scholarly journals Event Matching Classification Method for Non-Intrusive Load Monitoring

2021 ◽  
Vol 13 (2) ◽  
pp. 693
Author(s):  
Elnaz Azizi ◽  
Mohammad T. H. Beheshti ◽  
Sadegh Bolouki
Keyword(s):  
Energy Management ◽  
Low Frequency ◽  
Training Dataset ◽  
Mode Transition ◽  
Smart Meters ◽  
Available Energy ◽  
Consumption Values ◽  
Load Monitoring ◽  
Transition Times ◽  
Event Based

Nowadays, energy management aims to propose different strategies to utilize available energy resources, resulting in sustainability of energy systems and development of smart sustainable cities. As an effective approach toward energy management, non-intrusive load monitoring (NILM), aims to infer the power profiles of appliances from the aggregated power signal via purely analytical methods. Existing NILM methods are susceptible to various issues such as the noise and transient spikes of the power signal, overshoots at the mode transition times, close consumption values by different appliances, and unavailability of a large training dataset. This paper proposes a novel event-based NILM classification algorithm mitigating these issues. The proposed algorithm (i) filters power signals and accurately detects all events; (ii) extracts specific features of appliances, such as operation modes and their respective power intervals, from their power signals in the training dataset; and (iii) labels with high accuracy each detected event of the aggregated signal with an appliance mode transition. The algorithm is validated using REDD with the results showing its effectiveness to accurately disaggregate low-frequency measured data by existing smart meters.

scholarly journals A Fusion Load Disaggregation Method Based on Clustering Algorithm and Support Vector Regression Optimization for Low Sampling Data

2019 ◽  
Vol 11 (2) ◽  
pp. 51 ◽  
Author(s):  
Quanbo Yuan ◽  
Huijuan Wang ◽  
Botao Wu ◽  
Yaodong Song ◽  
Hejia Wang
Keyword(s):  
Energy Consumption ◽  
Support Vector Regression ◽  
Clustering Algorithm ◽  
Low Frequency ◽  
Total Power ◽  
Support Vector ◽  
Smart Meters ◽  
Load Monitoring ◽  
Load Disaggregation ◽  
Economic Technology

In order to achieve more efficient energy consumption, it is crucial that accurate detailed information is given on how power is consumed. Electricity details benefit both market utilities and also power consumers. Non-intrusive load monitoring (NILM), a novel and economic technology, obtains single-appliance power consumption through a single total power meter. This paper, focusing on load disaggregation with low hardware costs, proposed a load disaggregation method for low sampling data from smart meters based on a clustering algorithm and support vector regression optimization. This approach combines the k-median algorithm and dynamic time warping to identify the operating appliance and retrieves single energy consumption from an aggregate smart meter signal via optimized support vector regression (OSVR). Experiments showed that the technique can recognize multiple devices switching on at the same time using low-frequency data and achieve a high load disaggregation performance. The proposed method employs low sampling data acquired by smart meters without installing extra measurement equipment, which lowers hardware cost and is suitable for applications in smart grid environments.

scholarly journals Frequency Selective Auto-Encoder for Smart Meter Data Compression

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1521
Author(s):  
Jihoon Lee ◽  
Seungwook Yoon ◽  
Euiseok Hwang
Keyword(s):  
Data Compression ◽  
High Frequency ◽  
Spectral Properties ◽  
Situational Awareness ◽  
Time Windows ◽  
Low Frequency ◽  
Smart Meters ◽  
Compression Performance ◽  
Reconstruction Performance ◽  
Frequency Selective

With the development of the internet of things (IoT), the power grid has become intelligent using massive IoT sensors, such as smart meters. Generally, installed smart meters can collect large amounts of data to improve grid visibility and situational awareness. However, the limited storage and communication capacities can restrain their infrastructure in the IoT environment. To alleviate these problems, efficient and various compression techniques are required. Deep learning-based compression techniques such as auto-encoders (AEs) have recently been deployed for this purpose. However, the compression performance of the existing models can be limited when the spectral properties of high-frequency sampled power data are widely varying over time. This paper proposes an AE compression model, based on a frequency selection method, which improves the reconstruction quality while maintaining the compression ratio (CR). For efficient data compression, the proposed method selectively applies customized compression models, depending on the spectral properties of the corresponding time windows. The framework of the proposed method involves two primary steps: (i) division of the power data into a series of time windows with specified spectral properties (high-frequency, medium-frequency, and low-frequency dominance) and (ii) separate training and selective application of the AE models, which prepares them for the power data compression that best suits the characteristics of each frequency. In simulations on the Dutch residential energy dataset, the frequency-selective AE model shows significantly higher reconstruction performance than the existing model with the same CR. In addition, the proposed model reduces the computational complexity involved in the analysis of the learning process.

scholarly journals Turbulent-driven low-frequency shearedE×Bflows as the trigger for the H-mode transition

2013 ◽  
Vol 53 (7) ◽  
pp. 073053 ◽  
Author(s):  
G.R. Tynan ◽  
M. Xu ◽  
P.H. Diamond ◽  
J.A. Boedo ◽  
I. Cziegler ◽  
...  
Keyword(s):  
Low Frequency ◽  
Mode Transition

scholarly journals Non-intrusive load monitoring based on low frequency active power measurements

AIMS Energy ◽  
2016 ◽  
Vol 4 (3) ◽  
pp. 414-443 ◽  
Author(s):  
Chinthaka Dinesh ◽  
◽  
Pramuditha Perera ◽  
Roshan Indika Godaliyadda ◽  
Mervyn Parakrama B. Ekanayake ◽  
...  
Keyword(s):  
Low Frequency ◽  
Active Power ◽  
Load Monitoring ◽  
Power Measurements

Optimal energy management strategy for fuel cell/battery/supercapacitor vehicles using wavelet transform and equivalent consumption minimization strategy

2021 ◽  
pp. 095440702110697
Author(s):  
Pengfei Zou ◽  
Fazhan Tao ◽  
Zhumu Fu ◽  
Pengju Si ◽  
Chao Ma
Keyword(s):  
Wavelet Transform ◽  
Fuel Cell ◽  
Energy Management ◽  
Fuel Economy ◽  
Management Strategy ◽  
Low Frequency ◽  
Energy Management Strategy ◽  
Power Sources ◽  
Optimal Energy ◽  
Optimal Energy Management

In this paper, the hybrid electric vehicle is equipped with fuel cell/battery/supercapacitor as the research object, the optimal energy management strategy (EMS) is proposed by combining wavelet transform (WT) method and equivalent consumption minimization strategy (ECMS) for reducing hydrogen consumption and prolonging the lifespan of power sources. Firstly, the WT method is employed to separate power demand of vehicles into high-frequency part supplied by supercapacitor and low-frequency part allocated to fuel cell and battery, which can effectively reduce the fluctuation of fuel cell and battery to prolong their lifespan. Then, considering the low-frequency power, the optimal SOC of battery is used to design the equivalent factor of the ECMS method to improve the fuel economy. The proposed hierarchical EMS can realize a trade-off between the lifespan of power sources and fuel economy of vehicles. Finally, the effectiveness of the proposed EMS is verified by ADVISOR, and comparison results are given compared with the traditional ECMS method and ECMS combining the filter.

A combined effective time series model based on clustering and whale optimization algorithm for forecasting smart meters electricity consumption

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Medhat Abd el Azem El Sayed Rostum ◽  
Hassan Mohamed Mahmoud Moustafa ◽  
Ibrahim El Sayed Ziedan ◽  
Amr Ahmed Zamel
Keyword(s):  
Optimization Algorithm ◽  
Prediction Accuracy ◽  
Electricity Consumption ◽  
Whale Optimization Algorithm ◽  
Computational Time ◽  
Smart Meters ◽  
Content Type ◽  
Individual Level ◽  
Consumption Values ◽  
Whale Optimization

Purpose The current challenge for forecasting smart meters electricity consumption lies in the uncertainty and volatility of load profiles. Moreover, forecasting the electricity consumption for all the meters requires an enormous amount of time. Most papers tend to avoid such complexity by forecasting the electricity consumption at an aggregated level. This paper aims to forecast the electricity consumption for all smart meters at an individual level. This paper, for the first time, takes into account the computational time for training and forecasting the electricity consumption of all the meters. Design/methodology/approach A novel hybrid autoregressive-statistical equations idea model with the help of clustering and whale optimization algorithm (ARSEI-WOA) is proposed in this paper to forecast the electricity consumption of all the meters with best performance in terms of computational time and prediction accuracy. Findings The proposed model was tested using realistic Irish smart meters energy data and its performance was compared with nine regression methods including: autoregressive integrated moving average, partial least squares regression, conditional inference tree, M5 rule-based model, k-nearest neighbor, multilayer perceptron, RandomForest, RPART and support vector regression. Results have proved that ARSEI-WOA is an efficient model that is able to achieve an accurate prediction with low computational time. Originality/value This paper presents a new hybrid ARSEI model to perform smart meters load forecasting at an individual level instead of an aggregated one. With the help of clustering technique, similar meters are grouped into a few clusters from which reduce the computational time of the training and forecasting process. In addition, WOA improves the prediction accuracy of each meter by finding an optimal factor between the average electricity consumption values of each cluster and the electricity consumption values for each one of its meters.

scholarly journals Analysis of the possibility of utilization of waste heat from the marine engine in the ORC power plant

2018 ◽  
Vol 70 ◽  
pp. 01006
Author(s):  
Radomir Kaczmarek ◽  
Aleksander Stachel
Keyword(s):  
Energy Management ◽  
Preliminary Analysis ◽  
Waste Heat ◽  
Electrical Power ◽  
Operating Parameters ◽  
Marine Engine ◽  
Available Energy ◽  
Marine Diesel ◽  
Main Engine ◽  
Cycle Efficiency

Modern marine diesel engines are characterized by ~50% conversion of chemical energy of fuel into useful work. The remaining amount of heat is lost to the environment as the so-called waste heat, whereby its part can be used in various marine heating installations. In the ship's engine the biggest source of waste heat, potentially useful for further use, both due to the amount of available energy and its quality, are exhaust emissions from the main engine. Taking into account the need for rational energy management, the paper presents a preliminary analysis and assessment of the possibility of using the heat of hot exhaust gases to generate electricity in the so-called low-temperature ORC installation. Based on the available data, the operation of ORC installation has been evaluated, taking into account the influence of engine operating parameters on the obtained electrical power and the cycle efficiency. The aim of the analysis was to demonstrate that there is a real possibility to use the waste heat from marine engine exhausts to drive the ORC and generate electricity.

scholarly journals Multi-State Energy Classifier to Evaluate the Performance of the NILM Algorithm

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5236 ◽  
Author(s):  
Sanket Desai ◽  
Rabei Alhadad ◽  
Abdun Mahmood ◽  
Naveen Chilamkurti ◽  
Seungmin Rho
Keyword(s):  
Power Consumption ◽  
Large Scale ◽  
Single Point ◽  
Ground Truth ◽  
Significant Rise ◽  
The State ◽  
Smart Meters ◽  
Energy Estimation ◽  
Consumption Data ◽  
Load Monitoring

With the large-scale deployment of smart meters worldwide, research in non-intrusive load monitoring (NILM) has seen a significant rise due to its dual use of real-time monitoring of end-user appliances and user-centric feedback of power consumption usage. NILM is a technique for estimating the state and the power consumption of an individual appliance in a consumer’s premise using a single point of measurement device such as a smart meter. Although there are several existing NILM techniques, there is no meaningful and accurate metric to evaluate these NILM techniques for multi-state devices such as the fridge, heat pump, etc. In this paper, we demonstrate the inadequacy of the existing metrics and propose a new metric that combines both event classification and energy estimation of an operational state to give a more realistic and accurate evaluation of the performance of the existing NILM techniques. In particular, we use unsupervised clustering techniques to identify the operational states of the device from a labeled dataset to compute a penalty threshold for predictions that are too far away from the ground truth. Our work includes experimental evaluation of the state-of-the-art NILM techniques on widely used datasets of power consumption data measured in a real-world environment.

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