Energy Consumption Information Research Articles

Smart Grid Network Resource Scheduling Algorithm Based on Network Calculus

Smart grid technology is further developed on the basis of distributed power grids and micro-grids. The communications network is an indispensable part of the smart grid. How to achieve real-time accurate transmission of various types of smart information is a technical guarantee for smart grids. This paper analyze data of smart grid bases on the demand of net resource, and adopts the idea of priority scheduling to draw on the advantages of integrated services and differentiated services to design a time-sharing polling scheduling strategy. In this strategy, the algorithm classify the network data, Using network calculus theory to analyze the optimal length of the queue satisfying the upper bound of delay, According to the degree of network congestion perception, the credibility of the data is calculated, and non-adaptive flows are quickly identified. The strategy ensure that the data is able to transfer to the user quickly and lively depend on its needs, So ensure the real-time transmission of energy supply & consumption information of active distribution network, realize multi-source collaborative optimization scheduling and distributed hierarchical intelligent control management. The algorithm can effectively ensure the demand of high-priority services, making the overall performance of the smart grid communication system data such as real-time and accuracy optimized.

An improved micro analysis-based energy consumption and carbon emissions modeling approach for a milling center

The complex structure and large number of energy-consuming components in a machine tool provide a constant challenge to the researchers to characterize and model the energy consumption during a machining process. Recently, Therblig-based energy model in conjunction with value stream mapping has been used to identify and reduce the energy waste in a turning process. However, this model does not depict the information of energy consumption and carbon emissions throughout the process. Hence, it is difficult to estimate how much energy consumption and carbon emissions are caused by each activity. This paper presents an improved micro analysis of the energy and carbon emissions for each activity of a machining process on a value stream map. A case study of milling process is provided to illustrate the proposed methodology. The case study shows the improvement in energy efficiency, time efficiency, and carbon emissions. The energy and carbon emissions of each activity provide better transparency of energy flow and carbon emissions information throughout the machining process. The proposed methodology can not only be used to reduce the peak load at the factory level but also help to develop potential energy and carbon emission reduction strategies during the process planning stage.

Data-driven building load profiling and energy management

Commercial buildings consume a lot of energy and contribute a significant part of greenhouse gas emission. Many energy-saving or green-building initiatives were compromised by equipment and human-related faults under the umbrella of poor facility management. Data-driven building energy management is a cost-effective approach to improve energy efficiency of commercial buildings, and gains more and more popularity worldwide with the deployment of smart metering systems. This paper developed a systematic process of using smart metering data to quantify building daily load profiles (i.e. energy consumption patterns) with a set of statistics, e.g. base load, peak load, rising time and so on. Then prediction models of these building load statistics are constructed from historical training data consisting of energy consumption, environment and holiday information. At last residuals of the prediction models are analyzed to form statistical control charts. As a result anomaly energy consumption could be detected by comparing the predicted statistics and observed ones, which will help building managers to locate problems just in time. The effectiveness of the proposed solution is verified through real-world data analysis and computational studies.

A review and vision on authentication and privacy preservation schemes in smart grid network

Security and privacy preservation are important research challenges in Internet of Things (IoT) applications, wherein the information related to customers' habit is seamlessly collected for further analytics. One such IoT application; Smart grid (SG) network uses Information and Communication Technology (ICT) to automate the traditional electricity network between customers and utility companies. In the SG setting, on one side, there is a seamless exchange of energy consumption information, demand change and price change messages between customers and utility grid; on the other side, it offers various cyber security challenges. To avoid some‐one intercepting these messages and modify personal information, it is necessary to communicate these messages using an appropriate security and privacy preservation scheme. The objective of this article is conduct a comprehensive survey on existing security and privacy techniques being developed for the SG network. The survey presented in this article is motivated by the earlier research attempts on cyber security issues in SG, key management techniques, authentication schemes and privacy preservation techniques in SG network. The paper provides a wide review on various authentication techniques till recent developments on lightweight authentication and the privacy preservation techniques including encryption based privacy preservation using homomorphic encryption, elliptic curve cryptography, and lattice cryptography. Furthermore, the article presents our vision on privacy and utility trade‐off issues, privacy enhancement using blockchains, lightweight cryptography, and security challenges in fog node.

Selective control of magnetism in FeAl/PIN-PMN-PT using an electric field

Mediating nonvolatile magnetism by purely electrical methods is a crucial challenge for the development of high-density and low energy consumption information storage. Research into most material systems, including ferromagnetic /ferroelectric heterostructures and, especially, magnetostriction/lead magnesium niobate-lead titanate (PMN-PT), has focused mainly on the nonvolatile strain-mediated converse magnetoelectric (ME) effect. The recent discovery of a type of FeAl/lead indium niobate (PIN)-PMN-PT heterostructure has provided an approach for electrical control of magnetism at room temperature. We demonstrate selective control of nonvolatile magnetism in (001)- and (111)-oriented FeAl/PIN-PMN-PT. In order to illustrate the behavior of electric field control of magnetism in (111)-oriented PIN-PMN-PT, a converse ME coupling mechanism is presented via polarization switching models. This mechanism indicates that the magnetization process is related to both the direction of polarization switching and the strain-mediated effect. By controlling the piezoresponse force microscopy (PFM) scan direction and analyzing the color contrast of the PFM images, we confirm the validity of the proposed models of polarization switching and demonstrate the validity of the mechanism. The results of this study may offer a method for the selective control of magnetism in nonvolatile multilevel data storage.

Influencing occupant's choices by using spatiotemporal information visualization in Immersive Virtual Environments

Feedback systems are useful to increase occupant awareness and disrupt their wasteful behaviors. The study presented in this paper explores the opportunity for feedback system designers to use information visualization to guide occupants to a desired option without reducing the occupant's control over the environment. In contrast to previous studies, we utilize spatiotemporal information visualization, that not only contradicts with occupants' initial choices but also presents the consequences of their choices. The information visualization is presented in a 1 to 1 scale, where the visualization coexists with the occupants over the office space in an immersive virtual environment. In total, 80 participants' data were collected. Two visualizations were presented to the participants, one relating to energy consumption and another relating to light distribution in the room. The results show that the information visualizations made the participants reconsider their choices. Our results also show that the energy consumption information visualization was more effective on guiding the participants towards an option that supported by the visualization, compared to the visualization that focused on room lighting. The results support the idea of using information visualization to drive occupant behavior towards desired outcomes and could help in the design of future feedback systems targeted at influencing occupant behavior.

Provisioning of energy consumption information for mobile ads

Advertisement-embedded mobile applications have been reported to consume a non-trivial amount of energy. Although a few studies have focused on the energy consumption of mobile advertisements (ads), no previous work has addressed the mobile ad ecosystem, which consists of users, application developers, and ad providers. In this paper, we define the advertisement energy information (AEI) required for the mobile ad ecosystem, and we propose a set of application programming interfaces (APIs) to provide AEI that considers various requirements of the underlying ecosystem. To realize the APIs, we developed a system service in Android to collect the AEI accurately and with low overhead. The experiment results show the validity of the proposed scheme, and the case studies demonstrate the usefulness of the proposed APIs.

EFFECT: an efficient flexible privacy-preserving data aggregation scheme with authentication in smart grid

Smart grid is considered as a promising approach to solve the problems of carbon emission and energy crisis. In smart grid, the power consumption data are collected to optimize the energy utilization. However, security issues in communications still present practical concerns. To cope with these challenges, we propose EFFECT, an efficient flexible privacy-preserving aggregation scheme with authentication in smart grid. Specifically, in the proposed scheme, we achieve both data source authentication and data aggregation in high efficiency. Besides, in order to adapt to the dynamic smart grid system, the threshold for aggregation is adjusted according to the energy consumption information of each particular residential area and the time period, which can support fault-tolerance while ensuring individual data privacy during aggregation. Detailed security analysis shows that our scheme can satisfy the desired security requirements of smart grid. In addition, we compare our scheme with existing schemes to demonstrate the effectiveness of our proposed scheme in terms of low computational complexity and communication overhead.

Distributed meter data aggregation framework based on Blockchain and homomorphic encryption

A significant progress in modern power grids is witnessed by the tendency of becoming complex cyber-physical systems. As a fundamental physical infrastructure, smart meter in the demand side provides real-time energy consumption information to the utility. However, ensuring information security and privacy in the meter data aggregation process is a non-trivial task. This study proposes a distributed, privacy-preserving, and secure meter data aggregation framework, backed up by Blockchain and homomorphic encryption (HE) technologies. Meter data are aggregated and verified by a hierarchical Blockchain system, in which the consensus mechanism is supported by the practical Byzantine fault tolerance algorithm. On the top of the Blockchain system, HE technology is used to protect the privacy of individual meter data items during the aggregation process. Performance analysis is conducted to validate the proposed method.

A Model for Predicting Energy Usage Pattern Types with Energy Consumption Information According to the Behaviors of Single-Person Households in South Korea

Residential energy consumption accounts for the majority of building energy consumption. Physical factors and technological developments to address this problem have been researched continuously. However, physical improvements have limitations, and there is a paradigm shift towards energy research based on occupant behavior. Furthermore, the rapid increase in the number of single-person households around the world is decreasing residential energy efficiency, which is an urgent problem that needs to be solved. This study prepared a large dataset for analysis based on the Korean Time Use Survey (KTUS), which provides behavioral data for actual occupants of single-person households, and energy usage pattern (EUP) types that were derived through K-modes clustering. The characteristics and energy consumption of each type of household were analyzed, and their relationships were examined. Finally, an EUP-type predictive model, with a prediction rate of 95.0%, was implemented by training a support vector machine, and an energy consumption information model based on a Gaussian process regression was provided. The results of this study provide useful basic data for future research on energy consumption based on the behaviors of occupants, and the method proposed in this study will also be applicable to other regions.

Integration of BIM and Energy Consumption Modelling for Manufacturing Prefabricated Components: A Case Study in China

Prefabrication is undergoing significant growth in the building and construction sector in China. Meanwhile, the integrated measurement and reduction solution for energy consumption is quickly becoming an important target of scientific efforts and research, especially in prefabricated buildings. Embedded energy consumption requirements can potentially be reduced during the manufacturing process of prefabricated components: the opportunity for its reduction can be significant. This paper aims to propose a BIM and LCA integrated approach that enables the energy consumption of prefabricated components in the manufacturing stage to be extracted and calculated in order to obtain a clear, precise understanding of the energy consumption management in prefabrication. To enhance the energy consumption description of prefabricated components, this paper clarifies the contents of energy consumption inventory and related calculation formula in raw material extraction and production, material transportation, and factorized production of prefabricated components, respectively. Specifically, considering that current Industry Foundation Classes (IFC) standard does not accommodate entities with energy consumption information or relationships, it applies the IFC extension method to connect the energy consumption inventory with the BIM model. The proposed approach is applied and verified in a case study from China. This study reveals that energy consumption control should be investigated in the element level considering the differences of various prefabricated components’ requirements in the manufacturing stage.

Analysis of Annual Energy Use Intensities (EUIs) by End-Use in Apartment Units According to Stratification Variables (2017 – 2018)

To achieve the goal of reducing greenhouses gases, many countries have recognized the importance of energy conservation in the building sector, and such countries are considerably strengthening their building energy conservation policies by reinforcing design standards, encouraging remodeling, and requiring zero-energy construction. In order to effectively strengthen these policies, it is necessary to provide information concerning energy consumption in the building sector to ensure the technical and economic feasibility of policies in the marketplace, and to allow building users and policy makers to easily access and understand energy consumption characteristics. It is important to provide information that allows people to effectively understand the state of energy consumption by end-use (space heating, space cooling, domestic hot water, etc.) as part of the creation of a concrete plan for energy reduction that incorporates various service systems and is familiar to people. This is because providing such information plays an important role in establishing concrete policies and encouraging voluntary energy performance improvements by building occupants. South Korea operates the Korea Energy Statistics Information System (KESIS) and the information provided by this type of information system consists mainly of energy consumption by energy source (electricity, gas, etc.), and such systems remain inadequate for providing effective information on energy consumption and energy use intensity (EUI) by end-use (space heating, space cooling, domestic hot water, etc.) as part of the creation of a concrete plan for energy conservation. In order to accurately provide energy consumption information by end-use rather than limit the information to mainly consumption corresponding to energy sources, in this study, measurement systems were installed in 2014 ~ 2016 based on the overall sampling designs of previous studies for apartment units, classifications, measurement and data gathering methods for energy consumption by end-use. The annual statistical values for EUI by end-use were collected from the measurement data for 71 sample apartment units from May 2017 to April 2018. This data was calculated and analyzed using stratification variable levels for completion year, supplied area, and the heat source type.

A New Non-Intrusive Load Monitoring Algorithm Based on Event Matching

Non-intrusive load monitoring is an attractive approach to obtain the energy consumption information without monitoring every device in a building. The main difficulty in the problem is to disaggregate the total power into the power used by specific devices. To solve the problem, this paper proposes an event-based scheme, in which the events corresponding to the appliance on or off are detected and then the events corresponding to the operation of a single device are matched. Finally, the events belonging to a single device are clustered so that the total power is disaggregated into the single device. The experiments are tested on the REDD dataset and the results are compared with the available algorithm, which verifies the validity of the algorithm.

Disrupted Balance of Long- and Short-Range Functional Connectivity Density in Type 2 Diabetes Mellitus: A Resting-State fMRI Study.

Previous studies have shown that type 2 diabetes mellitus (T2DM) can accelerate the rate of cognitive decline in patients. As an organ with high energy consumption, the brain network balances between lower energy consumption and higher information transmission efficiency. However, T2DM may modify the proportion of short- and long-range connections to adapt to the inadequate energy supply and to respond to various cognitive tasks under the energy pressure caused by homeostasis alterations in brain glucose metabolism. On the basis of the above theories, this study determined the abnormal functional connections of the brain in 32 T2DM patients compared with 32 healthy control (HC) subjects using long- and short-range functional connectivity density (FCD) analyses with resting-state fMRI data. The cognitive function level in these patients was also evaluated by neuropsychological tests. Moreover, the characteristics of abnormal FCD and their relationships with cognitive impairment were investigated in T2DM patients. Compared with the HC group, T2DM patients exhibited decreased long-range FCD in the left calcarine and left lingual gyrus and increased short-range FCD in the right angular gyrus and medial part of the left superior frontal gyrus (p < 0.05, Gaussian random-field theory corrected). In T2DM patients, the FCD z scores of the medial part of the left superior frontal gyrus were negatively correlated with the time cost in part B of the Trail Making Test (ρ = -0.422, p = 0.018). In addition, the FCD z scores of the right angular gyrus were negatively correlated with the long-term delayed recall scores of the Auditory Verbal Learning Test (ρ = -0.356, p = 0.049) and the forward scores of the Digital Span Test (ρ = -0.373, p = 0.039). T2DM patients exhibited aberrant long-range and short-range FCD patterns, which may suggest brain network reorganization at the expense of losing the integration of long-range FCD to adapt to the deficiency in energy supply. These changes may be associated with cognitive decline in T2DM patients.

Privacy-Aware Smart Metering: Progress and Challenges

The next-generation energy network, the so-called smart grid (SG), promises a tremendous increase in efficiency, safety and flexibility of managing the electricity grid as compared to the legacy energy network. This is needed today more than ever, as the global energy consumption is growing at an unprecedented rate, and renewable energy sources have to be seamlessly integrated into the grid to assure a sustainable human development. Smart meters (SMs) are among the crucial enablers of the SG concept; they supply accurate high-frequency information about users' household energy consumption to a utility provider, which is essential for time of use pricing, rapid fault detection, energy theft prevention, while also providing consumers with more flexibility and control over their consumption. However, highly accurate and granular SM data also poses a threat to consumer privacy as non-intrusive load monitoring techniques enable a malicious attacker to infer many details of a user's private life. This article focuses on privacy-enhancing energy management techniques that provide accurate energy consumption information to the grid operator, without sacrificing consumer privacy. In particular, we focus on techniques that shape and modify the actual user energy consumption by means of physical resources, such as rechargeable batteries, renewable energy sources or demand shaping. A rigorous mathematical analysis of privacy is presented under various physical constraints on the available physical resources. Finally, open questions and challenges that need to be addressed to pave the way to the effective protection of users' privacy in future SGs are presented.

Exploration of power consumption monitoring based on Internet of things

SummaryTo solve the problem that the general power consumption monitoring system only realizes the total amount statistics of energy consumption information, but there is no statistical analysis of energy consumption in items and classification as well as energy saving control, a solution scheme to the power consumption monitoring and energy saving management system based on the Internet of things technology was proposed. Based on the research on the Internet of things architecture and combined with the actual functional requirements of the project, the four‐level architecture of the Internet of things was designed. The database technology was used to achieve an expert system for enterprise energy saving control based on relational model. Finally, according to the actual project, the engineering application of the system was introduced. The research showed that the system proposed could effectively monitor the power consumption of enterprises and analyze and manage energy consumption. To sum up, the power consumption can be well monitored by applying Internet of things.

Multi-source data analytics for AM energy consumption prediction

The issue of Additive Manufacturing (AM) system energy consumption attracts increasing attention when many AM systems are applied in digital manufacturing systems. Prediction and reduction of the AM energy consumption have been established as one of the most crucial research targets. However, the energy consumption is related to many attributes in different components of an AM system, which are represented as multiple source data. These multi-source data are difficult to integrate and to model for AM energy consumption due to its complexity. The purpose of this study is to establish an energy value predictive model through a data-driven approach. Owing to the fact that multi-source data of an AM system involves nested hierarchy, a hybrid approach is proposed to tackle the issue. This hybrid approach incorporates clustering techniques and deep learning to integrate the multi-source data that is collected using the Internet of Things (IoT), and then to build the energy consumption prediction model for AM systems. This study aims to optimise the AM system by exploiting energy consumption information. An experimental study using the energy consumption data of a real AM system shows the merits of the proposed approach. Results derived using this hybrid approach reveal that it outperforms pre-existing approaches.

On Multiple Access of a Large Number of Machine-Type Devices in Cellular Networks

Introduction: Machine-to-machine communication assumes data transmission from various wireless devices and attracts attention of cellular operators. In this regard, it is crucial to recognize and control overload situations when a large number of such devices access the network over a short time interval.Purpose:Analysis of the radio network overload at the initial network entry stage in a machine-to-machine communication system.Results: A system is considered that features multiple smart meters, which may report alarms and autonomously collect energy consumption information. An analytical approach is proposed to study the operation of a large number of devices in such a system as well as model the settings of the random-access protocol in a cellular network and overload control mechanisms with respect to the access success probability, network access latency, and device power consumption. A comparison between the obtained analytical results and simulation data is also offered.

Implementation Strategy of Convolution Neural Networks on Field Programmable Gate Arrays for Appliance Classification Using the Voltage and Current (V-I) Trajectory

Specific information about types of appliances and their use in a specific time window could help determining in details the electrical energy consumption information. However, conventional main power meters fail to provide any specific information. One of the best ways to solve these problems is through non-intrusive load monitoring, which is cheaper and easier to implement than other methods. However, developing a classifier for deducing what kind of appliances are used at home is a difficult assignment, because the system should identify the appliance as fast as possible with a higher degree of certainty. To achieve all these requirements, a convolution neural network implemented on hardware was used to identify the appliance through the voltage and current (V-I) trajectory. For the implementation on hardware, a field programmable gate array (FPGA) was used to exploit processing parallelism in order to achieve optimal performance. To validate the design, a publicly available Plug Load Appliance Identification Dataset (PLAID), constituted by 11 different appliances, has been used. The overall average F-score achieved using this classifier is 78.16% for the PLAID 1 dataset. The convolution neural network implemented on hardware has a processing time of approximately 5.7 ms and a power consumption of 1.868 W.

Three-Dimensional Visualization Solution to Building-Energy Diagnosis for Energy Feedback

Owing to the large ratio of consumption in the building sector, energy-saving strategies are required. Energy feedback is an energy-saving strategy that prompts consumers to change their energy-consumption behaviors. The strategy has been principally focused on providing energy-consumption information. However, the realization of energy savings using only consumption information remains limited. In this paper, a building-energy, three-dimensional (3D) visualization solution is thus proposed. The aim is to determine if the building manager will replace the facility after our recommendation to improve the building-energy efficiency derived from the energy information is given. This solution includes the process of diagnosing a building and providing a prediction of energy requirements if a building improvement effort is undertaken. Accurate diagnostic information is provided by real-time measurement data from sensors and building models using a close-range photogrammetry method, without depending on blueprints. The information is provided by employing visualization effects to increase the energy-feedback efficiency. The proposed strategy is implemented on two testbeds, and building diagnostics are performed accordingly. For the first testbed, the predicted energy improvement amount resulting from the facility upgrade is provided. The second testbed is provided with a 3D visualization of the energy information. The predicted value of energy improvement was derived from the improvement plan through energy diagnosis in each testbed as about 30% and as about 28%, respectively. Unlike existing systems, which provide only ambiguous data that lack quantitative information, this study is meaningful because it provides energy information with the aid of visualization effects before and after building improvements.