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.

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.

Using Supervised Machine Learning to Explore Energy Consumption Data in Private Sector Housing

2015 ◽  
pp. 320-333 ◽  
Author(s):  
Mariya Sodenkamp ◽  
Konstantin Hopf ◽  
Thorsten Staake
Keyword(s):  
Machine Learning ◽  
Energy Consumption ◽  
Classification Accuracy ◽  
Residential Buildings ◽  
Supervised Machine Learning ◽  
Household Characteristics ◽  
Consumption Data ◽  
Feature Filtering ◽  
Electricity Load ◽  
Class Labels

Smart electricity meters allow capturing consumption load profiles of residential buildings. Besides several other applications, the retrieved data renders it possible to reveal household characteristics including the number of persons per apartment, age of the dwelling, etc., which helps to develop targeted energy conservation services. The goal of this chapter is to develop further related methods of smart meter data analytics that infer such household characteristics using weekly load curves. The contribution of this chapter to the state of the art is threefold. The authors first quadruplicate the number of defined features that describe electricity load curves to preserve relevant structures for classification. Then, they suggest feature filtering techniques to reduce the dimension of the input to a set of a few significant ones. Finally, the authors redefine class labels for some properties. As a result, the classification accuracy is elevated up to 82%, while the runtime complexity is significantly reduced.

scholarly journals Energy Consumption Prediction of Electric Vehicles Based on Digital Twin Technology

2021 ◽  
Vol 12 (4) ◽  
pp. 160
Author(s):  
Zhaolong Zhang ◽  
Yuan Zou ◽  
Teng Zhou ◽  
Xudong Zhang ◽  
Zhifeng Xu
Keyword(s):  
Energy Consumption ◽  
Real Time ◽  
Electric Vehicle ◽  
Digital Twin ◽  
Twin Model ◽  
Consumption Data ◽  
Energy Consumption Model ◽  
Energy Consumption Prediction ◽  
Consumption Prediction ◽  
Monitoring Platform

Digital twinning technology originated in the field of aerospace. The real-time and bidirectional feature of data interaction guarantees its advantages of high accuracy, real-time performance and scalability. In this paper the digital twin technology was introduced to electric vehicle energy consumption research. First, an energy consumption model of an electric vehicle of BAIC BJEV was established, then the model was optimized and verified through the energy consumption data of the drum test. Based on the data of the vehicle real-time monitoring platform, a digital twin model was built, and it was trained and updated by daily new data. Eventually it can be used to predict and verify the data of vehicle. In this way the prediction of energy consumption of vehicles can be achieved.

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 Impact of an Energy Monitoring System on the Energy Efficiency of an Automobile Factory: A Case Study

Energies ◽  
2020 ◽  
Vol 13 (10) ◽  
pp. 2577 ◽  
Author(s):  
Tehseen Akhtar ◽  
Asif Ur Rehman ◽  
Mohsin Jamil ◽  
Syed Omer Gilani
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Real Time ◽  
Quality Data ◽  
Efficiency Improvement ◽  
Data Logging ◽  
Energy Monitoring ◽  
Energy Efficiency Improvement ◽  
Automobile Factory ◽  
Consumption Data

Energy-saving strategies cannot be implemented without having detailed and regular power consumption data of a facility. The installation of an energy monitoring and data logging system can help in planning energy efficiency improvement policies by providing daily, monthly, and yearly energy consumption reports and graphs. The purpose of this study was to demonstrate the impact of an energy monitoring and management system on the improvement of energy efficiency in the industrial sector of developing countries. This study introduced an energy monitoring and data logging system installed in an automobile factory in Pakistan. Energy consumption data, which also included power quality data, were collected with the help of energy analyzers and transmitted to a centralized supervisory control and data acquisition (SCADA) software for data logging and monitoring purposes. This system was developed by combining Modbus with industrial Ethernet to communicate real-time energy consumption data of the factory to multiple local and remote locations. Monitoring and logging the real-time energy consumption data helped the user to find the significant energy losses inside the factory and to implement various energy conservation policies inside the facility, resulting in energy efficiency improvement. The energy consumption results indicate that the proposed system can help achieve an approximately 8% improvement in energy efficiency.

scholarly journals Intelligent Room-Based Identification of Electricity Consumption with an Ensemble Learning Method in Smart Energy

Energies ◽  
2021 ◽  
Vol 14 (20) ◽  
pp. 6717
Author(s):  
Vincent Le ◽  
Joshua Ramirez ◽  
Miltiadis Alamaniotis
Keyword(s):  
Energy Consumption ◽  
Nearest Neighbor ◽  
Electricity Consumption ◽  
K Nearest Neighbor ◽  
Real World Data ◽  
Energy Technologies ◽  
Consumption Data ◽  
Intelligent Management ◽  
Individual Consumption ◽  
Demand Response Programs

This paper frames itself in the realm of smart energy technologies that can be utilized to satisfy the electricity demand of consumers. In this environment, demand response programs and the intelligent management of energy consumption that are offered by utility providers will play a significant role in implementing smart energy. One of the approaches to implementing smart energy is to analyze consumption data and provide targeted contracts to consumers based on their individual consumption characteristics. To that end, the identification of individual consumption features is important for suppliers and utilities. Given the complexity of smart home load profiles, an appliance-based identification is nearly impossible. In this paper, we propose a different approach by grouping appliances based on their rooms; thus, we provide a room-based identification of energy consumption. To this end, this paper presents and tests an intelligent consumption identification methodology, that can be implemented in the form of an ensemble of artificial intelligence tools. The ensemble, which comprises four convolutional neural networks (CNNs) and four k-nearest neighbor (KNN) algorithms, is fed with smart submeter data and outputs the identified type of room in a given dwelling. Results obtained from real-world data exhibit the superiority of the ensemble, with respect to accuracy, as compared with individual CNN and KNN models.

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