Pub Date : 2022-12-02DOI: 10.1109/actea.2009.5227825
Valentina Emilia Balas
Valentina E. Balas is currently Full Professor in the Department of Automatics and Applied Software at the Faculty of Engineering, “Aurel Vlaicu” University of Arad, Romania. She holds a Ph.D. in Applied Electronics and Telecommunications from Polytechnic University of Timisoara. Dr. Balas is author of more than 200 research papers in refereed journals and International Conferences. Her research interests are in Intelligent Systems, Fuzzy Control, Soft Computing, Smart Sensors, Information Fusion, Modeling and Simulation.
Valentina E. Balas，现任罗马尼亚阿拉德大学Aurel Vlaicu工程学院自动化与应用软件系正教授。她拥有蒂米什瓦拉理工大学应用电子和电信博士学位。Balas博士在学术期刊和国际会议上发表了200多篇研究论文。主要研究方向为智能系统、模糊控制、软计算、智能传感器、信息融合、建模与仿真。
{"title":"Keynote speakers","authors":"Valentina Emilia Balas","doi":"10.1109/actea.2009.5227825","DOIUrl":"https://doi.org/10.1109/actea.2009.5227825","url":null,"abstract":"Valentina E. Balas is currently Full Professor in the Department of Automatics and Applied Software at the Faculty of Engineering, “Aurel Vlaicu” University of Arad, Romania. She holds a Ph.D. in Applied Electronics and Telecommunications from Polytechnic University of Timisoara. Dr. Balas is author of more than 200 research papers in refereed journals and International Conferences. Her research interests are in Intelligent Systems, Fuzzy Control, Soft Computing, Smart Sensors, Information Fusion, Modeling and Simulation.","PeriodicalId":368507,"journal":{"name":"2017 International Conference on Data Management, Analytics and Innovation (ICDMAI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122238016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-02-01DOI: 10.1109/ICDMAI.2017.8073490
A. Makandar, Bhagirathi Halalli
The use of technology in medical imaging is highly increased due to improved accuracy in radiologist's decisions. Computer Aided Diagnosis (CAD) tools helps radiologists to rule out the indirect symptoms which signs for false identification. Breast mass extraction from background is crucial step in processing of mammography. Hence, the proposed method primarily contemplates on three different segmentation techniques such as adaptive threshold based, modified watershed and energy based contour segmentation techniques and then relevant features extracted by Gray Level Covariance Matric (GLCM), Segmentation-based Fractal Texture Analysis (SFTA) and Shape features then passed to Support Vector Machine (SVM) classifier to classify mass type as benign or malignant. The experimental results show that the energy based contour segmentation techniques is more suitable for discriminating the mass type with highly promising results of accuracy, specificity and sensitivity as 98.26%, 100% and 96.83% respectively comparing to other techniques. The results of proposed methods experimented on MIAS dataset.
{"title":"Classification of mass type based on segmentation techniques with support vector machine model for diagnosis of breast cancer","authors":"A. Makandar, Bhagirathi Halalli","doi":"10.1109/ICDMAI.2017.8073490","DOIUrl":"https://doi.org/10.1109/ICDMAI.2017.8073490","url":null,"abstract":"The use of technology in medical imaging is highly increased due to improved accuracy in radiologist's decisions. Computer Aided Diagnosis (CAD) tools helps radiologists to rule out the indirect symptoms which signs for false identification. Breast mass extraction from background is crucial step in processing of mammography. Hence, the proposed method primarily contemplates on three different segmentation techniques such as adaptive threshold based, modified watershed and energy based contour segmentation techniques and then relevant features extracted by Gray Level Covariance Matric (GLCM), Segmentation-based Fractal Texture Analysis (SFTA) and Shape features then passed to Support Vector Machine (SVM) classifier to classify mass type as benign or malignant. The experimental results show that the energy based contour segmentation techniques is more suitable for discriminating the mass type with highly promising results of accuracy, specificity and sensitivity as 98.26%, 100% and 96.83% respectively comparing to other techniques. The results of proposed methods experimented on MIAS dataset.","PeriodicalId":368507,"journal":{"name":"2017 International Conference on Data Management, Analytics and Innovation (ICDMAI)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123302822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-02-01DOI: 10.1109/ICDMAI.2017.8073520
Y. Angal, Anita Gade
Robotic process mechanization is the utilization of programming with manmade brainpower and machine learning abilities to deal with high-volume, repeatable assignments that beforehand required a human to perform. Robot is an automated machine. In library administration, we need librarian for management of books. To lessen curator inconvenience we have created robotization in library to speedy transport of books utilizing robotic arm. Library management robotic system is combination of software used to manage the library database and hardware used to manage the book handling. This system helps to keep the records of whole transactions of books available in a library. A robot is modular design of sensor operated motors to manage the library. Robot acquires the book information from stored database. The robot gathers the barcode data from the books and relates the decoded barcode data with the search input. The robot conveys a standardized identification scanner which gathers the scanner tag information from the books orchestrated in a vertical way and contrasts the decoded scanner tag information. Global positioning system is used for book location finding. This aides and streamlines the occupation of custodian and lessens the manual routine work done by the library staff.
{"title":"Development of library management robotic system","authors":"Y. Angal, Anita Gade","doi":"10.1109/ICDMAI.2017.8073520","DOIUrl":"https://doi.org/10.1109/ICDMAI.2017.8073520","url":null,"abstract":"Robotic process mechanization is the utilization of programming with manmade brainpower and machine learning abilities to deal with high-volume, repeatable assignments that beforehand required a human to perform. Robot is an automated machine. In library administration, we need librarian for management of books. To lessen curator inconvenience we have created robotization in library to speedy transport of books utilizing robotic arm. Library management robotic system is combination of software used to manage the library database and hardware used to manage the book handling. This system helps to keep the records of whole transactions of books available in a library. A robot is modular design of sensor operated motors to manage the library. Robot acquires the book information from stored database. The robot gathers the barcode data from the books and relates the decoded barcode data with the search input. The robot conveys a standardized identification scanner which gathers the scanner tag information from the books orchestrated in a vertical way and contrasts the decoded scanner tag information. Global positioning system is used for book location finding. This aides and streamlines the occupation of custodian and lessens the manual routine work done by the library staff.","PeriodicalId":368507,"journal":{"name":"2017 International Conference on Data Management, Analytics and Innovation (ICDMAI)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123716474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-02-01DOI: 10.1109/ICDMAI.2017.8073482
N. Patil, R. S. Kanase, D. Bondar, P. Bamane
The developing countries are still deficient in the generation of Electrical energy in contrast to demand of the country. In addition to this, prominent problem they are facing is gargantuan electrical power loss. The inadequate power quality, unpaid bills and power theft are the most vital factors behind this huge power loss. Many appropriate solutions for this problem were proposed but still there is scope for improvement. So, this paper presents an Intelligent Energy Meter (IEM) which provides solution for maintaining power quality, provides superior metering and billing system also controls power theft. The concept of intelligent energy meter is validated by experimental setup, consisting of Arduino, GSM and RASBERRYPI model B.
{"title":"Intelligent energy meter with advanced billing system and electricity theft detection","authors":"N. Patil, R. S. Kanase, D. Bondar, P. Bamane","doi":"10.1109/ICDMAI.2017.8073482","DOIUrl":"https://doi.org/10.1109/ICDMAI.2017.8073482","url":null,"abstract":"The developing countries are still deficient in the generation of Electrical energy in contrast to demand of the country. In addition to this, prominent problem they are facing is gargantuan electrical power loss. The inadequate power quality, unpaid bills and power theft are the most vital factors behind this huge power loss. Many appropriate solutions for this problem were proposed but still there is scope for improvement. So, this paper presents an Intelligent Energy Meter (IEM) which provides solution for maintaining power quality, provides superior metering and billing system also controls power theft. The concept of intelligent energy meter is validated by experimental setup, consisting of Arduino, GSM and RASBERRYPI model B.","PeriodicalId":368507,"journal":{"name":"2017 International Conference on Data Management, Analytics and Innovation (ICDMAI)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125373436","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-02-01DOI: 10.1109/ICDMAI.2017.8073489
A. Makandar, A. Patrot
Increasing suspicious instructions of various malware through a challenge to the malware analysts to identify and classify samples belongs to the malicious family. They have witnessed the very fast increase in both the number and complexity of malware set of instructions. Malware invest profoundly in technology and capability to reorganize the process of building and mutate existing malware set of instructions to avoid traditional protection. Classify malware variants by applying image processing techniques. The textures play an important role in many image processing applications. In this paper we proposed the Support Vector Machine (SVM) multi-class malware image classification challenge from an image processing perspective. The multi-resolution and wavelets are used to build effective texture feature vector using Gabor Wavelet, GIST and Discrete wavelet Transform and other features. The proposed algorithm experimented on Malimg Dataset of malware total 12,470 samples are used. In that 1610 samples are trained and 1710 samples are tested on 8 malware family which is randomly selected from the dataset. We compare this approach to existing malware classification approaches previously published research work. This is an efficient and more accurate malware detection algorithm using Wavelet Transform with machine learning classifiers techniques to detect malware samples more capably compare to existing work.
{"title":"Malware class recognition using image processing techniques","authors":"A. Makandar, A. Patrot","doi":"10.1109/ICDMAI.2017.8073489","DOIUrl":"https://doi.org/10.1109/ICDMAI.2017.8073489","url":null,"abstract":"Increasing suspicious instructions of various malware through a challenge to the malware analysts to identify and classify samples belongs to the malicious family. They have witnessed the very fast increase in both the number and complexity of malware set of instructions. Malware invest profoundly in technology and capability to reorganize the process of building and mutate existing malware set of instructions to avoid traditional protection. Classify malware variants by applying image processing techniques. The textures play an important role in many image processing applications. In this paper we proposed the Support Vector Machine (SVM) multi-class malware image classification challenge from an image processing perspective. The multi-resolution and wavelets are used to build effective texture feature vector using Gabor Wavelet, GIST and Discrete wavelet Transform and other features. The proposed algorithm experimented on Malimg Dataset of malware total 12,470 samples are used. In that 1610 samples are trained and 1710 samples are tested on 8 malware family which is randomly selected from the dataset. We compare this approach to existing malware classification approaches previously published research work. This is an efficient and more accurate malware detection algorithm using Wavelet Transform with machine learning classifiers techniques to detect malware samples more capably compare to existing work.","PeriodicalId":368507,"journal":{"name":"2017 International Conference on Data Management, Analytics and Innovation (ICDMAI)","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116675299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-02-01DOI: 10.1109/ICDMAI.2017.8073500
Omkar K. Shinde, V. B. Pulavarthi
Static Synchronous compensator (STATCOM), a member of FACTS family has become a prominent device for reactive power compensation and dynamic performance improvement of the system. A voltage source converter (VSC) is at the heart of a STATCOM and has been realized by various topologies in literature. Its control can be divided into compensation and DC voltage balance. A review on converters for STATCOM and their control is presented along with the new emerging technologies in the system. One more attribute of STATCOM besides compensation is its use for improving system stability which is achieved through implementation of various control algorithms, switching techniques which have been reported in literature.
{"title":"STATCOM converters and control: A review","authors":"Omkar K. Shinde, V. B. Pulavarthi","doi":"10.1109/ICDMAI.2017.8073500","DOIUrl":"https://doi.org/10.1109/ICDMAI.2017.8073500","url":null,"abstract":"Static Synchronous compensator (STATCOM), a member of FACTS family has become a prominent device for reactive power compensation and dynamic performance improvement of the system. A voltage source converter (VSC) is at the heart of a STATCOM and has been realized by various topologies in literature. Its control can be divided into compensation and DC voltage balance. A review on converters for STATCOM and their control is presented along with the new emerging technologies in the system. One more attribute of STATCOM besides compensation is its use for improving system stability which is achieved through implementation of various control algorithms, switching techniques which have been reported in literature.","PeriodicalId":368507,"journal":{"name":"2017 International Conference on Data Management, Analytics and Innovation (ICDMAI)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128735842","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-02-01DOI: 10.1109/ICDMAI.2017.8073511
Kulkarni Vaibhav Mukund
It's an exploratory review study, based on testing the hypothesis concerned. The statuesque is already in the existence in the form of the established theories and side of hypothesis need not to be researched again separately, considering it as the prior work referred. That was not a part of this study, this study is a further episode of the same series of research. Operations management was limited to some of the functional areas or domains of management & now it is expanding in the scope to some more functional areas. It does not look limited to any specific functional area or areas of management, rather_is becoming now in practice, gradually. & seems likely to become, fully, as a common function of the management, like the functions from planning to control, & similarly looks necessary to be accepted, in theory too. It seems to be neither mere a functional area, nor mere a common factor among some of the functional areas only, but clicks as a common function of management, as it is getting spread, which may be needed to be recognized now theoretically too. Operation function too has been pervaded like every other function, by the different other functions of management. Every function of management requires separate plans respectively. Organising also pervades different other functions of management and also required controlling as the planning requires. That's why, the apparent flowing' function's similarity with & influence by 1st 2 functions of the management, planning & organising, might not been needed to be a reason to deny the possibility of it's existence as a separate function of management or administration. Thus the ‘flow-process’ or operations seems not just as a separate ‘functional-area’ like manufacturing, but seems a function of management like planning etc. which may pervade some or all the functional areas of management, like Marketing, HRM, Finance, Production, Research, Office & IT etc.
{"title":"An exploratory critical theoretical review of the structure of the ongoing theoretical flow of the management — Functions","authors":"Kulkarni Vaibhav Mukund","doi":"10.1109/ICDMAI.2017.8073511","DOIUrl":"https://doi.org/10.1109/ICDMAI.2017.8073511","url":null,"abstract":"It's an exploratory review study, based on testing the hypothesis concerned. The statuesque is already in the existence in the form of the established theories and side of hypothesis need not to be researched again separately, considering it as the prior work referred. That was not a part of this study, this study is a further episode of the same series of research. Operations management was limited to some of the functional areas or domains of management & now it is expanding in the scope to some more functional areas. It does not look limited to any specific functional area or areas of management, rather_is becoming now in practice, gradually. & seems likely to become, fully, as a common function of the management, like the functions from planning to control, & similarly looks necessary to be accepted, in theory too. It seems to be neither mere a functional area, nor mere a common factor among some of the functional areas only, but clicks as a common function of management, as it is getting spread, which may be needed to be recognized now theoretically too. Operation function too has been pervaded like every other function, by the different other functions of management. Every function of management requires separate plans respectively. Organising also pervades different other functions of management and also required controlling as the planning requires. That's why, the apparent flowing' function's similarity with & influence by 1st 2 functions of the management, planning & organising, might not been needed to be a reason to deny the possibility of it's existence as a separate function of management or administration. Thus the ‘flow-process’ or operations seems not just as a separate ‘functional-area’ like manufacturing, but seems a function of management like planning etc. which may pervade some or all the functional areas of management, like Marketing, HRM, Finance, Production, Research, Office & IT etc.","PeriodicalId":368507,"journal":{"name":"2017 International Conference on Data Management, Analytics and Innovation (ICDMAI)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129644839","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-02-01DOI: 10.1109/ICDMAI.2017.8073506
Vishnu S. Nair, Deepak Gupta, S. Gunasekar
India is a country which possess the world's oldest and most diverse cultures embedded by a set of core values. In spite of this diversity, core values are cherished by the people across the country. Consumer choices are impacted to a good extend by their way of life, qualities and social class, alongside the society they connect with and those they respect. In general, values are held beliefs that are closely connected to culture about what's acceptable and desirable. The objective of this study is to understand the influence of values on quality seeking behavior of Indian consumers. A model was created to conceptualize and understand these factors. The study was conducted by means of an online survey which collected data from 166 respondents across 32 Indian cities. All variables used in the study are measured using standard scales taken from literature. The results from the logistic regression indicate that giving of more importance to values such as being well respected in the society and sense of accomplishment has a positive influence on the quality consciousness. The study further reveals that consumers who earn higher income tend to be more quality seeking than those who earn less income.
{"title":"Influence of values on quality consciousness among Indian consumers","authors":"Vishnu S. Nair, Deepak Gupta, S. Gunasekar","doi":"10.1109/ICDMAI.2017.8073506","DOIUrl":"https://doi.org/10.1109/ICDMAI.2017.8073506","url":null,"abstract":"India is a country which possess the world's oldest and most diverse cultures embedded by a set of core values. In spite of this diversity, core values are cherished by the people across the country. Consumer choices are impacted to a good extend by their way of life, qualities and social class, alongside the society they connect with and those they respect. In general, values are held beliefs that are closely connected to culture about what's acceptable and desirable. The objective of this study is to understand the influence of values on quality seeking behavior of Indian consumers. A model was created to conceptualize and understand these factors. The study was conducted by means of an online survey which collected data from 166 respondents across 32 Indian cities. All variables used in the study are measured using standard scales taken from literature. The results from the logistic regression indicate that giving of more importance to values such as being well respected in the society and sense of accomplishment has a positive influence on the quality consciousness. The study further reveals that consumers who earn higher income tend to be more quality seeking than those who earn less income.","PeriodicalId":368507,"journal":{"name":"2017 International Conference on Data Management, Analytics and Innovation (ICDMAI)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131799947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-02-01DOI: 10.1109/ICDMAI.2017.8073528
A. Gupta, S. Jagadish
India is an extremely populous country with a massive population of 1.2 billion residents, and a large proportion of people with disabilities in both rural and urban India. Speech impairment is common with people with hearing loss since birth. Among the total disabled population, about 27% have movement constraints and hence are confined to wheelchairs. This paper proposes the idea of using machine learning implementation to develop a device that can benefit the speech and motion constrained population. Gesture control plays an essential role in order to convert the sensor data into speech output or operating a pick and place bot for the motion constrained. Various algorithms are interfaced with the device to provide efficient functionality and throughput. Machine learning and data analytics technologies have been on the rise recently and are finding applications in various domains and industries.
{"title":"Machine learning oriented gesture controlled device for the speech and motion impaired","authors":"A. Gupta, S. Jagadish","doi":"10.1109/ICDMAI.2017.8073528","DOIUrl":"https://doi.org/10.1109/ICDMAI.2017.8073528","url":null,"abstract":"India is an extremely populous country with a massive population of 1.2 billion residents, and a large proportion of people with disabilities in both rural and urban India. Speech impairment is common with people with hearing loss since birth. Among the total disabled population, about 27% have movement constraints and hence are confined to wheelchairs. This paper proposes the idea of using machine learning implementation to develop a device that can benefit the speech and motion constrained population. Gesture control plays an essential role in order to convert the sensor data into speech output or operating a pick and place bot for the motion constrained. Various algorithms are interfaced with the device to provide efficient functionality and throughput. Machine learning and data analytics technologies have been on the rise recently and are finding applications in various domains and industries.","PeriodicalId":368507,"journal":{"name":"2017 International Conference on Data Management, Analytics and Innovation (ICDMAI)","volume":"203 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124555404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-02-01DOI: 10.1109/ICDMAI.2017.8073513
Mrinall K. Patil, S. Deshmukh, Ritu Agrawal
Electricity price is the governing factor in taking various operational decisions such as generation scheduling, exchange of power amongst utilities, trading of power in market along with keeping pace with technical stability and reliability of power system. The accurate forecasting of price of electric power is a need of every participant in restructured power system scenario. Hence, this paper is an attempt to apply data mining for forecasting the electricity price. The k-mean algorithm is used for classification of data of historical prices of New York Energy Market (NYISO) according to type of day, into three classes. The k-NN algorithm to divide the classified data into two patterns for month of February-March and April to January. Once classification is done, the data is used for developing forecasting model. The historical electricity price data of 2014, along with load is used as input patterns. The accuracy of the developed model is verified by forecasting respective period samples of 2015. The performance of forecasting model is very satisfactory. The step wise development of forecasting model and the results are discussed in detail.
{"title":"Electric power price forecasting using data mining techniques","authors":"Mrinall K. Patil, S. Deshmukh, Ritu Agrawal","doi":"10.1109/ICDMAI.2017.8073513","DOIUrl":"https://doi.org/10.1109/ICDMAI.2017.8073513","url":null,"abstract":"Electricity price is the governing factor in taking various operational decisions such as generation scheduling, exchange of power amongst utilities, trading of power in market along with keeping pace with technical stability and reliability of power system. The accurate forecasting of price of electric power is a need of every participant in restructured power system scenario. Hence, this paper is an attempt to apply data mining for forecasting the electricity price. The k-mean algorithm is used for classification of data of historical prices of New York Energy Market (NYISO) according to type of day, into three classes. The k-NN algorithm to divide the classified data into two patterns for month of February-March and April to January. Once classification is done, the data is used for developing forecasting model. The historical electricity price data of 2014, along with load is used as input patterns. The accuracy of the developed model is verified by forecasting respective period samples of 2015. The performance of forecasting model is very satisfactory. The step wise development of forecasting model and the results are discussed in detail.","PeriodicalId":368507,"journal":{"name":"2017 International Conference on Data Management, Analytics and Innovation (ICDMAI)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123579429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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