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نتیجه جستجو - smart meters

تعداد مقالات یافته شده: 20
ردیف عنوان نوع
1 A pattern recognition methodology for analyzing residential customers load data and targeting demand response applications
یک روش تشخیص الگو برای تجزیه و تحلیل داده های باربری مشتریان مسکونی و هدف قرار دادن برنامه های پاسخ تقاضا-2019
The availability of smart meter data allows defining innovative applications such as demand response (DR) programs for households. However, the dimensionality of data imposes challenges for the data min- ing of load patterns. In addition, the inherent variability of residential consumption patterns is a major problem for deciding on the characteristic consumption patterns and implementing proper DR settle- ments. In this regard, this paper utilizes a data size reduction and clustering methodology to analyze residential consumption behavior. Firstly, the distinctive time periods of household activity during the day are identified. Then, using these time periods, a modified symbolic aggregate approximation (SAX) technique is utilized to transform the load patterns into symbolic representations. In the next step, by applying a clustering method, the major consumption patterns are extracted and analyzed. Finally, the customers are ranked based on their stability over time. The proposed approach is applied on a large dataset of residential customers’ smart meter data and can achieve three main goals: 1) it reduces the dimensionality of data by utilizing the data size reduction, 2) it alleviates the problems associated with the clustering of residential customers, 3) its results are in accordance with the needs of systems oper- ators or demand response aggregators and can be used for demand response targeting. The paper also provides a thorough analysis of different aspects of residential electricity consumption and various ap- proaches to the clustering of households which can inform industry and research activity to optimize smart meter operational use
Keywords: Clustering algorithms | Demand response | Load patterns | Smart meters | Symbolic aggregate approximation (SAX)
مقاله انگلیسی
2 Motif-based association rule mining and clustering technique for determining energy usage patterns for smart meter data
روش استخراج و مجموعه خوشه بندی قانون مبتنی بر موتیف برای تعیین الگوهای مصرف انرژی برای داده های کنتور هوشمند-2019
Nowadays, smart energy meters are being used to record periodic electricity consumption. The real time data produced by smart meters provide the detailed information about the electricity usage of a particular consumer. In this paper, we propose a motif-based association rule mining and clustering technique for determining the energy usage patterns for smart meter data. The association rules of motifs within a specific time window characterizes behaviors of energy consumer. In particular, we focus on an extraction of the temporal information of the smart meter. The process is based on the unique combination of Symbolic Aggregate approximation (SAX), temporal motif discovery and association rule mining to detect the expected and unexpected patterns robustly. Experiments on real world smart meter datasets justify that the proposed model discovers the useful routine behavior of electricity energy consumers, which are helpful for electricity utility experts. Further, in this paper, clustering on the motifs is performed which gives the different consumption behavior of consumers on different days which can help distribution network operator (DNO) for electricity network modeling and management. In future, we can form motif-based signature using the proposed approach for different applications such as anomaly detection and dynamic detection of operating patterns.
Keywords: Smart Meter | Association rule | Data analytics | Temporal data mining | Clustering Motif
مقاله انگلیسی
3 Prediction Method for Smart Meter Life Based On Big Data
روش پیش بینی برای زندگی هوشمند متر بر اساس داده های بزرگ-2018
In order to better investigate the working life of Smart Meter and discover in advance the possible faults existed in the same batch, the operational data of Smart Meter in running state has to be analyzed so as to construct the prediction model of Smart Meter life. Firstly, we collect all the data from the running smart meters regarding the operational faults, maintenance management, and their application data from the Power Information Collection System; Secondly, we analyze the operational data in running state; Lastly, according to the advantages and disadvantages of the three reliability prediction methods (element stress method, reliability prediction method based on reliability test, and reliability prediction method based on reliability verification), we can protract the model that can reflect the life degradation characteristics and life prediction of smart meter.
Keywords: smart meter, reliability prediction technology, big data
مقاله انگلیسی
4 Compression of smart meter big data_ A survey
فشرده سازی داده های بزرگ متریک هوشمند : یک مرور-2018
In recent years, the smart grid has attracted wide attention from around the world. Large scale data are collected by sensors and measurement devices in a smart grid. Smart meters can record fine-grained information about electricity consumption in near real-time, thus forming the smart meter big data. Smart meter big data has provided new opportunities for electric load forecasting, anomaly detection, and demand side management. However, the high-dimensional and massive smart meter big data not only creates great pressure on data transmission lines, but also incur enormous storage costs on data centres. Therefore, to reduce the transmission pressure and storage overhead, improve data mining efficiency, and thus fulfil the potential of smart meter big data. This study presents a comprehensive study on the compression techniques for smart meter big data. The development of smart grids and the characteristics and application challenges of electric power big data are first introduced, followed by analysis of the characteristics and benefits of smart meter big data. Finally, this study focuses on the potential data compression methods for smart meter big data, and discusses the evaluation methods for smart meter big data compression.
Keywords: Smart grid ، Smart meter ، Energy big data ، Data compression
مقاله انگلیسی
5 فشرده سازی هوشمند برای داده های بزرگ: مرور
سال انتشار: 2018 - تعداد صفحات فایل pdf انگلیسی: 11 - تعداد صفحات فایل doc فارسی: 40
در سال های اخیر، شبکه هوشمند توجه گسترده ای از سراسر جهان را به خود جلب کرده است. داده های مقیاس بزرگ توسط سنسور ها و دستگاه های اندازه گیری در یک شبکه هوشمند جمع آوری می شوند. مقیاس هوشمند می تواند اطلاعات دقیق در مورد مصرف الکتریسیته را در زمان واقعی به ثبت برساند، بنابراین داده های بزرگ در مقیاس هوشمند اندازه گیری می شود. داده های بزرگ مقیاس هوشمند فرصت های جدیدی برای پیش بینی بار الکتریکی، کشف عادت ها و مدیریت تقاضا ارائه داده است. با این حال، ابعاد بزرگ و داده های بزرگ در مقیاس هوشمند عظیم نه تنها فشار زیادی را بر خطوط انتقال داده ایجاد می کند، بلکه هزینه های ذخیره سازی زیادی را در مراکز داده نیز به همراه می آورد. بنابراین، برای کاهش فشار انتقال و ارتفاع محل ذخیره سازی، برای بهبود راندمان استخراج داده ها، و به اين ترتيب ظرفیت های تحقق هوشمند داده های بزرگ 130 سانتی متری است. مقاله پیش رو یک مطالعه جامع در مورد تکنیک های فشرده سازی داده های بزرگ هوشمند را ارائه می دهد. توسعه شبکه های هوشمند و خصوصیات و چالش های کاربرد داده های بزرگ الکتریکی ابتدا معرفی شده و سپس تجزیه و تحلیل ویژگی ها و مزایای داده های بزرگ مقیاس بزرگ انجام می پذیرد. در نهایت، این مطالعه بر روی روش های فشرده سازی اطلاعات بالقوه برای داده های بزرگ هوشمند تمرکز می کند و روش های ارزیابی فشرده سازی داده های مقیاس هوشمند را مورد بحث قرار می دهد.
کلمات کلیدی: شبکه هوشمند | مقیاس هوشمند | داده های بزرگ انرژی | فشرده سازی داده ها.
مقاله ترجمه شده
6 مروری روی سیستم مدیریت انرژی خانه با درنظر گرفتن پاسخ های تقاضا، فناوری های هوشمند و کنترل گرهای هوشمند
سال انتشار: 2018 - تعداد صفحات فایل pdf انگلیسی: 12 - تعداد صفحات فایل doc فارسی: 35
تقاضای روزافزون برق و پیدایش شبکه های هوشمند، فرصتهای جدیدی برای ایجاد یک سیستم مدیریت انرژی خانه (HEMS) ایجاد کرده است که می تواند مصرف انرژی را کاهش دهد. HEMSاز یک ابزار پاسخ به تقاضا (DR) استفاده می کند که تقاضا را برای بهبود مصرف انرژی خانه تغییر داده و کمتر می کند. این سیستم معمولا" با درنظر گرفتن چندین عامل مثل هزینه های انرژی، نگرانی های زیست محیطی، ویژگی های بار، و راحتی مشتری، زمان بندی های مصرف بهینه خلق می کند. با توسعه سنجه های هوشمند، انجام دادن کنترل بار با استفاده از HEMS با ابزار امکانپذیر کننده سیستم پاسخ به تقاضا امکانپذیر شده است. این مقاله یک مرور جامع روی تحقیقات قبلی و فعلی مربوط به HEMS را با درنظر گرفتن برنامه های مختلف پاسخ به تقاضا، فناوری های هوشمند و کنترل گرهای زمان بندی بار فراهم می کند. کاربرد هوش مصنوعی برای کنترل گرهای زمان بندی بار مثل شبکه عصب مصنوعی، منطق فازی، و سیستم تفسیر فازی عصبی سازگار، نیز مرور می شود. روشهای بهینه سازی ذهنی که به صورت گسترده ای برای زمان بندی بهینه وسایل مختلف برقی در یک خانه هوشمند استفاده می شوند، نیز مورد بحث قرار می گیرد.
عبارتهای شاخص: سیستم مدیریت انرژی خانه | پاسخ به تقاضا | فناوری های هوشمند | فناوری بی سیم یکپارچه | کنترل گر زمان بندی هوشمند.
مقاله ترجمه شده
7 Big Data Mining of Users Energy Consumption Patterns in the Wireless Smart Grid
کاوش داده های بزرگ الگوهای مصرف انرژی کاربران در شبکه هوشمند بی سیم-2018
A growing number of utility companies are starting to use cellular wireless networks to transmit data in the smart grid. Consequently, millions of users daily energy consumption data are sent by wireless smart meters. However, the broadcast transfer manner of wireless communication makes it naturally vulnerable to cyber attacks. Since smart meter readings can easily be leaked, users energy patterns could be inferred. Hence, users privacy at home is under serious threat. This article begins by introducing the existing work on stealing data from wireless communication networks. Then three types of big data mining schemes for analyzing stolen data are represented. Finally, we discuss several ongoing defense strategies in the era of the wireless smart grid.
Keywords: Big Data, cellular radio, data mining, data privacy, energy consumption, power engineering computing, power system security, security of data,smart meters, smart power grids
مقاله انگلیسی
8 Big Data Analytics in Chinas Electric Power Industry
تحلیل داده های بزرگ در صنعت برق چین-2018
Driven by its fast-growing high-tech industry during the 2010s, China has witnessed an upsurge in data rates from online shopping, mobile Internet services, and industrial informatization, among other uses. As awareness of big data has increased, multiple industries have begun to embrace analytics to exploit value from all these data. The electric power industry, using modern information and communication technologies and millions of newly deployed smart meters, is an important forerunner in the big data analytics field.
مقاله انگلیسی
9 Security analysis of an advanced metering infrastructure
تجزیه و تحلیل امنیت یک زیرساخت اندازه گیری پیشرفته-2017
Article history:Received 28 December 2016Revised 23 February 2017Accepted 25 February 2017 Available online xxxKeywords:Advanced Metering Infrastructure Smart MetersData Collectors Attack Vectors Targets Functionality Attacks ImpactsAn advanced metering infrastructure is an integrated system of smart meters, communica- tions networks and data management systems designed to support the safe, efficient and re- liable distribution of electricity while providing advanced functionality to energy customers. Unfortunately, sophisticated cyber attacks on advanced metering infrastructures are a clear and present danger. The most devastating scenario involves a computer worm that traverses advanced metering infrastructures and permanently disables millions of smart meters.This paper presents a security analysis of an advanced metering infrastructure com- prising more than one million smart meters, 100+ data collectors and two meter data man- agement systems. Specifically, it provides detailed evaluations of the attack surface, targets– especially the critical data collectors – and their functionality, and possible attacks and their impacts. The systematic identification of each target and its functionality, and possi- ble attacks and their direct impacts, are essential to understanding the security landscape as well as specifying and prioritizing mitigation efforts as part of a robust risk management program. Although this work is based on an analysis of one large advanced metering infras- tructure, strong attempts have been undertaken to extract and articulate the commonalities when describing the attack surface, targets, possible attacks and their impacts. Thus, the re- sults presented in this paper can be used as a foundation upon which the unique aspects of an advanced metering infrastructure can be added to create a robust risk management program geared for the specific deployment.© 2017 Elsevier B.V. All rights reserved.
Keywords:Advanced Metering Infrastructure | Smart Meters | Data Collectors | Attack Vectors | Targets | Functionality | Attacks | Impacts
مقاله انگلیسی
10 Design and implementation of a secure cloud-based billing model for smart meters as an Internet of things using homomorphic cryptography
طراحی و پیاده سازی مدل صدور صورتحساب مبتنی بر ابر برای دستگاه های هوشمند به عنوان اینترنت از چیزهایی که از رمزنگاری هماهنگ استفاده می کنند-2017
Smart grids introduce many outstanding security and privacy issues, especially when smart meters are connected to public networks, creating an Internet of things in which customer usage data is frequently exchanged and processed in large volumes. In this research, we propose a cloud-based data storage and processing model with the ability to preserve user privacy and confidentiality of smart meter data in a smart grid. This goal is achieved by encrypting smart meter data before storage on the cloud using a homomorphic asymmetric key cryptosystem. By applying the homomorphic feature of the cryptographic technique, we propose methods to allow most of the computing works of calculating customer invoices based on total electricity consumption to be done directly on encrypted data by the cloud. One of the outstanding features in our model is the aggregation of encrypted smart meter readings using fixed point number arithmetic. To test the feasibility of our model, we conducted many experiments to estimate the number of homomorphic additions to be performed by the cloud and the computation time in different billing periods using data from the Smart project, in which smart grid readings were continuously collected from different households in every second within two months and electricity usage data collected every minute from 400 anonymous houses in one day. We also propose a parallel version of our billing algorithm to utilize the processing capability of multi-core processors in cloud servers so that computation time is reduced significantly compared to using our sequential algorithm. Our research works and experiments demonstrate clearly how cloud services can strengthen the security, privacy and efficiency of privacy-sensitive data frequently exchanged and processed in an Internet of things where smart meters communicate directly with public networks.
Keywords: Smart grid | IoT | Internet of things | Homomorphic encryption
مقاله انگلیسی
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