Incremental pattern characterization learning and forecasting for electricity consumption using smart meters
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Abstract:
This paper presents a novel methodology for the incremental characterization and prediction of electricity consumption based on smart meter readings. A self-learning algorithm is developed to incrementally discover patterns in a data stream environment and sustain acquired knowledge for subsequent learning. It generates an evolving columnar structure composed of learning outcomes from each phase. This columnar structure characterizes electricity consumption and thus exposes significant patterns and continuity over time. The proposed technique is applied to smart meter data collected from RMIT University premises. Results show the potential for incremental pattern characterization learning in electricity consumption analysis and forecasting.Keywords:
Smart meter
Consumption
Characterization
Electricity meter
Smart environment and Pervasive Computing has deeply influenced the present power system. The present smart grid is capable of determining and handling the load anywhere, anytime due to the Wireless communication. The Smart Meter (SM) is enabled with communication module, potential transformer and current transformer to measure the amount of power being used. A Zigbee communication enabled SM is designed for a smart grid and its performance is evaluated in this paper. The data error rate is also evaluated.
Smart meter
Electricity meter
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Intelligent information systems are necessary to improve power grid operations. The current trend reveals the deployment of smart meter at consumer sites as the energy provider’s starting point to grid modernisation. Smart meters are considered as a core element in most of the smart grid projects. The smart metering system is designed to collect and transmit detailed consumption data. Detailed data from the smart meter could profile the consumer’s lifestyle and this is among the reasons for consumer’s resistance to smart meters. It is also not clear how the power grid will benefit from individual consumer’s detailed power usage data. This article attempts to investigate this issue by analysing if detailed end-user data is required for the operation of other control systems in the grid. Further an analysis is conducted to identify which stakeholders will benefit from the detailed smart meter data. Based on the analysis remedial measures are proposed.
Smart meter
Metering mode
Electricity meter
Automatic meter reading
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The continuous wave laser power meter is designed according to the physical properties of silicon photocell.The power meter is used to measure multiple wavelengths.The precision,practicability and utilization of the power meter are higher.
Electricity meter
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The traditional power grid becomes 'smart' when it is combined with the communication and information technology. Along with smart grid, the traditional meter is replaced with smart meter. Smart meters play an important role in energy consumption reporting and, thereby, billing. Besides smart meters, the smart grid communication network is composed of heterogeneous devices that are communicating through public networks. Therefore, smart metering communications are susceptible to cyber-attacks and privacy breaches which are still under debating. This paper gives a brief overview of smart grid, smart metering, and the communication networks. Then, the privacy and security requirements of the smart grid network are derived. The various kind of cyber-attacks are discussed, after that, the different schemes and approaches that have been proposed in previous papers are reviewed. Lastly, the open issues on security and privacy of smart grid metering communications are highlighted.
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Metering mode
Automatic meter reading
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Application of big data techniques in power system will contribute to the sustainable development of power industry companies and the establishment of strong smart grid. This paper presents a review on smart grids and big data. In order to achieve this, available articles in the literature were reviewed in perception with the features of big data and smart grids. Potential issues for smart grids and big data analytics were discussed. The recent solutions and challenges in smart grids and big data analytics were presented. The technologies used for smart big data analytics were evaluated. The contribution of this article was then presented. The results have been shown that "data" is now a new feature to be added as a main component of power systems.
Data Analysis
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Smart meter is one of the most important devices used in the smart grid (SG). The smart meter is an advanced energy meter that obtains information from the end users' load devices and measures the energy consumption of the consumers and then provides added information to the utility company and/or system operator. Several sensors and control devices, supported by dedicated communication infrastructure, are utilized in a smart meter. This paper outlines some smart meter's aspects and functions of smart meter. In addition, it introduces two basic types of smart meter system's communication technologies: Radio Frequency (RF) and Power Line Carrier (PLC) and recent advances with regard to these two technologies. This paper also presents different policy and current status as well as future projects and objectives of SG development in several countries. Finally, the paper compares some main aspects about latest products of smart meter from different companies.
Smart meter
Power-line communication
Automatic meter reading
Electricity meter
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Smart meters are developed gradually from the traditional power meters, which is an important terminal in the smart grid. It is the foundation of information integration, analysis, optimization and information expression, and undertakes the task to data acquisition, measurement and transmission. This paper reviews the development and application of smart meters, analysis the technical requirements, function of smart meter, and summarizes the benefit of smart meters’ application. It can be concluded that the large-scale use of smart meters will fully reflected energy conservation, efficiency, and safety of the smart grid.
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Electricity meter
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The concept of smart grid has been realized in the last few decades with the evolution of metering from mechanical meter to electronic meter and has progressed from Automated Meter Reading (AMR) to Advanced Metering Infrastructure (AMI). Smart grids are those networks that carries and delivers electricity based on specific smart technologies. Smart grid overcomes the limitations of conventional grids with the incorporation of smart meters in the grid through AMI. Smart meter can be defined as an energy meter that measures energy consumption with many incorporated smart features like demand side management, demand response, load control, load curtailment, etc. Smart meters forms the backbone of a smart grid imparting all of its smart technologies to the smart grid. The development of smart meter with Indian Standards is described in this paper.
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Automatic meter reading
Metering mode
Electricity meter
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Reducing the power supply-demand gap and increasing reliability of power supply are the challenges of current energy management. Implementation of smart grid, smart meters and smart metering can be a possible solution for power demand reduction, efficient power supply management, and optimization of management resource usages. Smart meters include sophisticated measurement and calculation hardware, software, calibration and communication capabilities. For interoperability within a smart grid infrastructure, smart meters are designed to perform functions, and store and communicate data according to certain standards. In this work we discuss smart meter and various elements of smart metering, current state of the technologies related to smart grid, smart meter, advanced metering infrastructure (AMI), and meter data flow in smart grid. We also discuss standards related to smart meter, meter data format and data transmission, functions of smart meter, and functionalities of smart meters, currently deployed by utilities around the world.
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Metering mode
Automatic meter reading
Electricity meter
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The increasing transformation from the legacy power grid to the smart grid brings new opportunities and challenges to power system operations. Bidirectional communications between home-area devices and the distribution system empower smart grid functionalities. More granular energy consumption data flows through the grid and enables better smart grid applications. This may also lead to privacy violations since the data can be used to infer the consumer's residential behavior, so-called power signature. Energy utilities mostly aggregate the data, especially if the data is shared with stakeholders for the management of market operations. Although this is a privacy-friendly approach, recent works show that this does not fully protect privacy. On the other hand, some applications, like nonintrusive load monitoring, require disaggregated data. Hence, the challenging problem is to find an efficient way to facilitate smart grid operations without sacrificing privacy. In this paper, we propose a privacy-preserving scheme that leverages consumer privacy without reducing accuracy for smart grid applications like load monitoring. In the proposed scheme, we use a trusted execution environment (TEE) to protect the privacy of the data collected from smart appliances (SAs). The scheme allows customer-oriented smart grid applications as the scheme does not use regular aggregation methods but instead uses customer-oriented aggregation to provide privacy. Hence the accuracy loss stemming from disaggregation is prevented. Our scheme protects the transferred consumption data all the way from SAs to Utility so that possible false data injection attacks on the smart meter that aims to deceive the energy request from the grid are also prevented. We conduct security and game-based privacy analysis under the threat model and provide performance analysis of our implementation. Our results demonstrate that the proposed method overperforms other privacy methods in terms of communication and computation cost. The execution time of aggregation for 10,000 customers, each has 20 SAs is approximately 1 second. The decryption operations performed on the TEE have a linear complexity e.g., 172800 operations take around 1 second while 1728000 operations take around 10 seconds. These results can scale up using cloud or hyper-scalers for real-world applications as our scheme performs offline aggregation.
Smart meter
Data aggregator
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