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.

Online identification of appliances from power consumption data collected by smart meters

2015 ◽  
Vol 19 (2) ◽  
pp. 463-473 ◽  
Author(s):  
M. Rodríguez Fernández ◽  
I. González Alonso ◽  
E. Zalama Casanova
Keyword(s):  
Power Consumption ◽  
Smart Meters ◽  
Online Identification ◽  
Consumption Data

scholarly journals A Practical Load Disaggregation Approach for Monitoring Industrial Users Demand with Limited Data Availability

Energies ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 4880
Author(s):  
Sara Tavakoli ◽  
Kaveh Khalilpour
Keyword(s):  
Large Scale ◽  
Service Providers ◽  
Low Cost ◽  
Estimation Error ◽  
Data Availability ◽  
Smart Meters ◽  
Industrial Sectors ◽  
Load Monitoring ◽  
Load Disaggregation ◽  
Electrical Loads

The emergence of smart sensors has had a significant impact on the utility industry. In particular, it has made the planning and implementation of demand-side management (DSM) programmes easier. Nevertheless, for various reasons, some users may not implement smart meters for load monitoring. This paper addresses such cases, particularly large-scale industrial users, which, despite heavy electrical loads coming from many different processes, implement only simple energy measuring equipment for billing purposes. This necessitates the utilisation of novel methodologies for load disaggregation, often referred to as nonintrusive load monitoring (NILM). The availability of such tools can create multifold benefits for industrial park management, utility service providers, regulators, and policymakers. Here, we introduce an optimisation algorithm for nonintrusive load disaggregation that is low-cost, speedy, and acceptably accurate. As a case study, we used real network data of three industrial sectors: food processing, stonecutting, and glassmaking. For all cases, the optimisation framework developed a desegregated profile and estimated the load with an error of less than 5%. For non-workdays, given the higher uncertainty for the continuity of different processes, the estimation error was higher but still in an acceptable range of around 3.63–15.09% with an average of 8.10%.

scholarly journals Thwarting Nonintrusive Occupancy Detection Attacks from Smart Meters

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Dapeng Man ◽  
Wu Yang ◽  
Shichang Xuan ◽  
Xiaojiang Du
Keyword(s):  
Power Consumption ◽  
Real Time ◽  
Real World ◽  
Side Effect ◽  
Smart Meter ◽  
Smart Meters ◽  
Occupancy Detection ◽  
Load Monitoring ◽  
Privacy Issues ◽  
Statistical Metrics

Occupancy information is one of the most important privacy issues of a home. Unfortunately, an attacker is able to detect occupancy from smart meter data. The current battery-based load hiding (BLH) methods cannot solve this problem. To thwart occupancy detection attacks, we propose a framework of battery-based schemes to prevent occupancy detection (BPOD). BPOD monitors the power consumption of a home and detects the occupancy in real time. According to the detection result, BPOD modifies those statistical metrics of power consumption, which highly correlate with the occupancy by charging or discharging a battery, creating a delusion that the home is always occupied. We evaluate BPOD in a simulation using several real-world smart meter datasets. Our experiment results show that BPOD effectively prevents the threshold-based and classifier-based occupancy detection attacks. Furthermore, BPOD is also able to prevent nonintrusive appliance load monitoring attacks (NILM) as a side-effect of thwarting detection attacks.

Lifestyle Analysis from Household Electricity Consumption Data

2020 ◽  
Vol 24 (2) ◽  
pp. 214-220
Author(s):  
Yu Shirai ◽  
Shunichi Hattori ◽  
Yasufumi Takama ◽  
◽  
Keyword(s):  
Quality Of Life ◽  
Electricity Consumption ◽  
Ground Truth ◽  
Smart Meters ◽  
Positive Correlation ◽  
Aging Society ◽  
Consumption Data ◽  
Target Data

This paper aims to analyze the lifestyle of residents from household electricity consumption data. Improving QOL (Quality of Life) of elderlies has attracted attention in a super-aging society. It is known that the lifestyle of a person directly affects his / her health and QOL. Therefore, understanding a lifestyle is expected to be useful for providing various support for improving QOL, such as recommending adequate actions and daily habit. As a means for understanding residents’ lifestyle, this paper focuses on household electricity consumption data, which gets to be available with the spread of smart meters. The analysis is conducted by estimating the time of taking essential actions such as wake up and eating. As the target data has no ground truth, this paper also shows the result of an experiment on the detection of the essential actions. The analysis results reveal several findings which could be useful for improving QOL, such as positive correlation between regularity of dinner time and bedtime.

scholarly journals MORED: A Moroccan Buildings’ Electricity Consumption Dataset

Energies ◽  
2020 ◽  
Vol 13 (24) ◽  
pp. 6737
Author(s):  
Mohamed Aymane Ahajjam ◽  
Daniel Bonilla Licea ◽  
Chaimaa Essayeh ◽  
Mounir Ghogho ◽  
Abdellatif Kobbane
Keyword(s):  
Electricity Consumption ◽  
Ground Truth ◽  
Ground Truth Data ◽  
Disadvantaged Neighborhoods ◽  
Consumption Data ◽  
Load Monitoring ◽  
Open Datasets

This paper consists of two parts: an overview of existing open datasets of electricity consumption and a description of the Moroccan Buildings’ Electricity Consumption Dataset, a first of its kind, coined as MORED. The new dataset comprises electricity consumption data of various Moroccan premises. Unlike existing datasets, MORED provides three main data components: whole premises (WP) electricity consumption, individual load (IL) ground-truth consumption, and fully labeled IL signatures, from affluent and disadvantaged neighborhoods. The WP consumption data were acquired at low rates (1/5 or 1/10 samples/s) from 12 households; the IL ground-truth data were acquired at similar rates from five households for extended durations; and IL signature data were acquired at high and low rates (50 k and 4 samples/s) from 37 different residential and industrial loads. In addition, the dataset encompasses non-intrusive load monitoring (NILM) metadata.

scholarly journals Privacy-Preserving Electricity Billing System Using Functional Encryption

Energies ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 1237
Author(s):  
Jong-Hyuk Im ◽  
Hee-Yong Kwon ◽  
Seong-Yun Jeon ◽  
Mun-Kyu Lee
Keyword(s):  
Power Consumption ◽  
Privacy Preserving ◽  
Prototype System ◽  
Sensitive Information ◽  
Functional Encryption ◽  
Smart Meters ◽  
Privacy Concerns ◽  
Billing System ◽  
Consumption Data ◽  
High Resolution Power

The development of smart meters that can frequently measure and report power consumption has enabledelectricity providers to offer various time-varying rates, including time-of-use and real-time pricing plans. High-resolution power consumption data, however, raise serious privacy concerns because sensitive information regarding an individual’s lifestyle can be revealed by analyzing these data. Although extensive research has been conducted to address these privacy concerns, previous approaches have reduced the quality of measured data. In this paper, we propose a new privacy-preserving electricity billing method that does not sacrifice data quality for privacy. The proposed method is based on the novel use of functional encryption. Experimental results on a prototype system using a real-world smart meter device and data prove the feasibility of the proposed method.

scholarly journals NILM techniques applied to a real-time monitoring system of the electricity consumption

ACTA IMEKO ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 139
Author(s):  
Barbara Cannas ◽  
Sara Carcangiu ◽  
Daniele Carta ◽  
Alessandra Fanni ◽  
Carlo Muscas ◽  
...  
Keyword(s):  
Power Consumption ◽  
Monitoring System ◽  
Event Detection ◽  
Electricity Consumption ◽  
Low Frequency ◽  
Detection Algorithm ◽  
Detection Approach ◽  
Consumption Data ◽  
Load Monitoring ◽  
Using Data

Non-Intrusive Load Monitoring (NILM) allows providing appliance-level electricity consumption information and decomposing the overall power consumption by using simple hardware (one sensor) with a suitable software. This paper presents a low-frequency NILM-based monitoring system suitable for a typical house. The proposed solution is a hybrid event-detection approach including an event-detection algorithm for devices with a finite number of states and an auxiliary algorithm for appliances characterized by complex patterns. The system was developed using data collected at households in Italy and tested also with data from BLUED, a widely used dataset of real-world power consumption data. Results show that the proposed approach works well in detecting and classifying what appliance is working and its consumption in complex household load dataset.

scholarly journals Abnormal electricity detection with hybrid deep neural network model

2018 ◽  
Vol 189 ◽  
pp. 03001
Author(s):  
Jie Liu ◽  
Xiang Cao ◽  
Diangang Wang ◽  
Kejia Pan ◽  
Cheng Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Power Consumption ◽  
Large Scale ◽  
Deep Neural Network ◽  
Electricity Consumption ◽  
The State ◽  
Detection Model ◽  
Gradient Model ◽  
The Arts ◽  
The Difference

This paper tackles a new challenge in abnormal electricity detection: how to promptly detect stealing electricity behavior by a large-scale data from power users. Proposed scheme firstly forms power consumption gradient model by extracting daily trend indicators of electricity consumption, which can exactly reflect the short-term power consumption trend for each user. Furthermore, we design the line-losing model by analyzing the difference between power supplying and actual power consumption. Finally, a hybrid deep neural network detection model is built by combining with the power consumption gradient model and the line-losing model, which can quickly pin down to the abnormal electricity users. Comprehensive experiments are implemented by large-scale user samples from the State Grid Corporation and Tensorflow framework. Extensive results show that comparing with the state-of-the-arts, proposed scheme has a superior detection performance, and therefore is believed to be able to give a better guidance to abnormal electricity detection.

scholarly journals PUMPNET: a deep learning approach to pump operation detection

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Luyao Ma ◽  
Qingyu Meng ◽  
Shirui Pan ◽  
Ariel Liebman
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Power Consumption ◽  
Smart Grids ◽  
Sampling Rate ◽  
High Energy ◽  
Series Data ◽  
Pump Operation ◽  
Consumption Data ◽  
Load Monitoring

AbstractNon-urgent high energy-consuming residential appliances, such as pool pumps, may significantly affect the peak to average ratio (PAR) of energy demand in smart grids. Effective load monitoring is an important step to provide efficient demand response (DR) to PAR. In this paper, we focus on pool pump analytics and present a deep learning framework, PUMPNET, to identify the pool pump operation patterns from power consumption data. Different from conventional time-series based Non-intrusive Load Monitoring (NILM) methods, our approach transfers the time-series data into image-like (date-time matrix) data. Then a U-shaped fully convolutional neural network is developed to detect and segment the image-like data in pixel level for operation detection. Our approach identify whether pool pumps operate given thirty-minute interval aggregated active power consumption data in kilowatt-hours only. Furthermore, the PUMPNET algorithm could identify pool pump operation status with high accuracy in the low-frequency sampling scenario for thousands of household, compared to traditional NILM algorithms which process high sampling rate data and can only apply to limited number of households. Experiments on real-world data validate the promising results of the proposed PUMPNET model.

scholarly journals An Electric Bus Power Consumption Model and Optimization of Charging Scheduling Concerning Multi-External Factors

Energies ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 2060 ◽  
Author(s):  
Yajing Gao ◽  
Shixiao Guo ◽  
Jiafeng Ren ◽  
Zheng Zhao ◽  
Ali Ehsan ◽  
...  
Keyword(s):  
Power Consumption ◽  
Prediction Model ◽  
Large Scale ◽  
Operating Cost ◽  
Electricity Consumption ◽  
External Factors ◽  
Electric Bus ◽  
Consumption Data ◽  
Power Consumption Prediction ◽  
Consumption Prediction

With the large scale operation of electric buses (EBs), the arrangement of their charging optimization will have a significant impact on the operation and dispatch of EBs as well as the charging costs of EB companies. Thus, an accurate grasp of how external factors, such as the weather and policy, affect the electric consumption is of great importance. Especially in recent years, haze is becoming increasingly serious in some areas, which has a prominent impact on driving conditions and resident travel modes. Firstly, the grey relational analysis (GRA) method is used to analyze the various external factors that affect the power consumption of EBs, then a characteristic library of EBs concerning similar days is established. Then, the wavelet neural network (WNN) is used to train the power consumption factors together with power consumption data in the feature library, to establish the power consumption prediction model with multiple factors. In addition, the optimal charging model of EBs is put forward, and the reasonable charging time for the EB is used to achieve the minimum operating cost of the EB company. Finally, taking the electricity consumption data of EBs in Baoding and the data of relevant factors as an example, the power consumption prediction model and the charging optimization model of the EB are verified, which provides an important reference for the optimal charging of the EB, the trip arrangement of the EB, and the maximum profit of the electric public buses.

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