store the actual data. A file in an HDFS namespace is split into several blocks and those blocks are stored in a set of DataNodes. The NameNode determines the mapping of blocks to the DataNodes. The DataNodes takes care of read and write operation with the file system. They also take care of block creation, deletion and replication based on instruction given by NameNode. HDFS provides a shell like any other file system and a list of commands are available to interact with the file system. These shell commands will be covered in a separate chapter along with appropriate examples.

3.5 Hadoop YARN Fig 4: Distributed Computation and Storage with Cluster Management Technology (YARN)

This is a framework for job scheduling and cluster resource management.

3.3 Hadoop MapReduce

3.6 Hadoop Common Utilities

Hadoop MapReduce is a software framework for easily writing applications which process big amounts of data in-parallel on large clusters (thousands of nodes) of commodity hardware in a reliable, faulttolerant manner. The framework takes care of scheduling tasks, monitoring them and re-executes the failed tasks. The MapReduce framework consists of a single master JobTracker and one slave TaskTracker per cluster-node. The master is responsible for resource management, tracking resource consumption/availability and scheduling the jobs component tasks on the slaves, monitoring them and re-executing the failed tasks. The slaves TaskTracker execute the tasks as directed by the master and provide task-status information to the master periodically. The JobTracker is a single point of failure for the Hadoop MapReduce service which means if JobTracker goes down, all running jobs are halted.

These are Java libraries and utilities required by other Hadoop modules. These libraries provide file system and OS level abstractions and contains the necessary Java files and scripts required to start Hadoop.

3.4 Hadoop Distributed File System Hadoop can work directly with any mountable distributed file system such as Local FS, HFTP FS, S3, FS, and others, but the most common file system used by Hadoop is the Hadoop Distributed File System (HDFS) [2]. The Hadoop Distributed File System (HDFS) is based on the Google File System

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4. PROPOSED WORK The proposed methodology for the dynamic discovery of the electricity consumption can be divided into six stages, as shown in Fig 5. The first stage conducts some load data preparations, including data cleaning and load curve normalization. The second stage reduces the dimensionality of the load profiles using SAX. The third stage formulates the electricity consumption dynamics of each individual customer utilizing time-based Markov model. The K-L distance is applied to measure the difference between any two Markov models to obtain the distance matrix in the fourth stage. The fifth stage performs a modified CFSFDP clustering algorithm to discover the typical dynamics of electricity consumption. Finally, the results of the analysis of the demand response targeting are obtained in the sixth stage.

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iJournals: International Journal of Software & Hardware Research in Engineering ISSN-2347-4890 Volume 6 Issue 10 October, 2018 Start

Data Preparation

For Customer 1

For Customer 2

For Customer 3

Step A

SAX

SAX

SAX

Step B

Markov Model

Markov Model

Markov Model

Step C

K-L Distance Calculation to Obtain the Dissimililarity Matrix

Step D

Cluster Customers into Groups of Similar Dynamic Characters

Analysis for Demand Response Targeting

Fig 5: Overall Architecture of Consumption Identification Framework The details of the first five stages will be introduced in the following, and the demand response targeting analysis. The proposed system is divided into five modules namely Data Normalization, Dimension Reduction and loading Data into Hadoop framework, Energy Consumption calculation, k-means clustering approach and Distributed Clustering.

4.1 Data Normalization Data preparations including data cleaning are performed using normalization technique. To make the load profiles comparable, the normalization process transforms the consumption data of arbitrary value to the range of, as shown in (1).

x i - x min x max - x min

(1)

Where, xi and xi’ and denote the actual and normalized electricity consumption at time i; It should be noted that the normalization is performed on a daily basis instead of over entire periods.

4.2 Dimension Reduction and loading Data into Hadoop framework For dimension reduction and to predict the trend or level of electricity consumption for each customer, we may make full use of their past and

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n  f ijt if  f kjt  0  n k 1    f kjt  k 1  0 otherwise

Where,

End

x i' 

present states. If the future consumption level or state depends only on the present state, it is called a Markov property and can be modeled by a Markov chain.

Various Markov models have been applied to load forecasting. For a symbolic string with N symbols, discrete Markov model with N corresponding states can be applied to model the dynamic characteristics of their consumption levels. However, customers have different dynamic characteristics at different periods for their regular routines every day. Therefore, timebased Markov model is applied to formulate the characteristics. For each adjunct period, a Markov chain can be modeled. Then, the one-step transition number matrix at period can be calculated.

f 11t  t f F   21     t f n1

f 12t f 22t  f n2t

   

 P̂11t f 1nt    f 2nt  t P̂21t , P̂     t t  f nn  P̂n1

P̂12t P̂22t  P̂n2t

   

P̂1nt   P̂2nt  ,   P̂nnt 

From the above equation all the input data’s are converted into the matrix formation and the values are fixed for easily processing the information. Then the input data’s are passed into the Hadoop framework for the parallel processing.

4.3 Energy Consumption Calculation The data’s are collected for individual customers are handled separately. Divide the big data set into k parts, each marked as Li. Note that the data on one distributed site can be further partitioned to make the size of the data sets on each site more even.

An adaptive k-means method is performed for each individual part to obtain a certain number of cluster centers. Each cluster center can represent all the objects belonging to this cluster with a small error. All these cluster centers of Li are selected as the representative objects Mi, which are defined as a local model. A modified Energy Consumption method is applied to all the representative objects (local models) that are centralized and gathered to classify them into several groups R, which are defined as a global model.

Then, according to the final clustering result, the cluster label of each local site would be updated.

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iJournals: International Journal of Software & Hardware Research in Engineering ISSN-2347-4890 Volume 6 Issue 10 October, 2018

4.4 k-means clustering Approach

4.5 Distributed Clustering

A set of clustering centers will be obtained by k-means, where the sum of the squared distances between each object is minimized [4]. This is called Vector Quantization (VQ). We try to establish a local model by finding the “code book” that guarantees that the distortion of each object by VQ satisfies the threshold condition according to Traditional k-means needs a given number of centers, which makes it difficult to guarantee that (11) holds. The Fig 6 shows the detailed procedures of the adaptive k-means method. The distortion threshold varies depending on different needs. Smaller threshold corresponds to higher clustering accuracy and larger number of local representative objects, and vice versa. The value of Kmin and Kmax can be determined according to the data transmission limits. Especially, if ensuring certain precision is the priority, the adaptive k-means can start from Kmin=2 until the (11) holds by setting the value of Kmax to positive infinity. The proposed adaptive kmeans distinguishes from traditional k-means in at least four aspects:

To verify the proposed distributed clustering algorithm, we divide the 6445 customers into three equal parts. Then, the distortion threshold θ is carefully selected for the adaptive k-means method, as a larger threshold leads to poor accuracy, whereas a smaller one leads to little compression. The CR is defined as the ratio between the volume of the compressed data and the volume of the original data. Especially, the compressed data refers to local models obtained by adaptive k-means, and the original data.

5. RESULTS AND DISCUSSION The Electricity Consumption analysis is carried out through Hadoop Framework and clustering of similar customers is carried out through the Rprogramming for visualization of similar customers.

Fig. 7 represents the present of all the Hadoop daemons are started and working properly. The main components are Name node and Data Node for processing map-reduce operation.

First, for adaptive k-means, the number of clusters adjusts dynamically depending on whether the distortion threshold condition is satisfied, in contrast to traditional k-means, where it should be predetermined.

Start

Parameter setting Kmin, Kmax, K=Kmin

Fig 7: Hadoop Daemons Fig. 8 represents the directory creation inside Hadoop directory. Once the Directory is created the input electricity Consumption.csv is loaded into the Hadoop directory to perform map-reduce operation.

K-means clustering

Count the number of clusters violating the threshold Nv

Yes Nv =0?

No K = K + Nv

Yes K> Kmax ?

No 2-means clustering for each violating cluster

Update the clusters

End

Fig 6: k-means clustering Approach

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Fig 8: Directory Creation and Loading Inputs Fig. 9 shows the directory once we successfully created in Hadoop framework and data are loaded inside the directory.

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iJournals: International Journal of Software & Hardware Research in Engineering ISSN-2347-4890 Volume 6 Issue 10 October, 2018

Fig 9: Electricity Consumption directory in Hadoop

Fig 12: Overall Electricity Consumption Behavior

Fig. 10 represents the map-reducer process starts successfully and gives the complete input and output directory and submission of jar file into the Hadoop directory.

Fig. 13 represents the k-means clustering approach for identifying the consumption of electricity Silicon Valley customers over the year of 1990-2005.

From the clustering we identified that South California and Bear Valley electric Customers are consuming more than 80000 watts per year.

Fig 10: MapReduce Process Begins Fig. 11 represents the output directory inside the hadoop framework when map-reduce successfully complete and output is written inside the directory titled part-r-00000.

Fig 13: k-means Clustering Approach

6. CONCLUSION & FUTURE WORK Fig 11: Output Directory in Hadoop Fig. 12 shows the overall electricity consumption behavior pattern is identified for every year by taking account with different customers.

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In this work, a novel approach for the clustering of electricity consumption behavior dynamics toward large data sets has been proposed. A density-based clustering technique is performed to discover the typical dynamics of electricity consumption and segment customers into different groups. Finally, with the year and overall energy

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iJournals: International Journal of Software & Hardware Research in Engineering ISSN-2347-4890 Volume 6 Issue 10 October, 2018 consumption for every individual customers are identified and the result of the dynamic clustering to identify the demand response potential of each group’s customers. Limited by the data sets, the influence of external factors like temperature, day type, and economy on the electricity consumption is not considered in depth in this project. Future works will focus on feature extraction and data mining techniques combining electricity consumption with external factors.

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