scholarly journals Deep Learning Assisted Buildings Energy Consumption Profiling Using Smart Meter Data

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 873 ◽  
Author(s):  
Amin Ullah ◽  
Kilichbek Haydarov ◽  
Ijaz Ul Haq ◽  
Khan Muhammad ◽  
Seungmin Rho ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Clustering Algorithm ◽  
Energy Utilization ◽  
Self Organizing Map ◽  
Residential Areas ◽  
Energy Management Systems ◽  
Consumption Data ◽  
Som Clustering ◽  
Low Dimensional ◽  
High Level

The exponential growth in population and their overall reliance on the usage of electrical and electronic devices have increased the demand for energy production. It needs precise energy management systems that can forecast the usage of the consumers for future policymaking. Embedded smart sensors attached to electricity meters and home appliances enable power suppliers to effectively analyze the energy usage to generate and distribute electricity into residential areas based on their level of energy consumption. Therefore, this paper proposes a clustering-based analysis of energy consumption to categorize the consumers’ electricity usage into different levels. First, a deep autoencoder that transfers the low-dimensional energy consumption data to high-level representations was trained. Second, the high-level representations were fed into an adaptive self-organizing map (SOM) clustering algorithm. Afterward, the levels of electricity energy consumption were established by conducting the statistical analysis on the obtained clustered data. Finally, the results were visualized in graphs and calendar views, and the predicted levels of energy consumption were plotted over the city map, providing a compact overview to the providers for energy utilization analysis.

Contemporary Energy Management Systems and Future Prospects

2021 ◽  
pp. 1984-2013
Author(s):  
Amir Manzoor
Keyword(s):  
Energy Consumption ◽  
Energy Management ◽  
User Participation ◽  
Energy Utilization ◽  
Security And Privacy ◽  
Management Systems ◽  
Future Research ◽  
Energy Management Systems ◽  
Research Areas ◽  
Emerging Trends

The transformation of electric grid into smart grid has improved management of available resources and increased energy efficiency. Energy management systems (EMS) play an important role in enhancing user participation in control of energy management. Using such systems, consumers can obtain information about their energy consumption patterns and shape their energy consumption behaviors for efficient energy utilization. Contemporary EMS utilizes advanced analytics and ICT to provide consumers actionable feedback and control of energy management. These systems provide high availability, an easy-to-use user interface, security, and privacy. This chapter explores the contemporary EMS, their applications, classifications, standards, and frameworks. The chapter defines a set of requirements for EMS and provides feature comparison of various EMS. The chapter also discusses emerging trends and future research areas in EMS.

scholarly journals Visualizing text similarities from a graph-based SOM

2015 ◽  
Vol 14 (7) ◽  
pp. 5877-5886
Author(s):  
Khalid Kahloot ◽  
Mohammad A. Mikki ◽  
Akram A. ElKhatib
Keyword(s):  
Clustering Algorithm ◽  
Vector Space Model ◽  
Graph Model ◽  
Self Organizing Map ◽  
Text Data ◽  
Pagerank Algorithm ◽  
Space Model ◽  
As Graph ◽  
Som Clustering ◽  
Text Searching

Text in articles is based on expert opinion of a large number of people including the views of authors. These views are based on cultural or community aspects, which make extracting information from text very difficult. This paper introduced how to utilize the capabilities of a modified graph-based Self-Organizing Map (SOM) in showing text similarities. Text similarities are extracted from an article using Google's PageRank algorithm. Sentences from an input article are represented as graph model instead of vector space model. The resulted graph can be shown in a visual animation for eight famous graph algorithms execution with animation speed control.The resulted graph is used as an input to SOM. SOM clustering algorithm is used to construct knowledge from text data. We used a visual animation for eight famous graph methods with animation speed control and according to similarity measure; an adjustable number of most similar sentences are arranged in visual form. In addition, this paper presents a wide variety of text searching. We had compared our project with famous clustering and visualization project in term of purity, entropy and F measure. Our project showed accepted results and mostly superiority over other projects.

Contemporary Energy Management Systems and Future Prospects

2016 ◽  
pp. 265-292
Author(s):  
Amir Manzoor
Keyword(s):  
Energy Consumption ◽  
Energy Management ◽  
User Participation ◽  
Energy Utilization ◽  
Security And Privacy ◽  
Management Systems ◽  
Future Research ◽  
Energy Management Systems ◽  
Research Areas ◽  
Emerging Trends

The transformation of electric grid into smart grid has improved management of available resources and increased energy efficiency. Energy management systems (EMS) play an important role in enhancing user participation in control of energy management. Using such systems, consumers can obtain information about their energy consumption patterns and shape their energy consumption behaviors for efficient energy utilization. Contemporary EMS utilizes advanced analytics and ICT to provide consumers actionable feedback and control of energy management. These systems provide high availability, an easy-to-use user interface, security, and privacy. This chapter explores the contemporary EMS, their applications, classifications, standards, and frameworks. The chapter defines a set of requirements for EMS and provides feature comparison of various EMS. The chapter also discusses emerging trends and future research areas in EMS.

An Improving Algorithm Based on SOM Clustering and its Applications

2013 ◽  
Vol 655-657 ◽  
pp. 1000-1004
Author(s):  
Chen Guang Yan ◽  
Yu Jing Liu ◽  
Jin Hui Fan
Keyword(s):  
Data Clustering ◽  
Clustering Algorithm ◽  
Network Clustering ◽  
Self Organizing Map ◽  
Clustering Method ◽  
Map Algorithm ◽  
Som Neural Network ◽  
Simulation Results ◽  
Som Clustering ◽  
Selection Of

SOM (Self-organizing Map) algorithm is a clustering method basing on non-supervision condition. The paper introduces an improved algorithm based on SOM neural network clustering. It proposes SOM’s basic theory on data clustering. For SOM’s practical problems in applications, the algorithm also improved the selection of initial weights and the scope of neighborhood parameters. Finally, the simulation results in Matlab prove that the improved clustering algorithm improve the correct rate and computational efficiency of data clustering and to make the convergence speed better.

scholarly journals Online Methodology for Separating the Power Consumption of Lighting Sockets and Air-Conditioning in Public Buildings Based on an Outdoor Temperature Partition Model and Historical Energy Consumption Data

2021 ◽  
Vol 11 (3) ◽  
pp. 1031
Author(s):  
Tianyi Zhao ◽  
Chengyu Zhang ◽  
Terigele Ujeed ◽  
Liangdong Ma
Keyword(s):  
Energy Consumption ◽  
Power Consumption ◽  
Air Conditioning ◽  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Public Buildings ◽  
Energy Monitoring ◽  
Consumption Data ◽  
Monitoring Platform

Among sub-items of energy consumption in public buildings, lighting sockets play an important role in energy-saving analysis. So, the energy consumption data quality of lighting sockets is important. However, limited by the initial cost of energy monitoring platform, it is difficult to install electricity meters covering all branches and to retrofit the incompact classification electricity branches, which results in a mixture of the lighting socket energy consumption and other components. In this study, a separation methodology is proposed. First, the abnormal data in the energy monitoring platform are cleaned and screened using a clustering algorithm. Second, the average outdoor air temperature partitioning model (OATPM) method and the k-nearest neighbor (KNN) clustering algorithm method are proposed for identifying and separating the abnormal data. These two methods have complementary advantages in the best applicable scenarios, including calculation accuracy and other aspects. The verification results for three buildings show that the relative error of this separation methodology is less than 15%. Finally, this paper presents the optimization parameters of the KNN method. Through this methodology, building managers need only historical data in an energy monitoring platform to separate the combined power consumption of the lighting sockets and air-conditioning online, independent of detailed information statistics.

scholarly journals A Stochastic Approach to Energy Policy and Management: A Case Study of the Pakistan Energy Crisis

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2424 ◽  
Author(s):  
Zaman Sajid ◽  
Asma Javaid
Keyword(s):  
Energy Consumption ◽  
Energy Policy ◽  
Stochastic Analysis ◽  
Probabilistic Approach ◽  
Energy Utilization ◽  
Energy Crisis ◽  
Energy Data ◽  
Consumption Data ◽  
The Government ◽  
Energy Theft

The energy policy of a country dictates its ability to better manage and deal with an energy crisis. A sustainable energy policy deals with not only energy production but also with energy consumption. In the past, the government of Pakistan has lacked such an approach. This study aims to develop a policy-making framework to improve the energy management of Pakistan through a probabilistic approach. Stochastic analysis is performed in this study and the uncertainty in energy data is used to propose a holistic energy policy. Energy-utilization data from 17 different sources are used to compare the accuracy of energy-consumption data from 1989 to 2013. The analysis reveals that there exists an uncertainty in energy-consumption data and the major cause of this uncertainty is energy theft. The analysis shows that the industry has the highest uncertainty in its energy-data utilization, followed by the transport and the domestic sectors of Pakistan. Based on stochastic analysis, seven recommended energy-policy guidelines are presented to manage the energy crisis in the country. The analysis proposes that Pakistan needs to take measures to control energy theft.

scholarly journals Implementasi Kombinasi Algoritme Self-Organizing Map dan Fuzzy C-Means untuk Pengelompokan Performa Belajar Siswa pada Media Pembelajaran Digital

2021 ◽  
Vol 8 (3) ◽  
pp. 549
Author(s):  
Nabila Divanadia Luckyana ◽  
Ahmad Afif Supianto ◽  
Tibyani Tibyani
Keyword(s):  
Student Performance ◽  
Clustering Algorithm ◽  
Learning Rate ◽  
Digital Learning ◽  
Self Organizing Map ◽  
Log Data ◽  
Fuzzy C Means ◽  
Silhouette Coefficient ◽  
Som Clustering ◽  
Self Organizing

<p>Media pembelajaran digital mampu menyimpan data dalam bentuk log data yang dapat digunakan untuk melihat perbedaan performa siswa yang tentu saja berbeda-beda antara satu siswa dengan siswa yang lainnya. Perbedaan performa siswa tersebut menyebabkan dibutuhkannya sebuah tahapan yang berfungsi untuk mempermudah proses evaluasi dengan cara menempatkan siswa kedalam kelompok yang sesuai agar dapat membantu tenaga pengajar dalam menangani serta memberikan umpan balik yang tepat pada siswanya. Penelitian ini bertujuan memanfaatkan log data dari sebuah media pembelajaran digital dengan menggunakan kombinasi dari algoritme S<em>elf-Organizing Map</em> dan <em>Fuzzy C-Means </em>untuk mengelompokan siswa berdasarkan aktivitas mereka selama belajar dengan media tersebut. Data akan melalui sebuah proses reduksi dimensi dengan menggunakan algoritme SOM, lalu dikelompokkan dengan menggunakan algoritme FCM. Selanjutnya, data dievaluasi dengan menggunakan nilai <em>silhouette coefficient </em>dan dibandingkan dengan algoritme SOM <em>clustering </em>konvensional. Berdasarkan hasil implementasi yang telah dilakukan menggunakan 12 data <em>assignment </em>pada media pembelajaran <em>Monsakun</em>, dihasilkan parameter-parameter optimal seperti ukuran <em>map </em>atau jumlah <em>output neuron </em>sejumlah 25x25 dengan nilai <em>learning rate </em>yang berbeda-beda disetiap <em>assignment</em>. Selain itu, diperoleh pula 2 kelompok siswa pada setiap <em>assignment </em>berdasarkan nilai <em>silhouette coefficient </em>tertinggi yang mencapai lebih dari 0.8 di beberapa <em>assignment</em>. Melalui serangkaian pengujian yang telah dilakukan, penerapan kombinasi algoritme SOM dan FCM secara signifikan menghasilkan <em>cluster </em>yang lebih baik dibandingkan dengan algoritme SOM <em>clustering </em>konvensional.</p><p> </p><p><strong><em>Abstract</em></strong></p><p> <em>Digital learning media is able to store data in the form of log data that can be used to see differences in student performance. The difference in student performance causes the need for a stage that functions to simplify the evaluation process by placing students into appropriate groups in order to assist the teaching staff in handling and providing appropriate feedback to students. This study aims to utilize log data from a digital learning media using a combination of the Self-Organizing Map algorithm and Fuzzy C-Means to classify students based on their activities while learning with these media. The data will go through a dimensional reduction process using the SOM algorithm, then grouped using the FCM algorithm. Furthermore, the data were evaluated using the silhouette coefficient value and compared with the conventional SOM clustering algorithm. Based on the results of the implementation that has been carried out using 12 data assignments on the Monsakun learning media, optimal parameters such as map size or the number of neuron outputs are 25x25 with different learning rate values in each assignment. In addition, 2 groups of students were obtained for each assignment based on the highest silhouette coefficient score which reached more than 0.8 in several assignments. Through a series of tests that have been carried out, the implementation of a combination of the SOM and FCM algorithms has significantly better clusters than the conventional SOM clustering algorithm.</em></p>

scholarly journals Authenticated Energy Efficient Clustering Algorithm for secure transmission in Wireless Sensor Networks

2020 ◽  
Vol 9 (3) ◽  
pp. 111-116
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Energy Efficient ◽  
Clustering Algorithm ◽  
Complex Structure ◽  
Data Communication ◽  
Energy Utilization ◽  
Wireless Sensor ◽  
Energy Efficient Clustering

Authenticated energy consumption is the main criteria for constructing the Wireless Sensor Networks (WSNs). Every sensor has the dissimilar processing, communication range, memory unit. Each sensor node has restricted energy and memory. All the WSN based transmission architecture has the problem of authentication. The transmission overload and energy utilization have complex structure to perform the quality of service in WSN routing in a secure way. In spite of providing efficient communication for WSN, clustering approach is used to transmit the data packet from beginning node to the end node. Data gathering helps to organize the network and minimize the network overhead during data communication. Effective cluster head selection method is used for enhanced energy efficiency. Authenticated Energy Efficient Clustering Algorithm (AEEC) is proposed for efficient authenticated energy consumption-based routing methodology for WSN. The effective communication is performed by generating the authentication code within the sensor nodes to construct the innovative secured transmission based framework. The simulation results proved that the proposed method is implemented to reduce the energy consumption, routing overhead, end to end delay and increased amount of throughput compared to the other techniques.

scholarly journals K-Means Clustering in WSN with Koheneon SOM and Conscience Function

2019 ◽  
Vol 13 (8) ◽  
pp. 63
Author(s):  
Asia K. Bataineh ◽  
Mohammad Habib Samkari ◽  
Abdualla Abdualla ◽  
Saad Al-Azzam
Keyword(s):  
Neural Networks ◽  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Clustering Algorithm ◽  
Dynamic Environments ◽  
Wireless Sensor ◽  
Average Lifetime ◽  
Self Organizing Map ◽  
Over Time

Wireless Sensor Networks (WSNs) are broadly utilized in the recent years to monitor dynamic environments which vary in a rapid way over time. The most used technique is the clustering one, such as Kohenon Self Organizing Map&nbsp;(KSOM) and K means. This paper introduces a hybrid clustering technique that represents the use of K means clustering algorithm with the KSOM with conscience function of Neural Networks and applies it on a certain WSN in order to measure and evaluate its performance in terms of both energy and lifetime criteria. The application of this algorithm in a WSN is performed using the MATLAB software program. Results demonstrate that the application of K-means clustering algorithm with KSOM algorithm enhanced the performance of the WSN which depends on using KSOM algorithm only in which it offers an enhancement of 11.11% and 3.33% in terms of network average lifetime and consumed energy, respectively. The comparison among the current work and a previous one demonstrated the effectiveness of the proposed approach in this work in terms of reducing the energy consumption.

Distributed Entropy Energy-Efficient Clustering algorithm for cluster head selection (DEEEC)

2020 ◽  
Vol 39 (6) ◽  
pp. 8139-8147
Author(s):  
Ranganathan Arun ◽  
Rangaswamy Balamurugan
Keyword(s):  
Energy Efficient ◽  
Clustering Algorithm ◽  
Cluster Head ◽  
Residual Energy ◽  
Energy Utilization ◽  
Sensor Nodes ◽  
Second Stage ◽  
Energy Efficient Clustering ◽  
Two Stages ◽  
Ch Selection

In Wireless Sensor Networks (WSN) the energy of Sensor nodes is not certainly sufficient. In order to optimize the endurance of WSN, it is essential to minimize the utilization of energy. Head of group or Cluster Head (CH) is an eminent method to develop the endurance of WSN that aggregates the WSN with higher energy. CH for intra-cluster and inter-cluster communication becomes dependent. For complete, in WSN, the Energy level of CH extends its life of cluster. While evolving cluster algorithms, the complicated job is to identify the energy utilization amount of heterogeneous WSNs. Based on Chaotic Firefly Algorithm CH (CFACH) selection, the formulated work is named “Novel Distributed Entropy Energy-Efficient Clustering Algorithm”, in short, DEEEC for HWSNs. The formulated DEEEC Algorithm, which is a CH, has two main stages. In the first stage, the identification of temporary CHs along with its entropy value is found using the correlative measure of residual and original energy. Along with this, in the clustering algorithm, the rotating epoch and its entropy value must be predicted automatically by its sensor nodes. In the second stage, if any member in the cluster having larger residual energy, shall modify the temporary CHs in the direction of the deciding set. The target of the nodes with large energy has the probability to be CHs which is determined by the above two stages meant for CH selection. The MATLAB is required to simulate the DEEEC Algorithm. The simulated results of the formulated DEEEC Algorithm produce good results with respect to the energy and increased lifetime when it is correlated with the current traditional clustering protocols being used in the Heterogeneous WSNs.

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