Trip Energy Estimation Methodology and Model Based on Real-World Driving Data for Green-Routing Applications

2018 ◽  
Vol 2672 (24) ◽  
pp. 41-48 ◽  
Author(s):  
Jacob Holden ◽  
Harrison Van Til ◽  
Eric Wood ◽  
Lei Zhu ◽  
Jeffrey Gonder ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Fuel Consumption ◽  
Real World ◽  
Energy Use ◽  
Average Energy ◽  
Estimation Model ◽  
Energy Estimation ◽  
Conventional Vehicle ◽  
Look Up Table ◽  
Wide Range

A data-informed model to predict energy use for a proposed vehicle trip has been developed in this paper. The methodology leverages roughly one million miles of real-world driving data to generate the estimation model. Driving is categorized at the sub-trip level by average speed, road gradient, and road network geometry, then aggregated by category. An average energy consumption rate is determined for each category, creating an energy rate look-up table. Proposed vehicle trips are then categorized in the same manner, and estimated energy rates are appended from the look-up table. The methodology is robust and applicable to a wide range of driving data. The model has been trained on vehicle travel profiles from the Transportation Secure Data Center at the National Renewable Energy Laboratory and validated against on-road fuel consumption data from testing in Phoenix, Arizona. When compared against the detailed on-road conventional vehicle fuel consumption test data, the energy estimation model accurately predicted which route would consume less fuel over a dozen different tests. When compared against a larger set of real-world origin–destination pairs, it is estimated that implementing the present methodology should accurately select the route that consumes the least fuel 90% of the time. The model results can be used to inform control strategies in routing tools, such as change in departure time, alternate routing, and alternate destinations to reduce energy consumption. This work provides a highly extensible framework that allows the model to be tuned to a specific driver or vehicle type.

scholarly journals Variability in Measured Real-World Operational Energy Use and Emission Rates of a Plug-In Hybrid Electric Vehicle

Energies ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 1140 ◽  
Author(s):  
H. Christopher Frey ◽  
Xiaohui Zheng ◽  
Jiangchuan Hu
Keyword(s):  
Real World ◽  
Hybrid Electric Vehicle ◽  
Energy Use ◽  
Average Energy ◽  
Specific Power ◽  
Emission Measurement ◽  
Emission Rates ◽  
Wide Range ◽  
Hybrid Electric ◽  
Cold Starts

Compared to comparably sized conventional light duty gasoline vehicles (CLDGVs), plug-in hybrid electric vehicles (PHEVs) may offer benefits of improved energy economy, reduced emissions, and the flexibility to use electricity as an energy source. PHEVs operate in either charge depleting (CD) or charge sustaining (CS) mode; the engine has the ability to turn on and off; and the engine can have multiple cold starts. A method is demonstrated for quantifying the real-world activity, energy use, and emissions of PHEVs, taking into account these operational characteristics and differences in electricity generation resource mix. A 2013 Toyota Prius plug-in was measured using a portable emission measurement system. Vehicle specific power (VSP) based modal average energy use and emission rates are inferred to assess trends in energy use and emissions with respect to engine load and for comparisons of engine on versus engine off, and cold start versus hot stabilized running. The results show that, compared to CLDGVs, the PHEV operating in CD mode has improved energy efficiency and lower CO2, CO, HC, NOx, and PM2.5 emission rates for a wide range of power generation fuel mixes. However, PHEV energy use and emission rates are highly variable, with periods of relatively high on-road emission rates related to cold starts.

scholarly journals An Exhaustive Search Energy Optimization Method for Residential Building Envelope in Different Climatic Zones of Kazakhstan

Buildings ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 633
Author(s):  
Mirzhan Kaderzhanov ◽  
Shazim Ali Memon ◽  
Assemgul Saurbayeva ◽  
Jong R. Kim
Keyword(s):  
Energy Consumption ◽  
Energy Savings ◽  
Residential Buildings ◽  
Average Energy ◽  
Optimization Method ◽  
Building Envelope ◽  
Energy Reduction ◽  
Estimation Model ◽  
Total Energy Consumption ◽  
Energy Estimation

Nowadays, the residential sector of Kazakhstan accounts for about 30% of the total energy consumption. Therefore, it is essential to analyze the energy estimation model for residential buildings in Kazakhstan so as to reduce energy consumption. This research is aimed to develop the Overall Thermal Transfer Value (OTTV) based Building Energy Simulation Model (BESM) for the reduction of energy consumption through the envelope of residential buildings in seven cities of Kazakhstan. A brute force optimization method was adopted to obtain the optimal envelope configuration varying window-to-wall ratio (WWR), the angle of a pitched roof, the depth of the overhang shading system, the thermal conductivity, and the thicknesses of wall composition materials. In addition, orientation-related analyses of the optimized cases were conducted. Finally, the economic evaluation of the base and optimized cases were presented. The results showed that an average energy reduction for heating was 6156.8 kWh, while for cooling it was almost 1912.17 kWh. The heating and cooling energy savings were 16.59% and 16.69%, respectively. The frontage of the building model directed towards the south in the cold season and north in the hot season demonstrated around 21% and 32% of energy reduction, respectively. The energy cost savings varied between 9657 to 119,221 ₸ for heating, 9622 to 36,088 ₸ for cooling.

scholarly journals Deep Learning Approach of Energy Estimation Model of Remote Laser Welding

Energies ◽  
2019 ◽  
Vol 12 (9) ◽  
pp. 1799 ◽  
Author(s):  
Jumyung Um ◽  
Ian Anthony Stroud ◽  
Yong-keun Park
Keyword(s):  
Deep Learning ◽  
Laser Welding ◽  
Process Parameters ◽  
Energy Use ◽  
Technical Specification ◽  
Average Energy ◽  
Welding Process ◽  
Learning Approach ◽  
Estimation Model ◽  
Remote Laser Welding

Due to concerns about energy use in production systems, energy-efficient processes have received much interest from the automotive industry recently. Remote laser welding is an innovative assembly process, but has a critical issue with the energy consumption. Robot companies provide only the average energy use in the technical specification, but process parameters such as robot movement, laser use, and welding path also affect the energy use. Existing literature focuses on measuring energy in standardized conditions in which the welding process is most frequently operated or on modularizing unified blocks in which energy can be estimated using simple calculations. In this paper, the authors propose an integrated approach considering both process variation and machine specification and multiple methods’ comparison. A deep learning approach is used for building the neural network integrated with the effects of process parameters and machine specification. The training dataset used is experimental data measured from a remote laser welding robot producing a car back door assembly. The proposed estimation model is compared with a linear regression approach and shows higher accuracy than other methods.

scholarly journals Digital Twin-Driven Decision Making and Planning for Energy Consumption

2021 ◽  
Vol 10 (2) ◽  
pp. 37
Author(s):  
Yasmin Fathy ◽  
Mona Jaber ◽  
Zunaira Nadeem
Keyword(s):  
Energy Consumption ◽  
Energy Demand ◽  
Energy Production ◽  
Energy Use ◽  
Synthetic Data ◽  
Average Energy ◽  
Energy System ◽  
Real Dataset ◽  
Digital Twin ◽  
The Impact

The Internet of Things (IoT) is revolutionising how energy is delivered from energy producers and used throughout residential households. Optimising the residential energy consumption is a crucial step toward having greener and sustainable energy production. Such optimisation requires a household-centric energy management system as opposed to a one-rule-fits all approach. In this paper, we propose a data-driven multi-layer digital twin of the energy system that aims to mirror households’ actual energy consumption in the form of a household digital twin (HDT). When linked to the energy production digital twin (EDT), HDT empowers the household-centric energy optimisation model to achieve the desired efficiency in energy use. The model intends to improve the efficiency of energy production by flattening the daily energy demand levels. This is done by collaboratively reorganising the energy consumption patterns of residential homes to avoid peak demands whilst accommodating the resident needs and reducing their energy costs. Indeed, our system incorporates the first HDT model to gauge the impact of various modifications on the household energy bill and, subsequently, on energy production. The proposed energy system is applied to a real-world IoT dataset that spans over two years and covers seventeen households. Our conducted experiments show that the model effectively flattened the collective energy demand by 20.9% on synthetic data and 20.4% on a real dataset. At the same time, the average energy cost per household was reduced by 10.7% for the synthetic data and 17.7% for the real dataset.

scholarly journals Residential End-Use Energy Estimation Models in Korean Apartment Units through Multiple Regression Analysis

Energies ◽  
2019 ◽  
Vol 12 (12) ◽  
pp. 2327 ◽  
Author(s):  
Soo-Jin Lee ◽  
You-Jeong Kim ◽  
Hye-Sun Jin ◽  
Sung-Im Kim ◽  
Soo-Yeon Ha ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Energy Consumption ◽  
Survey Data ◽  
Field Survey ◽  
Maximum Error ◽  
Hot Water ◽  
Annual Data ◽  
Estimation Model ◽  
Energy Estimation ◽  
End Use

The aim of this study was to develop a mathematical regression model for predicting end-use energy consumption in the residential sector. To this end, housing characteristics were collected through a field survey and in-depth interviews with residents of 71 households (15 apartment complexes) in Seoul, South Korea, and annual data on end-use energy consumption were collected from measurement systems installed within each apartment unit. Based on the data collected, correlativity between the field-survey data and end-use energy consumption was analyzed, and effective independent variables from the field-survey data were selected. Regression models were developed and validated for estimating six end uses of energy consumption: heating, cooling, domestic hot water (DHW), lighting, electric appliances, and cooking. Regression analysis for ventilation was not applied, and instead a calculation formula was derived, because the energy-consumption proportion was too low. The adj-R2 of the estimation model ranged from 0.406 to 0.703, and the maximum error between measured and estimated values was around ±30%, depending on the end use.

scholarly journals Impact of the COVID-19 Pandemic on the Energy Use at the University of Almeria (Spain)

2021 ◽  
Vol 13 (11) ◽  
pp. 5843
Author(s):  
Mehdi Chihib ◽  
Esther Salmerón-Manzano ◽  
Mimoun Chourak ◽  
Alberto-Jesus Perea-Moreno ◽  
Francisco Manzano-Agugliaro
Keyword(s):  
Energy Consumption ◽  
Energy Use ◽  
Energy Savings ◽  
Electricity Consumption ◽  
Energy Performance ◽  
Energy Sector ◽  
Wide Range ◽  
University Buildings ◽  
The University ◽  
The Impact

The COVID-19 pandemic has caused chaos in many sectors and industries. In the energy sector, the demand has fallen drastically during the first quarter of 2020. The University of Almeria campus also declined the energy consumption in 2020, and through this study, we aimed to measure the impact of closing the campus on the energy use of its different facilities. We built our analysis based upon the dataset collected during the year 2020 and previous years; the patterns evolution through time allowed us to better understand the energy performance of each facility during this exceptional year. We rearranged the university buildings into categories, and all the categories reduced their electricity consumption share in comparison with the previous year of 2019. Furthermore, the portfolio of categories presented a wide range of ratios that varied from 56% to 98%, the library category was found to be the most influenced, and the research category was found to be the least influenced. This opened questions like why some facilities were influenced more than others? What can we do to reduce the energy use even more when the facilities are closed? The university buildings presented diverse structures that revealed differences in energy performance, which explained why the impact of such an event (COVID-19 pandemic) is not necessarily relevant to have equivalent variations. Nevertheless, some management deficiencies were detected, and some energy savings measures were proposed to achieve a minimum waste of energy.

DEVELOPING AN ENERGY BENCHMARKING SYSTEM FOR HOTEL BUILDINGS USING THE STATISTICAL METHOD AND THE SIMULATION-BASED APPROACH

2019 ◽  
Vol 14 (3) ◽  
pp. 1-22
Author(s):  
Anh Tuan Nguyen ◽  
David Rockwood
Keyword(s):  
Energy Consumption ◽  
Energy Use ◽  
Rating Scale ◽  
Average Energy ◽  
Simulation Method ◽  
Occupancy Rate ◽  
Building Stock ◽  
Related Data ◽  
Climatic Regions ◽  
Energy Use Intensity

Due to increased tourist activity, many cities now have a large number of hotel buildings. It is necessary to establish measures to evaluate energy use intensity to effectively manage energy consumption in this sector. This study uses a combined strategy to establish an energy benchmark for hotel buildings in Vietnam. First, a survey and analysis of actual building stock data of 50 hotels in Danang, Vietnam, was conducted. The survey-based benchmark and its related data was then used to build a reference energy model to estimate an energy benchmark for other climatic regions in Vietnam by using the energy simulation method. The results reveal that the average energy use intensity for hotels in Danang was 87.4 kWh/m2.year or 8628.6 kWh/guestroom.year. However, this study proposes that because of the differing expectations of comfort standards, hotels of different grades should have separate benchmarks. This study also proposes an energy intensity-based rating scale, including 7 grades from the least energy intensive (grade A) to the most energy intensive (grade G), which can be used to manage, label, or encourage sustainable energy use in hotel buildings. The relationship between the energy use intensity and the occupancy rate of the hotels was reported, compared, and explained. It was found that occupancy rate has no significant impact on the energy use intensity. From the survey result, some predictive models were developed to estimate annual energy consumption of hotel buildings based on their grades. The simulated benchmarks for other regions were also achieved. The results demonstrate many potential applications in the management, design and construction, and renovation of this building type.

scholarly journals Energy Consumption Estimation Model for Complete Coverage of a Tetromino Inspired Reconfigurable Surface Tiling Robot

Energies ◽  
2019 ◽  
Vol 12 (12) ◽  
pp. 2257 ◽  
Author(s):  
Arunmozhi Manimuthu ◽  
Anh Vu Le ◽  
Rajesh Elara Mohan ◽  
Prabahar Veerajagadeshwar ◽  
Nguyen Huu Khanh Nhan ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Energy Cost ◽  
Pulse Width Modulation ◽  
Real Life ◽  
Area Coverage ◽  
Estimation Model ◽  
Energy Estimation ◽  
Proposed Model ◽  
Estimation Scheme ◽  
Optimal Area

As autonomous tiling devices begin to perform floor cleaning, agriculture harvesting, surface painting tasks, with minimal or no human intervention, a new challenge arises: these devices also need to be energy efficient and be constantly aware of the energy expenditure during deployments. Typical approaches to this end are often limited to fixed morphology robots with little or no consideration for reconfiguring class of robots. The main contribution of the paper is an energy estimation scheme that allows estimating the energy consumption when a tetromino inspired reconfigurable floor tiling robot, hTetro moves from one configuration to another for completing the area covering task. To this end, the proposed model applying the Newton-Raphson algorithm in combination with Pulse width modulation (PWM)-H bridge to characterize the energy cost associated with locomotion gaits across all valid morphologies and identify optimal area coverage strategy among available options is presented. We validate our proposed approach using an 8’ × 8’ square testbed where there exist 12 possible solutions for complete area coverage however with varying levels of energy cost. Then, we implemented our approach to our hTetro platform and conducted experiments in a real-life environment. Experimental results demonstrate the application of our model in identifying the optimal area coverage strategy that has the least associated energy cost.

Relationship between Energy Consumption and Service Level: A Survey of Class a Office Buildings in Shanghai

2014 ◽  
Vol 953-954 ◽  
pp. 1545-1549
Author(s):  
Liang Zhang ◽  
Peng Xu ◽  
Zheng Wei Li
Keyword(s):  
Energy Consumption ◽  
Indoor Environment ◽  
Energy Use ◽  
Average Energy ◽  
Service Level ◽  
Office Buildings ◽  
Occupancy Rate ◽  
Class A ◽  
Base Section ◽  
Consumption Intensity

There has been a boom of Class A office buildings in Shanghai in recent years. Due to the high requirements of indoor environment in Class A office buildings, these buildings typically consume larger energy than other office buildings. To have a picture of how these buildings perform in terms of energy, a survey was conducted recently. This survey was targeted at investigating monthly energy consumption, occupancy rate, and Indoor Environment Quality of twenty Class A office buildings in Shanghai in two consecutive years (2009 and 2010). The results show that average energy consumption intensity of surveyed buildings amounts 230.52 kWh/m2. The energy consumption intensity of the base section (including energy use for public services) is about two times higher than that of the tenant section (energy use in tenant space), suggesting that base section has larger energy saving potential than tenant section. The results also indicate that energy consumption of Class A office buildings has no direct relationship with occupancy rate and IEQ. However, the LEED certified green buildings do bring higher rent in average to building owners.

scholarly journals A Deep Learning Method for Monitoring Vehicle Energy Consumption with GPS Data

2021 ◽  
Vol 13 (20) ◽  
pp. 11331
Author(s):  
Kwangho Ko ◽  
Tongwon Lee ◽  
Seunghyun Jeong
Keyword(s):  
Deep Learning ◽  
Energy Consumption ◽  
Fuel Consumption ◽  
Fuel Economy ◽  
Gps Data ◽  
Data Logger ◽  
Monitoring Method ◽  
Acceleration Rate ◽  
Consumption Data ◽  
Wide Range

A monitoring method for energy consumption of vehicles is proposed in the study. It is necessary to have parameters estimating fuel economy with GPS data obtained while driving in the proposed method. The parameters are trained by fuel consumption data measured with a data logger for the reference cars. The data logger is equipped with a GPS sensor and OBD connection capability. The GPS sensor measures vehicle speed, acceleration rate and road gradient. The OBD connector gathers the fuel consumption signaled from OBD port built in the car. The parameters are trained by a 5-layer deep-learning construction with input data (speed, acceleration, gradient) and labels (fuel consumption data) in the typical classification approach. The number of labels is about 6–8 and the number of neurons for hidden layers increases in proportionate to the label numbers. There are about 160–200 parameters. The parameters are calibrated to consider the wide range of fuel efficiency and deterioration degree in age for various test cars. The calibration factor is made from the certified fuel economy and model year taken from the car registration form. The error range of the estimated fuel economy from the measured value is about −6% to +7% for the eight test cars. It is accurate enough to capture the vehicle dynamics for using the input and output data in point-to-point classification style for training steps. Further, it is simple enough to hit fuel economy of the other test cars because fuel economy is a kind of averaged value of fuel consumption for the time period or driven distance for monitoring steps. You can predict or monitor energy consumption for any vehicle with the GPS-measured speed/acceleration/gradient data by the pre-trained parameters and calibration factors of the reference vehicles according to fuel types such as gasoline, diesel and electric. The proposed method requires just a GPS sensor that is cheap and common, and the calculating procedure is so simple that you can monitor energy consumption of various vehicles in real-time with ease. However, it does not consider weight, weather and auxiliary changes and these effects will be addressed in the future works with a monitoring service system under preparation.

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