Power Consumption Patterns Research Articles

An algorithm for detecting abnormal electricity mode of power users

In order to reduce the non-technical loss and reduce the operating cost of the power company, an abnormal power consumption detection algorithm is proposed. The algorithm includes feature extraction, principal component analysis, grid processing, local outliers, and so on. Firstly, we extract several feature quantities that characterize the user's power consumption pattern, and map the X users to the two-dimensional plane by principal component analysis. Data visualization and easy to calculate local outliers, and grid processing techniques to filter out data points in low density regions. The algorithm is used to reduce the number of training samples in the power user data set, and to output the anomalies and probabilities of all users' behavior. The experimental results show that the use of the sorting only need to detect the anomaly of a few users can find a large number of abnormal users, significantly improve the efficiency of the algorithm.

Cluster Survival Model of Concept Drift in Load Profile Data

An accurate scenario of customer’s power consumption patterns is a worthwhile asset for electricity provider. This paper proposes a cluster survival model of concept drift in load profile data. The cluster survival model of concept drift retrieves the dynamic behaviors of the clusters over time. We formulate a new data stream clustering algorithm, I-niceStream, which identifies the number of clusters and initial cluster centers automatically for producing the clustering results. We derive a modified Kullback–Leibler divergence for computing the concept drift scores from the clustering results. The concept drift scores are used to estimate the related clusters and the clustering patterns. The survival model categorizes the clustering patterns into sustaining, fading, and emerging types. Experiments were conducted on both synthetic datasets and real-world load profile dataset collected from different factories at Guangdong province in China. Experimental results show that the cluster survival model is able to identify the clustering patterns effectively from load profile data stream. The I-niceStream algorithm significantly outperformed three state-of-the-art algorithms in clustering accuracy on synthetic stream datasets.

Modeling of Electric Demand for Sustainable Energy and Management in India Using Spatio-Temporal DMSP-OLS Night-Time Data.

Changes in the pattern of electric power consumption in India have influenced energy utilization processes and socio-economic development to greater extent during the last few decades. Assessment of spatial distribution of electricity consumption is, thus, essential for projecting availability of energy resource and planning its infrastructure. This paper makes an attempt to model the future electricity demand for sustainable energy and its management in India. The nighttime light database provides a good approximation of availability of energy. We utilized defense meteorological satellite program-operational line-scan system (DMSP-OLS) nighttime satellite data, electricity consumption (1993-2013), gross domestic product (GDP) and population growth to construct the model. We also attempted to examine the sensitiveness of electricity consumption to GDP and population growth. The results revealed that the calibrated DMSP and model has provided realistic information on the electric demand with respect to GDP and population, with a better accuracy of r 2 = 0.91. The electric demand was found to be more sensitive to GDP (r = 0.96) than population growth (r = 0.76) as envisaged through correlation analysis. Hence, the model proved to be useful tool in predicting electric demand for its sustainable use and management.

A Disturbance-Free Built-In Self-Test and Diagnosis Technique for DC-DC Converters

Complex electronic systems include multiple power domains and drastically varying dynamic power consumption patterns, requiring the use of multiple power conversion and regulation units. High-frequency switching converters have been gaining prominence in the DC-DC converter market due to their high efficiency and smaller form factor. Unfortunately, they are also subject to higher process variations, and faster in-field degradation, jeopardizing stable operation of the power supply. This article presents a technique to track changes in the dynamic loop characteristics of DC-DC converters without disturbing the normal mode of operation using a white noise–based excitation and correlation. Using multiple points for injection and analysis, we show that the degraded part can be diagnosed to take remedial action. White noise excitation is generated via a pseudo-random disturbance at reference, load current, and pulse-width modulation (PWM) nodes of the converter with the test signal energy being spread over a wide bandwidth, without significantly affecting the converter noise and ripple floor. The impulse response is extracted by correlating the random input sequence with the disturbed output generated. Test signal analysis is achieved by correlating the pseudo-random input sequence with the output response and thereby accumulating the desired behavior over time and pulling it above the noise floor of the measurement set-up. An off-the-shelf power converter, LM27402, is used as the device-under-test (DUT) for experimental verification. Experimental results show that the proposed technique can estimate converter natural frequency and quality factor ( Q -factor) within ±2.5% and ±0.7% error margin respectively, over changes in load inductance and capacitance. For the diagnosis purpose, a measure of inductor's DC resistance (DCR) value, which is the inductor's series resistance and indicative of the degradation in inductor's Q -factor, is estimated within less than ±1.6% error margin.

Economic Assessment and Policy Analysis on Wind Energy Development in China

Considering rapid development and emerging new challenges of China's wind energy development, this paper summarizes and analyzes five critical problems. First, according to an economic potential assessment, if the feed-in tariff is set 0.60 Yuan/kWh, the economic potential of wind electricity of China will reach 6.63[Formula: see text]PWh, it could meet as much as 2/3 of total power demand by 2030, thus the targeted wind power proportion in future high renewable energy penetration can be achieved. Second, wind energy is abundant but little consumed in Northwest, Northeast and North China while it is insufficient in South and East of China, where grid-connection condition is well. Due to this reverse distribution pattern of wind power endowment and consumption in China, grid-connection transmission must be strengthened in the future, particularly the West–East and North–South power transmission projects must be pushed forward. Third, to address the issue of increasing wind energy curtailment in Northeast, Northwest and North China, stimulating local consumption of wind power by multiple ways is a possible solution in the short term, however, in the medium and long run, it is necessary to accelerate the construction of Ultra-High Voltage power grid in order to expand power transmission to other provinces and regions. Fourth, a new electricity market mechanism should be established as the fundamental measure of promoting new energy development, while green certificate trading market and carbon trading market will form the basis for such market mechanism in the long run. Fifth, though wind power development can bring multiple benefits to economy, employment, and environment, large-scale wind power development will, to some extent, affect local and regional climate. As global warming continues, the wind power resource distribution of China and the world may experience significant changes.

Persistent and adaptive power system for solar powered sensors of Internet of Things (IoT)

Powering remote sensors with real-time communication persistently is a key enabling challenge for the internet of things (IoT). Energy harvesting from the environment, energy storage, and power consumption of such sensors need to be jointly considered and duly optimized for the technology to move toward power autonomy and miniaturization. Using photovoltaic (PV) technology, the sun can provide enough power for devices operating outdoors to ensure persistent operation over years without the need to ever access them.High efficiency solar cells feature high power generation densities that utilize the ever present solar power in outdoor environments. The main challenge in designing PV powered devices for predictable operation is The intermittent nature of the solar resource is: the PV panel needs to be combined with energy storage, and the whole device must be designed and optimized specifically for persistent operation outdoors. As presented in our previous work [1], the design must consider (i) the changes (daily, seasonal, and due to cloud cover) in the solar insolation, (ii) the solar cell characteristics, and (iii) the power requirement of the sensing and communicating module. From these characteristics, we estimate the necessary storage capacity to reliably power the device over several years. The system size is then optimized for minimum cost or volume.In this paper, we present how an adaptive power consumption pattern affects the dimensions of solar cell and battery, and the optimal cost of the system. In our design approach, we constrain the design of the adaptive power system based on the available power as well as the expected system performance, using the rate of acquired data from the sensor as a measure of performance. This means the performance - or the power consumption profile - changes based on availability of energy source. We consider different scenarios in which the performance is adapted to the remaining storage and to the recent solar insolation, and discuss the design parameters and constraints in each scenario.

CPLP: An algorithm for tracking the changes of power consumption patterns in load profile data over time

In this paper, we propose a novel algorithm for tracking the Changes of Patterns in Load Profile (CPLP) data of factories. CPLP consists of two stages. The first stage is to cluster the load profiles in each time window and use the clusters to model the power consumption patterns. We propose a new ensemble clustering method to cluster the load profiles in consecutive time windows. It uses a hierarchical binary k-means algorithm to generate component clusterings and a new objective function to ensemble them to produce the final clustering. The second stage is to track the changes of patterns along the time windows. We propose a new method to detect the change of clusters from one window to the next one by using the distribution models of two related clusters in two neighboring windows. By using this method, we can link the clusters in the sequence of time windows to track the patterns. Experiments on synthetic and real-world load profile data have shown that the proposed algorithm was able to track the changes of power consumption patterns of different factory groups and identify the period of significant change, which are very useful for the smart grid applications.

Impacts of optimization strategies on performance, power/energy consumption of a GPU based parallel reduction

In the era of modern high performance computing, GPUs have been considered an excellent accelerator for general purpose data-intensive parallel applications. To achieve application speedup from GPUs, many of performance-oriented optimization techniques have been proposed. However, in order to satisfy the recent trend of power and energy consumptions, power/energy-aware optimization of GPUs needs to be investigated with detailed analysis in addition to the performance-oriented optimization. In this work, in order to explore the impact of various optimization strategies on GPU performance, power and energy consumptions, we evaluate performance and power/energy consumption of a well-known application running on different commercial GPU devices with the different optimization strategies. In particular, in order to see the more generalized performance and power consumption patterns of GPU based accelerations, our evaluations are performed with three different Nvdia GPU generations (Fermi, Kepler and Maxwell architectures), various core clock frequencies and memory clock frequencies. We analyze how a GPU kernel execution is affected by optimization and what GPU architectural factors have much impact on its performance and power/energy consumption. This paper also categorizes which optimization technique primarily improves which metric (i.e., performance, power or energy efficiency). Furthermore, voltage frequency scaling (VFS) is also applied to examine the effect of changing a clock frequency on these metrics. In general, our work shows that effective GPU optimization strategies can improve the application performance significantly without increasing power and energy consumption.

Evaluation of the energy performance of variable refrigerant flow systems using dynamic energy benchmarks based on data mining techniques

The variable refrigerant flow (VRF) system has extremely different energy performance at various operation conditions. Its power consumption is inconsistent even under the steady operation condition. In order to accurately evaluate the VRF system’s dynamic energy performance, this study proposed a data-mining-based method to benchmark and assess its energy uses. The correlation analysis is used for key factors selection and the interquartile range rule is employed to remove outliers of the database. In addition, the power consumption patterns are classified using decision tree (DT) method. The classification results are validated by the ANOVA analysis and post hoc test. Nine energy benchmarks are established based on the classified power consumption patterns. Moreover, an energy consumption rating system is established to provide quantitative assessment on the power consumption of the VRF system. A case study is conducted by comparatively analyzing the energy performance of the VRF system at multiple refrigerant charge fault cases. Results show that both the PLR and OT significantly affected the power consumption of the VRF system. However, the degree to which the refrigerant charge fault affects system power consumption varies with the power consumption patterns. For different patterns, the power consumptions of the VRF system were either lower, higher or similar to each other at various RCLs. Results also suggest that the energy benchmarking process provide reasonable classification criteria, and the grading process provide quantitative assessment on the energy consumption. Therefore, the proposed dynamic energy benchmarks are reliable and reasonable to evaluate the dynamic energy performance of VRF systems.

Demand Side Management in Nearly Zero Energy Buildings Using Heuristic Optimizations

Today’s buildings are responsible for about 40% of total energy consumption and 30–40% of carbon emissions, which are key concerns for the sustainable development of any society. The excessive usage of grid energy raises sustainability issues in the face of global changes, such as climate change, population, economic growths, etc. Traditionally, the power systems that deliver this commodity are fuel operated and lead towards high carbon emissions and global warming. To overcome these issues, the recent concept of the nearly zero energy building (nZEB) has attracted numerous researchers and industry for the construction and management of the new generation buildings. In this regard, this paper proposes various demand side management (DSM) programs using the genetic algorithm (GA), teaching learning-based optimization (TLBO), the enhanced differential evolution (EDE) algorithm and the proposed enhanced differential teaching learning algorithm (EDTLA) to manage energy and comfort, while taking the human preferences into consideration. Power consumption patterns of shiftable home appliances are modified in response to the real-time price signal in order to get monetary benefits. To further improve the cost and user discomfort objectives along with reduced carbon emission, renewable energy sources (RESs) are also integrated into the microgrid (MG). The proposed model is implemented in a smart residential complex of multiple homes under a real-time pricing environment. We figure out two feasible regions: one for electricity cost and the other for user discomfort. The proposed model aims to deal with the stochastic nature of RESs while introducing the battery storage system (BSS). The main objectives of this paper include: (1) integration of RESs; (2) minimization of the electricity bill (cost) and discomfort; and (3) minimizing the peak to average ratio (PAR) and carbon emission. Additionally, we also analyze the tradeoff between two conflicting objectives, like electricity cost and user discomfort. Simulation results validate both the implemented and proposed techniques.

A Joint Data Compression and Encryption Approach for Wireless Energy Auditing Networks

Fine-grained real-time metering is a fundamental service of wireless energy auditing networks, where metering data is transmitted from embedded wireless power meters to gateways for centralized processing, storage, and forwarding. Due to limited meter capability and wireless bandwidth, the increasing sampling rates and network scales needed to support new energy auditing applications pose significant challenges to metering data fidelity and secrecy . This article exploits the compression and encryption properties of compressive sensing (CS) to design a jo<underline>i</underline>nt data <underline>c</underline>ompression and <underline>e</underline>ncryption (JICE) approach that addresses these two challenges simultaneously. Compared with a conventional signal processing pipeline that compresses and encrypts data sequentially, JICE reduces computation and space complexities due to its simple design. It thus leaves more processor time and available buffer space for handling lossy wireless transmissions. Moreover, JICE features an adaptive reconfiguration mechanism that selects the signal representation basis of CS at runtime among several candidate bases to achieve the best fidelity of the recovered data at the gateways. This mechanism enables JICE to adapt to changing power consumption patterns. On a smart plug platform, we implemented JICE and several baseline approaches including downsampling, lossless compression, and the pipeline approach. Extensive testbed experiments show that JICE achieves higher data delivery ratios and lower recovery distortions under a range of realistic settings. In particular, at a meter sampling rate of 8 Hz, JICE increases the number of meters supported by a gateway by 50%, compared with the commonly used pipeline approach, while keeping a signal distortion rate lower than 5%.

Sustainable lifestyle factors influencing industries' electric consumption patterns using Fuzzy logic and DEMATEL: The Nigerian perspective

The methods applied in the attempt to minimize electricity consumption waste as result of consumer's consumption patterns is of crucial concern for government, investors and even consumers. There is a need to identify the potential factors influencing electricity consumption pattern in order to encourage sustainable practices. To examine the electricity consumption pattern and their influencing factors, this study applied Fuzzy-DEMATEL model to examine the sustainable lifestyle factors of electricity consumption pattern in Nigeria. The originality of this paper lies in the proposed approach that integrates fuzzy logic and DEMATEL to evaluate sustainable lifestyles factors that influence industries electric consumption patterns for the first time in Nigeria. Firstly, 24 sustainable lifestyle factors were sourced from literature sources and grouped into 4 clusters of economic, political, environmental and socio-cultural criteria. Two sets of questionnaires were distributed to 30 experts who comprise of managers and senior technicians in Nigerian Power Industry. The results show that the critical factors which influence industries electric consumption patterns are corruption, inadequate planning and design, electricity theft, literacy level, population demography and lifestyle attitude. The implication for decision makers is that in improving power consumption pattern in the Nigerian industries; emphasis should be placed on the critical sustainable lifestyle factors. Thus, corruption should be minimized, planning and design adequately implemented, theft of electricity curbed while improving literacy level and lifestyles of the masses to improve electric consumption patterns.

Forecasting power consumption for higher educational institutions based on machine learning

Electric power consumption is affected by diverse factors. In particular, a university campus, which is one of the highest power consuming institutions, shows a wide variation of electric load depending on time and environment. For stable operation of such institution, reliable electric power supply should be guaranteed. One of possible methods to do that is to forecast the electric load accurately and supply power accordingly. Even though various influencing factors of power consumption have been discovered for educational institutions by analyzing power consumption patterns and usage cases, further studies are required for the quantitative forecasting of their electric load. In this paper, we build a power consumption forecasting model using various machine learning algorithms. To evaluate their effectiveness, we consider four building clusters in a university and collect their power consumption data of 15-min interval over more than one year. For the data, we first extract features based on the periodic characteristic and then perform the principal component analysis and factor analysis for the features. We build two electric load forecasting models using artificial neural network and support vector regression. We evaluate the prediction performance of each forecasting model by 5-fold cross-validation and compare the prediction result to actual electric load. The experimental results show that the two forecasting models can achieve average error rate of 3.46–10 % for all clusters.

Orchestrating an Effective Formulation to Investigate the Impact of EMSs (Energy Management Systems) for Residential Units Prior to Installation

Demand Response (DR) programs under the umbrella of Demand Side Management (DSM) tend to involve end users in optimizing their Power Consumption (PC) patterns and offer financial incentives to shift the load at “low-priced” hours. However, users have their own preferences of anticipating the amount of consumed electricity. While installing an Energy Management System (EMS), the user must be assured that this investment gives optimum comfort of bill savings, as well as appliance utility considering Time of Use (ToU). Moreover, there is a difference between desired load distribution and optimally-scheduled load across a 24-h time frame for lowering electricity bills. This difference in load usage timings, if it is beyond the tolerance level of a user, increases frustration. The comfort level is a highly variable phenomenon. An EMS giving optimum comfort to one user may not be able to provide the same level of satisfaction to another who has different preferences regarding electricity bill savings or appliance utility. Under such a diversity of human behaviors, it is difficult to select an EMS for an individual user. In this work, a numeric performance metric,“User Comfort Level (UCL)”isformulatedonthebasisofuserpreferencesoncostsaving,toleranceindelayregardinguse of an appliance and return of investment. The proposed framework (UCL) allows the user to select an EMS optimally that suits his.her preferences well by anticipating electricity bill reduction, tolerable delay in ToU of the appliance and return on investment. Furthermore, an extended literature analysis is conducted demonstrating generic strategies of EMSs. Five major building blocks are discussed and a comparative analysis is presented on the basis of the proposed performance metric.

Analysis Of Power Consumption Pattern about Elementary․Middle․High School and Study of Application Plan that Surplus Power Produced in Photovoltaic Generating Facilities

The COP21 took place in Paris at the end of 2015 and the Paris Agreement was adopted. all around the world and Korean goverment push ahead for reducing GHG after COP21. Therefore, PV generating facilities installation increases rapidly following policy. Especially, the case of Schools(Elementary. Middle · High school) should annually supply duty renewable energy ratio following ‘The law of New&Renewable energy development, utilization, dissemination, promotion’. If only PV generation should afford renewable energy duty ratio, generated power will pile up in comparison with power consumption during specific time. In this thesis, effective method for using surplus power is proposed. Load profiles and generation profiles of schools were measured to calculate the surplus power. In order to find effective method for using surplus power, most economical electric power transaction method called counter trace is considered among current operating electric power transaction method(counter trace, private PPA, prosumer, etc.).

Real time optimal schedule controller for home energy management system using new binary backtracking search algorithm

In the domestic sector, increased energy consumption of home appliances has become a growing issue. Thus, reducing and scheduling energy usage is the key for any home energy management system (HEMS). To better match demand and supply, many utilities offer residential demand response program to change the pattern of power consumption of a residential customer by curtailing or shifting their energy use during the peak time period. In the present study, real time optimal schedule controller for HEMS is proposed using a new binary backtracking search algorithm (BBSA) to manage the energy consumption. The BBSA gives optimal schedule for home devices in order to limit the demand of total load and schedule the operation of home appliances at specific times during the day. Hardware prototype of smart sockets and graphical user interface software were designed to demonstrate the proposed HEMS and to provide the interface between loads and scheduler, respectively. A set of the most common home appliances, namely, air conditioner, water heater, refrigerator, and washing machine has been considered to be controlled. The proposed scheduling algorithm is applied under two cases in which the first case considers operation at weekday from 4 to 11 pm and the second case considers weekend at different time of the day. Experimental results of the proposed BBSA schedule controller are compared with the binary particle swarm optimization (BPSO) schedule controller to verify the accuracy of the developed controller in the HEMS. The BBSA schedule controller provides better results compared to that of the BPSO schedule controller in reducing the energy consumption and the total electricity bill and save the energy at peak hours of certain loads.

Agent based smart home energy management system

Consumers participation is important for successful implementation of a smart grid. The share of renewable energy sources is ever-increasing Indian power system. As investment required for power generation is quite high, it is important to understand the consumer’s electricity usage pattern for a better planning of resources. It also facilitates energy saving and grid reliability. For a smart grid to operate efficiently and in accordance with the consumer’s preference, real-time power consumption pattern of the consumer is monitored by a Smart Grid Controller for different categories (based on paying capacity). An alert mechanism for voluntary shifting of the consumption to keep the pre-declared budget within limit is suggested.

A Hybrid Signature-based Iterative Disaggregation algorithm for Non-Intrusive Load Monitoring

Information on residential power consumption patterns disaggregated at the single-appliance level is an essential requirement for energy utilities and managers to design customized energy demand management strategies. Non-Intrusive Load Monitoring (NILM) techniques provide this information by decomposing the aggregated electric load measured at the household level by a single-point smart meter into the individual contribution of each end-use. Despite being defined non-intrusive, NILM methods often require an intrusive data sampling process for training purpose. This calibration intrusiveness hampers NILM methods large-scale applications. Other NILM challenges are the limited accuracy in reproducing the end-use consumption patterns and their trajectories in time, which are key to characterize consumers’ behaviors and appliances efficiency, and the poor performance when multiple appliances are simultaneously operated. In this paper we contribute a hybrid, computationally efficient, algorithm for NILM, called Hybrid Signature-based Iterative Disaggregation (HSID), based on the combination of Factorial Hidden Markov Models, which provide an initial approximation of the end-use trajectories, and Iterative Subsequence Dynamic Time Warping, which processes the end-use trajectories in order to match the typical power consumption pattern of each appliance. In order to deal with the challenges posed by intrusive training, a supervised version of the algorithm, requiring appliance-level measurements for calibration, and a semi-supervised version, retrieving appliance-level information from the aggregate smart-metered signal, are proposed. Both versions are demonstrated onto a real-world power consumption dataset comprising five different appliances potentially operated simultaneously. Results show that HSID is able to accurately disaggregate the power consumption measured from a single-point smart meter, thus providing a detailed characterization of the consumers’ behavior in terms of power consumption. Numerical results also demonstrate that HSID is robust with respect to noisy signals and scalable to dataset including a large set of appliances. Finally, the algorithm can be successfully used in non-intrusive experiments without requiring appliance-level measurements, ultimately opening up new opportunities to foster the deployment of large-scale smart metering networks, as well as the design and practical implementation of personalized demand management strategies.

Data partitioning and association mining for identifying VRF energy consumption patterns under various part loads and refrigerant charge conditions

Variable refrigerant flow systems account for a considerable portion of energy consumption in buildings. In order to improve the energy efficiency and estimate the energy saving potentials of variable refrigerant flow systems this study proposes a data mining based method to identify and interpret the power consumption patterns and associations. Two descriptive data mining algorithms, clustering analysis and association rules mining, are used for data partitioning and association mining. The proposed method consists of four phases: data pre-processing, data partitioning, data association mining and knowledge interpretation. Experimental data collected from a tested variable refrigerant flow system in the standard psychrometer testing room are pre-processed and prepared to examine the proposed method. Three time independent influential factors: part load ratio, refrigerant charge level and cooling condition are analyzed. Results show that the method is able to help identify energy consumption patterns and extract energy consumption rules in variable refrigerant flow systems. Three distinct energy consumption patterns are identified: undercharge fault, low and high part load ratio conditions. For compressor operation frequency switch control and refrigerant undercharge patterns, the energy saving potentials could be estimated by making comparisons between energy patterns and rules in a top-down way.

Secure GCM implementation on AVR

In Internet of Things (IoT), sensor devices should deliver the collected sensor data to the server after the data is encrypted. The encrypted data cannot be eavesdropped by adversaries but the side channel information including clock cycles and power consumption patterns during encryption operations can leak the secret information. For this reason, cryptography engineering should prevent potential threats by designing the cryptography functions in secure manner. In this paper, we explore the feasible side channel attacks on cryptography operations, particularly polynomial multiplication for Galois/Counter Mode of operation (GCM). We perform the horizontal Correlation Power Analysis (CPA) on the most well-known Lopez et al.’s polynomial multiplication and successfully extract the secret values from power consumption patterns. In order to prevent proposed attack model, we suggest a masked polynomial multiplication, ensuring a regular and constant-time solution without potential vulnerabilities including Look-up Table (LUT) access and branch statements. With proposed polynomial multiplication, we suggest the secure and efficient implementation of GCM on the low-end embedded processor. Finally, we further explore the long polynomial multiplication for Elliptic Curve Cryptography (ECC) operations. We exploit the combination of Karatsuba algorithm and proposed masked polynomial multiplication, which achieved the practically fast polynomial multiplication on embedded processors.