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%.

The Death of the Legal Profession and the Future of Law

2020 ◽  
Author(s):  
Dan Hunter
Keyword(s):  
Large Scale ◽  
Service Providers ◽  
Low Cost ◽  
Legal Profession ◽  
Legal Service ◽  
Law Firm ◽  
Commercial Practice ◽  
Technology Platforms ◽  
The Law ◽  
And Shifts

This article identifies the five large-scale changes that have happened or are happening to the legal profession: 1. How technology solutions have moved law from a wholly bespoke service to one that resembles an off-the-shelf commodity; 2. How globalisation and outsourcing upend traditional expectations that legal work is performed where the legal need is, and shifts production away from high cost centres to low cost centres; 3. How managed legal service providers – who are low cost, technology-enabled, and process-driven – threaten traditional commercial practice; 4. How technology platforms will diminish the significance of the law firm; and 5. How artificial intelligence and machine learning systems will take over a significant portion of lawyers’ work by the end of the 2020s. The article discusses how these changes have transformed or are transforming the practice of law, and explains how institutions within the law will need to respond if they are to remain relevant (or even to survive). More broadly, it examines the social implications of a legal environment where a large percentage of the practice of law is performed by institutions that sit outside the legal profession.

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.

Community Monitoring Improves Public Service Provision at Scale: Experimental Evidence from a Child Development Program in Nicaragua

2020 ◽  
Author(s):  
Florencia López Boo ◽  
Jane Leer ◽  
Akito Kamei
Keyword(s):  
Child Development ◽  
Experimental Evidence ◽  
Large Scale ◽  
Service Providers ◽  
Low Cost ◽  
Development Program ◽  
Small Scale ◽  
Community Monitoring ◽  
Quality Assurance Mechanism ◽  
Intent To Treat

Expanding small-scale interventions without lowering quality and attenuating impact is a critical policy challenge. Community monitoring overs a low-cost quality assurance mechanism by making service providers account-able to local citizens, rather than distant administrators. This paper provides experimental evidence from a home visit parenting program implemented at scale by the Nicaraguan government, with two types of monitoring: (a) institutional monitoring; and (b) community monitoring. We find d a positive intent-to-treat effect on child development, but only among groups randomly assigned to community monitoring. Our findings show promise for the use of community monitoring to ensure quality in large-scale government-run social programs.

Angolan construction rebound could prove elusive

2017 ◽  
Keyword(s):  
Large Scale ◽  
Service Providers ◽  
Low Cost ◽  
Political Influence ◽  
Content Type ◽  
Considerable Uncertainty ◽  
Credit Lines ◽  
Private Sector Investment ◽  
Water Port ◽  
Oil Sector

Subject Angola construction sector downturn. Significance Between 2004 and 2014, an economic boom attracted large-scale, state-subsidised investments in housing and infrastructure. Since the oil price downturn, Angola has suffered a construction slowdown as the state finds itself unable to pay service providers and import materials. Projects that have survived are those with attached political interests or money already guaranteed by existing oil credit lines, creating considerable uncertainty over future investment potential. Impacts Large-scale projects, such as the new Cabinda deep-water port, risk being undermined by political influence. Private-sector investment in low-cost housing, water and electricity initiatives could increase. Public-private partnerships in the non-oil sector are likely to grow.

scholarly journals LoRaWAN IoT Technology for Energy Smart Metering Case Study Lebanon

2021 ◽  
Vol 886 ◽  
pp. 30-41
Author(s):  
Carine Zaraket ◽  
Panagiotis Papageorgas ◽  
Michel Aillerie ◽  
Kyriakos Agavanakis
Keyword(s):  
Long Range ◽  
Short Range ◽  
Low Cost ◽  
Data Management System ◽  
Area Network ◽  
Smart Metering ◽  
Wide Area Network ◽  
Smart Meters ◽  
Industrial Sectors ◽  
Last Mile

Internet of things (IoT) technology is based on connecting each real object to the internet. Every single object is uniquely recognized and reachable over the network. IoT last mile connectivity is based on different communication technologies and protocols, where the majority is categorized as short-range networks that operate in ISM band like Zigbee, Wifi and Bluetooth. Short-range technologies were successfully tested and deployed in different industrial sectors. However, in the energy sectors its deployment is challenging in certain hard to reach areas where a reliable last mile connectivity is required between the home area network (HAN) smart meters and the meter data management system (MDMS). Therefore recently, Low Power Wide Area Network (LPWAN) technology, which offers a long range connectivity, has emerged as a promising technology for IoT. Within LPWAN, variety of platforms exist and operate in licensed and unlicensed spectrum respectively like NB-IoT, and LoRaWAN, Sigfox. In this paper we discuss both the performance of LoRaWAN in a real-world environment and its deployment as a low cost, long range and reliable last mile solution for energy smart metering in urban area scenario where short range solution may not work the best. Furthermore, a prototype that is adapted to the existing Lebanese traditional energy sector was developed to test LoRaWAN usefulness in Lebanon.

scholarly journals Capturing high-resolution water demand data in commercial buildings

2021 ◽  
Author(s):  
Peter Melville-Shreeve ◽  
Sarah Cotterill ◽  
David Butler
Keyword(s):  
High Resolution ◽  
Real Time ◽  
Water Demand ◽  
Rainwater Harvesting ◽  
Service Providers ◽  
Low Cost ◽  
Time Data ◽  
Smart Meters ◽  
High Resolution Data ◽  
Site Water

Abstract Water demand measurements have historically been conducted manually, from meter readings less than once per month. Leading water service providers have begun to deploy smart meters to collect high-resolution data. A low-cost flush counter was developed and connected to a real-time monitoring platform for 119 ultra-low flush toilets in 7 buildings on a university campus to explore how building users influence water demand. Toilet use followed a typical weekly pattern in which weekday use was 92% ± 4 higher than weekend use. Toilet demand was higher during term time and showed a strong, positive relationship with the number of building occupants. Mixed-use buildings tended to have greater variation in toilet use between term time and holidays than office-use buildings. The findings suggest that the flush sensor methodology is a reliable method for further consideration. Supplementary data from the study's datasets will enable practitioners to use captured data for (i) forecast models to inform water resource plans; (ii) alarm systems to automate maintenance scheduling; (iii) dynamic cleaning schedules; (iv) monitoring of building usage rates; (v) design of smart rainwater harvesting to meet demand from real-time data; and (vi) exploring dynamic water pricing models, to incentivise optimal on-site water storage strategies.

scholarly journals RSS-Based Localization and Mobility Evaluation Using a Single NB-IoT Cell

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6172
Author(s):  
Thomas Janssen ◽  
Rafael Berkvens ◽  
Maarten Weyn
Keyword(s):  
Low Power ◽  
Large Scale ◽  
Low Cost ◽  
Estimation Error ◽  
Signal Strength ◽  
Location Estimation ◽  
Wide Area Networks ◽  
Localization Performance ◽  
Cell Reselection ◽  
Signals Of Opportunity

Low Power Wide Area Networks (LPWAN) have the ability to localize a mobile transmitter using signals of opportunity, as a low power and low cost alternative to satellite-based solutions. In this paper, we evaluate the accuracy of three localization approaches based on the Received Signal Strength (RSS). More specifically, the performance of a proximity, range-based and optimized fingerprint-based algorithm is evaluated in a large-scale urban environment using a public Narrowband Internet of Things (NB-IoT) network. The results show a mean location estimation error of 340, 320 and 204 m, respectively. During the measurement campaign, we discovered a mobility issue in NB-IoT. In contrast to other LPWAN and cellular technologies which use multiple gateways or cells to locate a device, only a single cell antenna can be used for RSS-based localization in NB-IoT. Therefore, we address this limitation in the current NB-IoT hardware and software by studying the mobility of the cellular-based 3GPP standard in a localization context. Experimental results show that the lack of handover support leads to increased cell reselection time and poor cell sector reliability, which in turn results in reduced localization performance.

scholarly journals Smart Metering and Scheduling of Electrical Loads of Buildings

2021 ◽  
Vol 2096 (1) ◽  
pp. 012132
Author(s):  
V Mishuchkov ◽  
M Pushkareva ◽  
S Belov
Keyword(s):  
Energy Efficiency ◽  
Successful Implementation ◽  
Smart Metering ◽  
Smart Meters ◽  
Global Trends ◽  
Improve Energy Efficiency ◽  
Energy Management Systems ◽  
Service Contracts ◽  
Load Monitoring ◽  
Electrical Loads

Abstract The article analyzes global trends in the development of smart metering and energy planning in buildings, the development and implementation of intelligent energy monitoring to collect and analyze data on energy consumption for management to improve energy efficiency, it considers the implementation of infrastructure for advanced smart metering smart home using NILM-technology non-intrusive load monitoring. It is shown that equipping buildings of various purposes in a smart city with real-time energy accounting systems with smart meters generates a new approach to improving the energy efficiency of buildings and contributes to the successful implementation of energy service contracts and energy management systems in them.

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 Load Disaggregation Using Microscopic Power Features and Pattern Recognition

Energies ◽  
2019 ◽  
Vol 12 (14) ◽  
pp. 2641 ◽  
Author(s):  
Wesley Angelino de Souza ◽  
Fernando Deluno Garcia ◽  
Fernando Pinhabel Marafão ◽  
Luiz Carlos Pereira da Silva ◽  
Marcelo Godoy Simões
Keyword(s):  
The Novel ◽  
Load Modeling ◽  
Smart Meters ◽  
Data Set ◽  
Power Theory ◽  
Load Monitoring ◽  
Load Disaggregation ◽  
New Generation ◽  
Conservative Power Theory ◽  
Conservative Power

A new generation of smart meters are called cognitive meters, which are essentially based on Artificial Intelligence (AI) and load disaggregation methods for Non-Intrusive Load Monitoring (NILM). Thus, modern NILM may recognize appliances connected to the grid during certain periods, while providing much more information than the traditional monthly consumption. Therefore, this article presents a new load disaggregation methodology with microscopic characteristics collected from current and voltage waveforms. Initially, the novel NILM algorithm—called the Power Signature Blob (PSB)—makes use of a state machine to detect when the appliance has been turned on or off. Then, machine learning is used to identify the appliance, for which attributes are extracted from the Conservative Power Theory (CPT), a contemporary power theory that enables comprehensive load modeling. Finally, considering simulation and experimental results, this paper shows that the new method is able to achieve 95% accuracy considering the applied data set.

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