scholarly journals A Novel IoT Based Smart Energy Meter with Backup Battery

2021 ◽  
pp. 357-364
Author(s):  
Osman Yakubu ◽  
C. Narendra Babu ◽  
C. Osei Adjei
Keyword(s):  
Energy Consumption ◽  
Real Time ◽  
Energy Demand ◽  
Single Phase ◽  
Data Repository ◽  
Smart Meters ◽  
Cloud Data ◽  
Energy Meter ◽  
Consumption Data ◽  
Utility Companies

Energy consumption is currently on the ascendency due to increased demand by domestic and industrial consumers. The quest to ensure that consumers manage their consumption and the utility companies also monitor consumers to manage energy demand and production resulted in smart energy meters which are able to transmit data automatically at certain intervals being introduced. These Smart Meters are still fraught with challenges as consumers are unable to effectively monitor their consumption and the meters are also expensive to deploy. This research aims to present a novel IoT based Smart Energy Meter that will gather consumption data in real time and transmit it to a cloud data repository for storage and analysis. The novelty of this inexpensive system is the introduction of an ADM25SC Single Phase DIN-RAIL Watt-hour Energy Meter which sends power to the microcontroller and also the introduction of a backup battery that keeps the meter on for some time to transmit outage data during power outages. Data gathered from the proposed IoT based Smart Energy Meter for a period is compared against that of the same period from a Smart G meter, a widely used energy meter, and is found to be very close confirming the accuracy of the IoT based Smart Energy Meter.

scholarly journals Wireless Home Energy Meter Manager using Android: A Preliminary study

2019 ◽  
Vol 18 (3-2) ◽  
pp. 32-36
Author(s):  
Sh. Nurul Hidayah Wan Julihi ◽  
Ili Najaa Aimi Mohd Nordin ◽  
Muhammad Rusydi Muhammad Razif ◽  
Amar Faiz Zainal Abidin
Keyword(s):  
Energy Consumption ◽  
Personal Information ◽  
Current Sensor ◽  
Smart Meter ◽  
Smart Meters ◽  
Long Distance ◽  
Energy Meter ◽  
Utility Companies ◽  
Preliminary Study ◽  
To Receive

Manual home energy meter reading and billing had caused inconvenience to the utility companies due to lack of manpower to read the energy meter at each household especially in the remote area, explains the increasing number of smart meter reader in the current market. Most of the smart meters in the market do not offer safety of privacy of consumers’ personal information since the data of electricity usage is being transferred digitally to the utility companies for more accurate bills calculation. Plus, the smart meter system is also a bit pricey to be installed in the rural area. Therefore, a private system that able to read energy consumption from a DC load and calculate its bill according to the tariff is proposed. Value of current is being obtained by using ACS712 current sensor. Hall circuit in the current sensor will converts magnetic field into a proportional voltage. The proposed system allows energy meter monitoring from an Android-based smartphone by displaying the real-time energy consumption and bill on Blynk application. An interface of Blynk is developed and connected to WiFi module, ESP8266 for visualizing the energy consumption of the DC load. In conclusion, the Energy Meter transmitter part able to read, calculate and transmit value of energy consumption and current bills to the Blynk application and Blynk application able to receive and show all the data transmitted at the present time. This system will be further improved for long-distance monitoring of electrical appliances used at home.

Real-Time Highly Resolved Spatial-Temporal Vehicle Energy Consumption Estimation Using Machine Learning and Probe Data

2021 ◽  
pp. 036119812110391
Author(s):  
Joseph Severino ◽  
Yi Hou ◽  
Ambarish Nag ◽  
Jacob Holden ◽  
Lei Zhu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Energy Consumption ◽  
Real Time ◽  
Volume Estimation ◽  
Energy Estimates ◽  
Volume Data ◽  
Road Transportation ◽  
Energy Efficient Routing ◽  
Real World Data ◽  
Consumption Data

Real-time highly resolved spatial-temporal vehicle energy consumption is a key missing dimension in transportation data. Most roadway link-level vehicle energy consumption data are estimated using average annual daily traffic measures derived from the Highway Performance Monitoring System; however, this method does not reflect day-to-day energy consumption fluctuations. As transportation planners and operators are becoming more environmentally attentive, they need accurate real-time link-level vehicle energy consumption data to assess energy and emissions; to incentivize energy-efficient routing; and to estimate energy impact caused by congestion, major events, and severe weather. This paper presents a computational workflow to automate the estimation of time-resolved vehicle energy consumption for each link in a road network of interest using vehicle probe speed and count data in conjunction with machine learning methods in real time. The real-time pipeline can deliver energy estimates within a couple seconds on query to its interface. The proposed method was evaluated on the transportation network of the metropolitan area of Chattanooga, Tennessee. The volume estimation results were validated with ground truth traffic volume data collected in the field. To demonstrate the effectiveness of the proposed method, the energy consumption pipeline was applied to real-world data to quantify road transportation-related energy reduction because of mitigation policies to slow the spread of COVID-19 and to measure energy loss resulting from congestion.

THE EFFECT OF OCCUPANCY ON ELECTRICITY USE IN THREE CANADIAN SCHOOLS

2018 ◽  
Vol 13 (1) ◽  
pp. 95-112
Author(s):  
Mohamed Ouf ◽  
Mohamed H. Issa ◽  
Phil Merkel ◽  
Panos Polyzois
Keyword(s):  
Energy Consumption ◽  
Real Time ◽  
Electricity Consumption ◽  
After School ◽  
Building Performance ◽  
Comprehensive Method ◽  
Electricity Use ◽  
Period 2 ◽  
Consumption Data ◽  
Plug Loads

Through building performance simulations, previous studies showed the effect of occupants on buildings' energy consumption. To further demonstrate this effect using empirical evidence, this study analyzed the effect of occupancy on real-time electricity consumption in three case-study schools in Manitoba. Within each school, one classroom as well as the gymnasium were sub-metered to collect real-time electricity consumption data at half-hourly intervals. The study focused on electricity consumption for lighting and plug loads, which make up 30% of energy consumption in Canadian commercial and institutional buildings. A comprehensive method was developed to investigate energy-related occupant behaviour in the sub-metered spaces using four different tools simultaneously: 1) gymnasium bookings after school hours over a four-month period, 2) half-hourly observations of lighting and equipment use in the sub-metered spaces in each school over a two-week period, 3) a daily survey administered to teachers in the sub-metered classrooms over a two-week period, and 4) occupancy and light sensors to evaluate actual recorded occupancy and light use durations over a four-month period. Results showed that recorded occupancy durations over a 4-month period only explained less than 10% of the variations in classrooms' lighting electricity consumption, meaning that lights may have been used frequently while classrooms were unoccupied. Results also showed the differences in gymnasiums' electricity consumption were still statistically significant between the three schools, even after school hours and when the gymnasiums were not used or booked for other activities. This study is the first to provide in-depth evaluation of the effect of occupancy on electricity consumption in Canadian schools.

Blackout Avoidance and Energy Saving Services with Alert Aggregation

2012 ◽  
Vol 3 (4) ◽  
pp. 40-56
Author(s):  
Erica Fong ◽  
Dickson K.W. Chiu ◽  
Haiyang Hu ◽  
Yi Zhuang ◽  
Hua Hu
Keyword(s):  
Energy Consumption ◽  
Energy Saving ◽  
Real Time ◽  
Mobile Devices ◽  
Critical Energy ◽  
The Internet ◽  
Monitor System ◽  
Energy Usage ◽  
Huge Number ◽  
Smart Meters

Peak electricity demands from huge number of households in a mega-city would cause contention, leading to potential blackout. This paper proposes bi-directional collaboration via a Smart Energy Monitor System (SEMS) between consumers and energy suppliers, exchanging real-time energy usage data with smart meters over the Internet and mobile devices for well-informed decisions and even predictions. The authors further propose the use of an Alert Management System (AMS) to monitor and aggregate critical energy consumption events for this purpose.

scholarly journals Analyzing Load Profiles of Energy Consumption to Infer Household Characteristics Using Smart Meters

Energies ◽  
2019 ◽  
Vol 12 (5) ◽  
pp. 773 ◽  
Author(s):  
Muhammad Fahim ◽  
Alberto Sillitti
Keyword(s):  
Energy Consumption ◽  
Time Series Data ◽  
Residential Buildings ◽  
Series Data ◽  
Smart Meters ◽  
Learning Mechanism ◽  
Efficient Energy ◽  
Household Characteristics ◽  
Univariate Time Series ◽  
Consumption Data

The increasing penetration of smart meters provides an excellent opportunity to monitor and analyze energy consumption in residential buildings. In this paper, we propose a framework to process the observed profiles of energy consumption to infer the household characteristics in residential buildings. Such characteristics can be used for improving resource allocation and for an efficient energy management that will ultimately contribute to reducing carbon dioxide (CO 2 ) emission. Our approach is based on automated extraction of features from univariate time-series data and development of a model through a variant of the decision trees technique (i.e., ensemble learning mechanism) random forest. We process and analyzed energy consumption data to answer four primitive questions. To evaluate the approach, we performed experiments on publicly available datasets. Our experiments show a precision of 82% and a recall of 81% in inferring household characteristics.

Analysis and Design of Electric Energy Consumption Inspection Equipment Based on ARM9

2012 ◽  
Vol 157-158 ◽  
pp. 447-451
Author(s):  
Hu Hu ◽  
Xin Tian ◽  
Li Hong Han ◽  
Bin Chen
Keyword(s):  
Energy Consumption ◽  
Real Time ◽  
Low Cost ◽  
Electric Energy ◽  
Basic Structure ◽  
Analysis And Design ◽  
Electric Energy Consumption ◽  
Real Time Analysis ◽  
Consumption Data ◽  
Real Time Transmission

The present paper introduces a sort of analysis and design of electric energy consumption inspection equipment based on ARM9, which can inspect multiple electric energy indexes and conduct a real time inspection to electric energy consumption. Both a real time collection and a real time transmission of electric energy consumption data are realized and a real time analysis of these data that are transmitted through the network to the host computer can be carried out as well, the features of which are low power consumption, low cost, very applicable, high real time performance, etc. The paper also describes the system’s basic structure, hardware design, software design and system debugging process.

scholarly journals Partitional Clustering-Hybridized Neuro-Fuzzy Classification Evolved through Parallel Evolutionary Computing and Applied to Energy Decomposition for Demand-Side Management in a Smart Home

Processes ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 1539
Author(s):  
Yu-Chen Hu ◽  
Yu-Hsiu Lin ◽  
Harinahalli Lokesh Gururaj
Keyword(s):  
Energy Consumption ◽  
Electric Energy ◽  
Smart Meter ◽  
Energy Decomposition ◽  
Smart Meters ◽  
Electrical Grid ◽  
Electric Energy Consumption ◽  
Partitional Clustering ◽  
Neuro Fuzzy ◽  
Consumption Data

The key advantage of smart meters over rotating-disc meters is their ability to transmit electric energy consumption data to power utilities’ remote data centers. Besides enabling the automated collection of consumers’ electric energy consumption data for billing purposes, data gathered by smart meters and analyzed through Artificial Intelligence (AI) make the realization of consumer-centric use cases possible. A smart meter installed in a domestic sector of an electrical grid and used for the realization of consumer-centric use cases is located at the entry point of a household/building’s electrical grid connection and can gather composite/circuit-level electric energy consumption data. However, it is not able to decompose its measured circuit-level electric energy consumption into appliance-level electric energy consumption. In this research, we present an AI model, a neuro-fuzzy classifier integrated with partitional clustering and metaheuristically optimized through parallel-computing-accelerated evolutionary computing, that performs energy decomposition on smart meter data in residential demand-side management, where a publicly available UK-DALE (UK Domestic Appliance-Level Electricity) dataset is used to experimentally test the presented model to classify the On/Off status of monitored electrical appliances. As shown in this research, the presented AI model is effective at providing energy decomposition for domestic consumers. Further, energy decomposition can be provided for industrial as well as commercial consumers.

scholarly journals Time-Pattern Profiling from Smart Meter Data to Detect Outliers in Energy Consumption

IoT ◽  
2020 ◽  
Vol 1 (1) ◽  
pp. 92-108
Author(s):  
William Hurst ◽  
Casimiro A. Curbelo Montañez ◽  
Nathan Shone
Keyword(s):  
Energy Consumption ◽  
Spatial Clustering ◽  
Smart Meter ◽  
Energy Usage ◽  
Smart Meters ◽  
Time Pattern ◽  
Behavioural Patterns ◽  
Utility Companies ◽  
The Uk ◽  
The Internet Of Things

Smart meters have become a core part of the Internet of Things, and its sensory network is increasing globally. For example, in the UK there are over 15 million smart meters operating across homes and businesses. One of the main advantages of the smart meter installation is the link to a reduction in carbon emissions. Research shows that, when provided with accurate and real-time energy usage readings, consumers are more likely to turn off unneeded appliances and change other behavioural patterns around the home (e.g., lighting, thermostat adjustments). In addition, the smart meter rollout results in a lessening in the number of vehicle callouts for the collection of consumption readings from analogue meters and a general promotion of renewable sources of energy supply. Capturing and mining the data from this fully maintained (and highly accurate) sensing network, provides a wealth of information for utility companies and data scientists to promote applications that can further support a reduction in energy usage. This research focuses on modelling trends in domestic energy consumption using density-based classifiers. The technique estimates the volume of outliers (e.g., high periods of anomalous energy consumption) within a social class grouping. To achieve this, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Ordering Points to Identify the Clustering Structure (OPTICS) and Local Outlier Factor (LOF) demonstrate the detection of unusual energy consumption within naturally occurring groups with similar characteristics. Using DBSCAN and OPTICS, 53 and 208 outliers were detected respectively; with 218 using LOF, on a dataset comprised of 1,058,534 readings from 1026 homes.

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