A new approach to analyse conditional demand: An application to Australian energy consumption

2021 ◽  
Vol 93 ◽  
pp. 105037
Author(s):  
Saroja Selvanathan ◽  
E.A. Selvanathan ◽  
Maneka Jayasinghe
Keyword(s):  
Energy Consumption ◽  
New Approach

Energy Consumption in Discrete Part Production

2011 ◽  
Author(s):  
Devi K. Kalla ◽  
Samantha Corcoran ◽  
Janet Twomey ◽  
Michael Overcash
Keyword(s):  
Energy Consumption ◽  
Life Cycles ◽  
Manufacturing Processes ◽  
New Approach ◽  
Energy Data ◽  
Important Improvement ◽  
Drilling Unit ◽  
Cradle To Gate ◽  
Discrete Part ◽  
Manufacturing Machine

It is widely recognized that industrial production inevitably results in an environmental impact. Energy consumption during production is responsible for a part of this impact, but is often not provided in cradle-to-gate life cycles. Transparent description of the transformation of materials, parts, and chemicals into products is described herein as a means to improve the environmental profile of products and manufacturing machine. This paper focuses on manufacturing energy and chemicals/materials required at the machine level and provides a methodology to quantify the energy consumed and mass loss for simple products in a manufacturing setting. That energy data are then used to validate the new approach proposed by (Overcash et.al, 2009a, and 2009b) for drilling unit processes. The approach uses manufacturing unit processes as the basis for evaluating environmental impacts at the manufacturing phase of a product’s life cycle. Examining manufacturing processes at the machine level creates an important improvement in transparency which aids review and improvement analyses.

Green DRCT

2017 ◽  
Vol 7 (1) ◽  
pp. 14-29
Author(s):  
Sangharatna Godboley ◽  
Arpita Dutta ◽  
Durga Prasad Mohapatra
Keyword(s):  
Energy Consumption ◽  
Software Testing ◽  
Transformation Method ◽  
New Approach ◽  
Code Coverage ◽  
Coverage Metrics ◽  
Branch Coverage ◽  
Concolic Testing ◽  
Green Software ◽  
Environment Sustainability

Being a good software testing engineer, one should have the responsibility towards environment sustainability. By using green principles and regulations, we can perform Green Software Testing. In this paper, we present a new approach to enhance Branch Coverage and Modified Condition/Decision Coverage uses concolic testing. We have proposed a novel transformation technique to improve these code coverage metrics. We have named this new transformation method Double Refined Code Transformer (DRCT). Then, using JoulMeter, we compute the power consumption and energy consumption in this testing process. We have developed a tool named Green-DRCT to measure energy consumption while performing the testing process.

scholarly journals A New Approach of EV Modeling and its Control Applications to Reduce Energy Consumption

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 141209-141225 ◽  
Author(s):  
Rina Ristiana ◽  
Arief Syaichu Rohman ◽  
Carmadi Machbub ◽  
Agus Purwadi ◽  
Estiko Rijanto
Keyword(s):  
Energy Consumption ◽  
Control Applications ◽  
New Approach ◽  
Reduce Energy Consumption

scholarly journals Enhancement of energy consumption estimation for electric vehicles by using machine learning

2021 ◽  
Vol 10 (1) ◽  
pp. 215
Author(s):  
Adnane Cabani ◽  
Peiwen Zhang ◽  
Redouane Khemmar ◽  
Jin Xu
Keyword(s):  
Machine Learning ◽  
Energy Consumption ◽  
Electric Vehicles ◽  
Optimal Path ◽  
Influence Factors ◽  
Air Conditioner ◽  
Driver Behaviour ◽  
Variable Parameters ◽  
New Approach

<p>Three main classes are considered of significant influence factors when predicting the energy consumption rate of electric vehicles (EV): environment, driver behaviour, and vehicle. These classes take into account constant or variable parameters which influences the energy consumption of the EV. In this paper, we develop a new model taking into account the three classes as well as the interaction between them in order to improve the quality of EV energy consumption. The model depends on a new approach based on machine learning and especially k-NN algorithm in order to estimate the EV energy consumption. Following a lazy learning paradigm, this approach allows better estimation performance. The advantage of our proposal, in regards to mathematical approach, is taking into account the real situation of the ecosystem on the basis of historical data. In fact, the behavior of the driver (driving style, heating usage, air conditioner usage, battery state, etc.) impacts directly the EV energy consumption. The obtained results show that we can reach up to 96.5% of accuracy about the estimated of energy-consumption. The proposed method is used in order to find the optimal path between two points (departure-destination) in terms of energy consumption.</p>

scholarly journals A new approach to the fabrication of VO2 nanoswitches with ultra-low energy consumption

Nanoscale ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 3443-3454
Author(s):  
Victor Ya. Prinz ◽  
Sergey V. Mutilin ◽  
Lyubov V. Yakovkina ◽  
Anton K. Gutakovskii ◽  
Alexander I. Komonov
Keyword(s):  
Energy Consumption ◽  
Single Crystals ◽  
High Stability ◽  
Low Energy ◽  
New Approach ◽  
Low Energy Consumption

The use of VO2 single crystals with embedded nanotips leads to the 4.2 fJ energy consumption per switching and ensures a high stability and endurance of the nanoswitches.

Understanding the Energy Behavior of Building Occupants Through the Chronology of Their Energy Interactions

2021 ◽  
Author(s):  
Danielle Preziuso ◽  
Gregory Kaminski ◽  
Philip Odonkor
Keyword(s):  
Energy Consumption ◽  
Energy Demand ◽  
Building Energy ◽  
Building Energy Consumption ◽  
New Approach ◽  
Smart Building ◽  
Energy Behavior ◽  
Human Energy ◽  
Sequential Nature ◽  
Consumption Habits

Abstract The energy consumption of buildings has traditionally been driven by the consumption habits of building occupants. However, with the proliferation of smart building technologies and appliances, automated machine decisions are beginning to impart their influence on building energy behavior as well. This is giving rise to a disconnect between occupant energy behavior and the overall energy consumption of buildings. Consequently, researchers can no longer leverage building energy consumption as a proxy for understanding human energy behavior. This paper addresses this problem by exploiting the habitual and sequential nature of human energy consumption. By studying the chronology of human energy actions, the results of this work present a promising new approach for non-intrusively learning about human energy behavior directly from building energy demand data.

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