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Articles de revues sur le sujet « Energy consumption model »

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Auteur : Grafiati
Publié le 8 juin 2024

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1

Gomez, Carles, Juan Carlos Veras, Rafael Vidal, Lluís Casals et Josep Paradells. « A Sigfox Energy Consumption Model ». Sensors 19, no 3 (7 février 2019) : 681. http://dx.doi.org/10.3390/s19030681.

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Sigfox has become one of the main Low-Power Wide Area Network (LPWAN) technologies, as it has attracted the attention of the industry, academy and standards development organizations in recent years. Sigfox devices, such as sensors or actuators, are expected to run on limited energy sources; therefore, it is crucial to investigate the energy consumption of Sigfox. However, the literature has only focused on this topic to a very limited extent. This paper presents an analytical model that characterizes device current consumption, device lifetime and energy cost of data delivery with Sigfox. In order to capture a realistic behavior, the model has been derived from measurements carried out on a real Sigfox hardware module. The model allows quantifying the impact of relevant Sigfox parameters and mechanisms, as well as frame losses, on Sigfox device energy performance. Among others, evaluation results show that the considered Sigfox device, powered by a 2400 mAh battery, can achieve a theoretical lifetime of 1.5 or 2.5 years while sending one message every 10 min at 100 bit/s or 600 bit/s, respectively, and an asymptotic lifetime of 14.6 years as the message transmission rate decreases.
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Lim, Ki Choo. « Development of Bottom-up model for Residential Energy Consumption by Use ». Journal of Energy Engineering 22, no 1 (31 mars 2013) : 38–43. http://dx.doi.org/10.5855/energy.2013.22.1.038.

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Jacewicz, Mariusz, Marcin Żugaj, Robert Głębocki et Przemysław Bibik. « Quadrotor Model for Energy Consumption Analysis ». Energies 15, no 19 (28 septembre 2022) : 7136. http://dx.doi.org/10.3390/en15197136.

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In this paper, a quadrotor dynamic model’s energy efficiency was investigated. A method for the design of the dynamic model which assures energy consumption estimation was presented. This model was developed to analyze the energy efficiency of the quadrotor during each maneuver. A medium-class quadrotor (4.689 kg) was used as a test platform. Thrust force correction factors obtained with FLIGHTLAB software were used to predict object behavior in forward flight. Model validation and long-duration flight tests in outdoor windy conditions are also presented. Monte-Carlo simulation was used to study the influence of uncertainties in model parameters on the simulation reliability. The developed model might be used for practical purposes (for example, energy-efficient coverage path planning).
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Li, Nan, Jing Zhao et Neng Zhu. « Building Energy Consumption Prediction Evaluation Model ». Advanced Materials Research 280 (juillet 2011) : 101–5. http://dx.doi.org/10.4028/www.scientific.net/amr.280.101.

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Building energy consumption prediction provides the possibility for regulating running condition of equipments in advance. Then the equipments will keep good movement and building energy consumption will reduce obviously. This paper built an energy consumption prediction evaluation model according to Matlab Artificial Neural Network Toolbox. The model was trained and simulated by operation data in June-September of 2008 and 2009 of a case building. Then it can be used to predict this building energy consumption by special data, such as meteorological characteristics of prediction year, operation load, operation time and energy consumption of last year. With more building samples, the model will be used in wide range of building energy consumption prediction.
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Darroudi, Seyed, Raül Caldera-Sànchez et Carles Gomez. « Bluetooth Mesh Energy Consumption : A Model ». Sensors 19, no 5 (12 mars 2019) : 1238. http://dx.doi.org/10.3390/s19051238.

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The recent publication of the Bluetooth Mesh standard is a remarkable milestone in the evolution of Bluetooth Low Energy (BLE). As a new technology in the Internet of Things (IoT) market, it is crucial to investigate the performance of Bluetooth Mesh. However, while a fundamental feature of Bluetooth Mesh is its suitability for energy-constrained devices, this aspect has not yet been properly considered in the literature. In this paper, we model the current consumption, lifetime and energy cost per delivered bit of a battery-operated Bluetooth Mesh sensor node. The model is based on measurements performed on a real hardware platform. Evaluation results quantify the impact of crucial Bluetooth Mesh parameters. Among others, we have found that a sensor device running on a simple 235 mAh battery, and sending a data message every 10 s, can achieve a lifetime of up to 15.6 months, whereas the asymptotic lifetime is 21.4 months.
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Naseri, Alireza, Ramin Vafaeipour Sorkhabi, Arash Dalili et Masoud Naseri. « Optimal Energy Consumption Model in Transport ». Energy Procedia 61 (2014) : 2379–82. http://dx.doi.org/10.1016/j.egypro.2014.12.009.

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Ma, Yun Long, Xiao Hua Chen, Bo Liu et Guo Feng Zhang. « Energy Efficiency Assessment Oriented Building Energy Consumption System Model ». Applied Mechanics and Materials 415 (septembre 2013) : 734–40. http://dx.doi.org/10.4028/www.scientific.net/amm.415.734.

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This paper analyzes the characteristics and composition of the energy consumption system of the building from the perspective of systematic energy conservation and presents the systematic framework of the consumption model. Based on the framework, the paper focuses on how to establish a building energy consumption assessment system, find the energy efficiency index system and assessment approaches, and apply the results directly into building energy conservation and emission reduction. It not only facilitates greatly the overall and efficient management of the energy consumption system of the building, but also serves as another new approach to achieve energy conservation and emission reduction.
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8

Wardhany, Arum Kusuma, Iwa Garniwa, Rima Kurnia Putri et Raja Dweyndo Hasintongan Gurning. « Energy Consumption Model for Open-Space Geometry Building : A Study Case in Jakarta ». Journal of Clean Energy Technologies 6, no 4 (juillet 2018) : 344–48. http://dx.doi.org/10.18178/jocet.2018.6.4.486.

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9

Kim, Ho-Young, Seul-Ye Lim et Seung-Hoon Yoo. « Analysis of residential natural gas consumption distribution function in Korea - a mixture model ». Journal of Energy Engineering 23, no 3 (30 septembre 2014) : 36–41. http://dx.doi.org/10.5855/energy.2014.23.3.036.

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Degefa, Mehari Weldemariam. « Ethiopian energy consumption forecast ». Mehran University Research Journal of Engineering and Technology 41, no 4 (1 octobre 2022) : 42. http://dx.doi.org/10.22581/muet1982.2204.04.

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This Energy consumption forecast is vital and has a great economic impact. Mathematical models developed for energy forecast can also serve as inputs for further studies. This study is intended to develop an energy consumption forecast using the grey prediction model GM (1,1), based on the actual energy consumption data from the year 2008 to 2017. The models are developed for the total, solid biomass, oil products, and electrical energy consumption; and the accuracy for each model is ratified. These developed forecasting models were used to anticipate six-year Ethiopian consumption of major energy types. The outcomes of models for all four energy consumption types show an upward trend; simulating and forecasting are found suited with the grey system model with development coefficient values less than 0.3 for all selected energy forms.
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Jin, Yi Bi, Fan Xu, Jin Wang et Shao Mei Fei. « Visualized Energy Consumption Monitoring Platform Based on Multi-Level Energy Consumption Model and WebGIS ». Applied Mechanics and Materials 409-410 (septembre 2013) : 542–47. http://dx.doi.org/10.4028/www.scientific.net/amm.409-410.542.

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This paper presents a multi-level energy consumption model including element model, device model, building model, organization model and region model, builds the visualized energy consumption monitoring platform, combines with WebGIS and Flash allowing a variety of user operations through map-display module, system-function module and database-support module. Corresponding present a multi-level energy consumption acquisition program based on the Mesh framework of ZigBee. Compared with the traditional energy consumption monitoring software, this system gives a better user interaction and friendliness.
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Mu, Tian, Geng Geng, Chongjie Wang et Ran Zou. « Evaluation approach of terminal energy consumption of public buildings based on a standardized model ». Indoor and Built Environment 31, no 4 (14 février 2022) : 1066–78. http://dx.doi.org/10.1177/1420326x211054429.

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The influencing degree of different factors on terminal energy consumption of public building varies distinctly. If the actual measured terminal energy consumption is directly analyzed, the comparison basis is often inconsistent, which may lead to errors of results. In order to evaluate the energy consumption based on a unified standard, eliminating the interference of influencing factors, the standardization and revision of the measurement value of the terminal energy consumption is necessary. By considering domestic public building samples of different climate regions as research objects, this research has introduced a dimensionality reduction and integration method for evaluating influencing factors that could affect the terminal energy consumption of public buildings based on principal component and clustering analysis. Then, the quantitative mathematical expressions of the relationship between terminal energy consumptions and influencing factors were established through multiple linear regression fitting, formulating the standardized mathematical model of the terminal energy consumption of public buildings. According to the energy consumption standardized model, the evaluation approach was provided and scored based on mode analysis method. This approach can evaluate terminal energy consumption level and energy efficiency potential, analyzing the level of terminal energy consumption of public buildings in China.
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Chen, Shuai, Ling Ling Chen et Ren Yi Shu. « Energy Consumption Model for Wireless Sensor Networks ». Advanced Materials Research 588-589 (novembre 2012) : 664–67. http://dx.doi.org/10.4028/www.scientific.net/amr.588-589.664.

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With the energy consumption, the new route may be reconstructed with energy sufficient nodes. In order to evaluate energy of nodes, an energy consumption model was raised to calculate node energy in wireless sensor networks. The model of nodes was setup in data structure, the energy consumption model was setup in the rule of energy consumption and the wireless radio model of wireless sensor networks was setup in the rule of wireless radio. The simulation algorithm was designed and realized. The result shows that the model can simulate energy consumption for wireless sensor networks which is helpful for route algorithm.
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14

Sun, Ying. « Establishment of Energy Consumption Model of Compressors ». Advanced Materials Research 468-471 (février 2012) : 1653–56. http://dx.doi.org/10.4028/www.scientific.net/amr.468-471.1653.

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The traditional methods used to select refrigerating compressors need a large calculation, take much time and contain some estimating errors. To establish the energy consumption model of compressors can imitate the performance curves of compressor. It makes selecting compressor without chart and only concerned with condensation temperature and evaporation temperature, which greatly improves the speed and quality of selecting compressor.
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15

Mouakher, Amira, Wissem Inoubli, Chahinez Ounoughi et Andrea Ko. « Expect : EXplainable Prediction Model for Energy ConsumpTion ». Mathematics 10, no 2 (14 janvier 2022) : 248. http://dx.doi.org/10.3390/math10020248.

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With the steady growth of energy demands and resource depletion in today’s world, energy prediction models have gained more and more attention recently. Reducing energy consumption and carbon footprint are critical factors for achieving efficiency in sustainable cities. Unfortunately, traditional energy prediction models focus only on prediction performance. However, explainable models are essential to building trust and engaging users to accept AI-based systems. In this paper, we propose an explainable deep learning model, called Expect, to forecast energy consumption from time series effectively. Our results demonstrate our proposal’s robustness and accuracy when compared to the baseline methods.
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16

Mubarakah, N., Suherman, M. Y. Al-Hakim et E. Warman. « Energy consumption model on WiMAX subscriber station ». IOP Conference Series : Materials Science and Engineering 309 (février 2018) : 012002. http://dx.doi.org/10.1088/1757-899x/309/1/012002.

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17

Liu, Xuemin, Man Zhang, Nan Hu, Hairui Yang et Junfu Lu. « Calculation model of coal comminution energy consumption ». Minerals Engineering 92 (juin 2016) : 21–27. http://dx.doi.org/10.1016/j.mineng.2016.01.008.

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18

Lutzenhiser, L. « A cultural model of household energy consumption ». Energy 17, no 1 (janvier 1992) : 47–60. http://dx.doi.org/10.1016/0360-5442(92)90032-u.

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19

Ma, Minglu, Min Su, Shuyu Li, Feng Jiang et Rongrong Li. « Predicting Coal Consumption in South Africa Based on Linear (Metabolic Grey Model), Nonlinear (Non-Linear Grey Model), and Combined (Metabolic Grey Model-Autoregressive Integrated Moving Average Model) Models ». Sustainability 10, no 7 (20 juillet 2018) : 2552. http://dx.doi.org/10.3390/su10072552.

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South Africa’s coal consumption accounts for 69.6% of the total energy consumption of South Africa, and this represents more than 88% of African coal consumption, taking the first place in Africa. Thus, predicting the coal demand is necessary, in order to ensure the supply and demand balance of energy, reduce carbon emissions and promote a sustainable development of economy and society. In this study, the linear (Metabolic Grey Model), nonlinear (Non-linear Grey Model), and combined (Metabolic Grey Model-Autoregressive Integrated Moving Average Model) models have been applied to forecast South Africa’s coal consumption for the period of 2017–2030, based on the coal consumption in 2000–2016. The mean absolute percentage errors of the three models are respectively 4.9%, 3.8%, and 3.4%. The forecasting results indicate that the future coal consumption of South Africa appears a downward trend in 2017–2030, dropping by 1.9% per year. Analysis results can provide the data support for the formulation of carbon emission and energy policy.
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20

Xu, Weifeng, Bin Yu, Yili Ni et Liguo Weng. « Energy budget targeting index model based on energy consumption dual control ». E3S Web of Conferences 385 (2023) : 01025. http://dx.doi.org/10.1051/e3sconf/202338501025.

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Aiming at the problems that it is difficult to avoid “one size fits all” when carrying out the dual control of energy consumption in local areas, the lack of clear rules and effective tools to support the dual control of energy consumption, and the asynchronous control of total energy consumption and intensity, a dual control of energy consumption targeting index model is proposed. According to the level of energy efficiency, different industries and enterprises are finely distinguished, and one industry, one index and one enterprise, one accounting are implemented. The priority of energy use is tilted to industries and enterprises with high energy efficiency levels, so that energy consumption is distributed more scientifically and reasonably among industries and enterprises within the industry, and the energy consumption structure of the whole region is optimized and reshaped.
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Rijsdijk, A. A., et G. Houter. « VALIDATION OF A MODEL FOR ENERGY CONSUMPTION, CO2 CONSUMPTION AND CROP PRODUCTION (ECP-MODEL). » Acta Horticulturae, no 328 (mars 1993) : 125–32. http://dx.doi.org/10.17660/actahortic.1993.328.9.

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Rijsdijk, A. A. « DEMONSTRATION OF A MODEL FOR ENERGY CONSUMPTION, CO2 CONSUMPTION AND CROP PRODUCTION (ECP-MODEL) ». Acta Horticulturae, no 312 (juillet 1992) : 117–18. http://dx.doi.org/10.17660/actahortic.1992.312.17.

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Abu, Rahaman, John Amakor, Rasaq Kazeem, Temilola Olugasa, Olusegun Ajide, Nosa Idusuyi, Tien-Chien Jen et Esther Akinlabi. « Modeling influence of weather variables on energy consumption in an agricultural research institute in Ibadan, Nigeria ». AIMS Energy 12, no 1 (2024) : 256–70. http://dx.doi.org/10.3934/energy.2024012.

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<abstract> <p>Climate change is having a significant impact on weather variables like temperature, humidity, precipitation, solar radiation, daylight duration, wind speed, etc. These weather variables are key indicators that affect electricity demand and consumption. Hence, understanding the significance of weather elements on energy needs and consumption is important to be able to adapt, strategize, and predict the effect of the changing climate on the required energy of an organization. This study aims to investigate the relationship between changing weather elements and electricity consumption, employing Multivariate Linear Regression (MLR), Support Vector Regressions (SVR), and Artificial Neural Network (ANN) models to predict the effect of weather changes on energy consumption. The following approaches were engaged for this study: Creating a catalog of weather elements and parameters of energy need or its consumption; analyzing and correlating electrical power consumption to weather factors; and developing prediction models—MLR, SVR, and ANN to predict the significance of the change in the variables of weather on the electrical energy consumption. Among the weather variables considered, temperature emerged as the most influential factor affecting electricity consumption, displaying the highest correlation. The monthly total pattern for electricity use for the case study area followed a similar pattern as the mean apparent temperature. Of the three models (MLR, SVR, and ANN) developed in this study, the ANN model yielded the best predictive performance, with Mean Square Error (MSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) of 2.733%, 1.292%, and 4.66%, respectively. Notably, the ANN model outperformed the other models (MLR and SVR) by more than 20% across the predictive performance metrics employed.</p> </abstract>
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Zhou, Yan, Hua Zhang, Wei Yan, Feng Ma, Gongfa Li et Wenjun Chang. « Energy consumption component analysis mathematical model of grinder energy unit ». International Journal of Computing Science and Mathematics 9, no 6 (2018) : 612. http://dx.doi.org/10.1504/ijcsm.2018.096316.

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Li, Gongfa, Wenjun Chang, Feng Ma, Wei Yan, Hua Zhang et Yan Zhou. « Energy consumption component analysis mathematical model of grinder energy unit ». International Journal of Computing Science and Mathematics 9, no 6 (2018) : 612. http://dx.doi.org/10.1504/ijcsm.2018.10017533.

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Huseynli, Bahman. « Gamification in Energy Consumption : A Model for Consumers’ Energy Saving ». International Journal of Energy Economics and Policy 14, no 1 (15 janvier 2024) : 312–20. http://dx.doi.org/10.32479/ijeep.14395.

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In this study, the possibility of saving energy with gamification designed applications was investigated, literature studies on this subject were examined and a gamification-based approach was followed for energy saving. From this point of view, the aim of the study is to propose a gamification-based model to make energy saving easier, fun, enjoyable and beneficial. This study is exploratory qualitative research using systematic literature review, synthesis and induction methods. Within the scope of the study, the literature was first examined. The literature review focused on learning gamification-based designs that encourage energy consumption reduction. After that, a new model was developed based on the general principles of gamification, the characteristics of consumer behavior and the energy consumption target, which is the main subject of the study. As a result of the study, a gamification-based model is proposed for household consumers to reduce their energy consumption and save energy. This gamification-based model includes making energy savings easier and more beneficial for consumers, as well as making it more fun and enjoyable. After that, the proposed model was analyzed based on the studies in the literature and finally the potential of the proposed framework was discussed.
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Terekh, Maksim, et Darya Tretyakova. « Primary energy consumption for insulating ». E3S Web of Conferences 157 (2020) : 06008. http://dx.doi.org/10.1051/e3sconf/202015706008.

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In this article a mathematical model for thermal protection level analysis is developed. It is based on the consumption rate of primary energy. It allows to calculate the relevant thickness of the selected insulation material under any climatic and economic conditions with any constant layers of building envelope taken from structural considerations. The key factors influencing the model are also evaluated. The main factors to influence the energy model are the region degree-days and the energy consumption rate for the production, transportation and installation of the insulation material. The following results were reached: this approach requires the data, which sometimes has no public access, provides us with an objective assessment criteria when comparing the level of building thermal protection in different countries.
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Gu, Wenbin, Zhuo Li, Zeyu Chen et Yuxin Li. « An energy-consumption model for establishing an integrated energy-consumption process in a machining system ». Mathematical and Computer Modelling of Dynamical Systems 26, no 6 (1 novembre 2020) : 534–61. http://dx.doi.org/10.1080/13873954.2020.1833045.

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Ha, Jinbing, et Ziyi Zhou. « Subway Energy Consumption Prediction based on XGBoost Model ». Highlights in Science, Engineering and Technology 70 (15 novembre 2023) : 548–52. http://dx.doi.org/10.54097/hset.v70i.13958.

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In the process of urban rail transit operation and management, accurate prediction of subway energy consumption is beneficial for establishing a reasonable operational organization mode and evaluating energy efficiency. Due to the multitude of factors affecting train energy consumption, traditional mathematical regression methods struggle to guarantee predictive accuracy. Thus, a energy consumption prediction method based on XGBoost is proposed. To enhance model training efficiency and accuracy, the Lasso model is utilized for feature selection of subway energy consumption influencing factors. Additionally, the K-means++ algorithm is employed for clustering subway energy consumption. Using the operational energy consumption data of Qingdao Subway Line 3 as an example for validation, XGBoost algorithm is employed to predict subway energy consumption. The results are then compared with those of the SVR and LSTM algorithms using three evaluation metrics. It is found that the XGBoost algorithm provides predictions of subway energy consumption that are closer to the experimental values.
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Nugroho, Fathul, et Noor Syaifudin. « Partial-factor Energy Efficiency Model of Indonesia ». MATEC Web of Conferences 164 (2018) : 01009. http://dx.doi.org/10.1051/matecconf/201816401009.

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This study employs the partial-factor energy efficiency to reveal the relationships between energy efficiency and the consumption of both, the renewable energy and non-renewable energy in Indonesia. The findings confirm that consumption of non-renewable energy will increase the inefficiency in energy consumption. On the other side, the use of renewable energy will increase the energy efficiency in Indonesia. As the result, the Government of Indonesia may address this issue by providing more support to the development and the consumption of renewable energy in order to increase the energy efficiency and vise versa.
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Fahmi Mustapa, Rijalul, NY Dahlan, Ihsan Mohd Yassin, Atiqah Hamizah Mohd Nordin et Azlee Zabidi. « Energy consumption prediction through linear and non-linear baseline energy model ». Indonesian Journal of Electrical Engineering and Computer Science 17, no 1 (1 janvier 2020) : 102. http://dx.doi.org/10.11591/ijeecs.v17.i1.pp102-109.

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<span>Accurate baseline energy models demand increase significantly as it lower the risk of energy savings quantification. It is achieved by performing energy consumption prediction with its respective independent variables through linear or non-linear modelling technique. Developing such model through linear modelling technique provide certain disadvantages due to the fact that the behavior of certain independent variables with respect to the energy consumption is non-linear in nature. Furthermore, linear modelling technique requires prior studies upon modelling to achieve accurate energy consumption prediction. Thus, to apprehend this situation, this paper main intention is to perform energy consumption prediction through a non-linear modelling technique to provide alternative option for developing a good and accurate baseline energy models. This study proposes energy consumption prediction based on Non-linear Auto Regressive with Exogenous Input – Artificial Neural Network (NARX-ANN) as a non-linear modelling technique that will be compared with Multiple Linear Regression Model (MLR) as linear modelling technique. A case study in Malaysian educational buildings during lecture week will be used for this purpose. The results demonstrate that NARX-ANN shows a higher accuracy through statistical error measurement.</span>
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Wang, Endong, et Zhigang Shen. « LIFECYCLE ENERGY CONSUMPTION PREDICTION OF RESIDENTIAL BUILDINGS BY INCORPORATING LONGITUDINAL UNCERTAINTIES ». Journal of Civil Engineering and Management 19, Supplement_1 (9 janvier 2014) : S161—S171. http://dx.doi.org/10.3846/13923730.2013.802744.

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Accurate prediction of buildings’ lifecycle energy consumption is a critical part in lifecycle assessment of residential buildings. Longitudinal variations in building conditions, weather conditions and building's service life can cause significant deviation of the prediction from the real lifecycle energy consumption. The objective is to improve the accuracy of lifecycle energy consumption prediction by properly modelling the longitudinal variations in residential energy consumption model using Markov chain based stochastic approach. A stochastic Markov model considering longitudinal uncertainties in building condition, degree days, and service life is developed: 1) Building's service life is estimated through Markov deterioration curve derived from actual building condition data; 2) Neural Network is used to project periodic energy consumption distribution for each joint energy state of building condition and temperature state; 3) Lifecycle energy consumption is aggregated based on Markov process and the state probability. A case study on predicting lifecycle energy consumption of a residential building is presented using the proposed model and the result is compared to that of a traditional deterministic model and three years’ measured annual energy consumptions. It shows that the former model generates much narrower distribution than the latter model when compared to the measured data, which indicates improved result.
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Bublyk, Yevhen, Olena Borzenko et Anna Hlazova. « Cryptocurrency energy consumption : Analysis, global trends and interaction ». Environmental Economics 14, no 2 (18 août 2023) : 49–59. http://dx.doi.org/10.21511/ee.14(2).2023.04.

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The rapid spread of cryptocurrencies is one of the most relevant trends today. One of the significant risks of their spread is the increase in energy consumption, which has a negative impact on the environment due to carbon emissions. This requires the development of a scientific toolkit for assessing relationships and predicting the impact of cryptocurrencies on energy consumption, which is the aim of this paper.With the correlational regression analysis, the model of the dependence of spending on IT sector, energy consumption of Bitcoin, Ethereum and global capitalization of the cryptocurrency market was conducted, based on statistical data from Statista.com, Сoinmarketcap.com and International Data Corporation. To check the possible relationship, tests for the adequacy of the results obtained (Fisher’s test, Student’s t-test) confirmed the correctness of coefficients for independent variables.The results showed a significant direct correlation (Multiple R is 95%) of spending on IT sector, energy consumption and global capitalization of the cryptocurrency market. The established relationships allowed predicting that Bitcoin energy consumption may reach 142 Terawatt hours per year in 2026. And its impact on environment by mining in 2022 was at least 27.4 Mt of CO2 emission.As a proposal, a conclusion was made on the expediency of linking mining to the use of certain sources of electricity production, such as “residual” natural gas, nuclear power, renewable energy sources. The obtained results and conclusions may be used as a basis for political decisions in the field of energy efficiency and climate change mitigation.
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Ünalan, Hakan, et Emrah Gökaltun. « Alternative window wall ratio of glasses with different solar heat gain coefficient and solar transmittance and their effect on total energy consumption in alternative directions ». Journal of Design for Resilience in Architecture and Planning 4, no 1 (30 avril 2023) : 122–35. http://dx.doi.org/10.47818/drarch.2023.v4i1087.

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Energy simulation model of the building of Eskişehir Technical University Industrial Engineering Department Academic and Administrative Staff rooms were created in this study carried in the scope of energy efficiency and performance of buildings. In the aforementioned energy simulation mode, in line with the International Measurement, Verification and Energy Needs Standards and Protocol (IPMVP) “energy consumption verification”; heating energy, indoor-outdoor environment and climate data were defined, energy consumption verification was carried out and a realistic model was achieved. Using the realistic model achieved, alternative directions were applied to alternative window wall ratios thereby calculating “reference energy consumptions” in “reference building models”. Energy consumptions, calculated by applying alternative glass types to reference models, were then compared with reference energy consumptions
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Pu, Tiecheng, et Jing Bai. « Predictive Model of Energy Consumption in Beer Production ». Journal of Engineering Science and Technology Review 6, no 2 (avril 2013) : 145–49. http://dx.doi.org/10.25103/jestr.062.30.

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Ozturk, Suat, et Feride Ozturk. « Forecasting Energy Consumption of Turkey by Arima Model ». Journal of Asian Scientific Research 8, no 2 (2018) : 52–60. http://dx.doi.org/10.18488/journal.2.2018.82.52.60.

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Li, Bing Bing, Hong Chao Zhang, Qing Di Ke, Li Ding et Lei Zhang. « Overview of Energy Consumption Model for Manufacturing Processes ». Applied Mechanics and Materials 130-134 (octobre 2011) : 2288–93. http://dx.doi.org/10.4028/www.scientific.net/amm.130-134.2288.

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The energy consumption for manufacturing processes is the largest impact contributor in various characterization categories, based on the assessment of environmental effects during the whole life cycle. It is necessary to investigate the manufacturing processes in depth to find out mechanism that can improve energy efficiency. This paper presents a comprehensive overview on two important aspects of energy consumption models for manufacturing processes: 1) two data collection methods: top-down and bottom-up; 2) two process-based analytical methods: thermodynamic model (including energy flow analysis and exergy analysis), and mechanical model. These models can improve energy efficiency.
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Ali, Jameel, et Majid Altamimi. « Energy consumption model for data transfer in smartphone ». Computer Communications 182 (janvier 2022) : 13–21. http://dx.doi.org/10.1016/j.comcom.2021.10.014.

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Aiello, Giuseppe, Rosalinda Inguanta, Giusj D’Angelo et Mario Venticinque. « Energy Consumption Model of Aerial Urban Logistic Infrastructures ». Energies 14, no 18 (21 septembre 2021) : 5998. http://dx.doi.org/10.3390/en14185998.

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In the last decade, logistic systems based on small aerial vehicles (drones) have become attractive for urban delivery operations as a sustainable alternative to ground vehicles because they are not affected by the congestion of the road network, thus allowing for faster and more reliable services. Aerial logistic systems, however, require a substantially different approach to operations management and need specifically designed supportive infrastructures. While the research on urban aerial delivery mostly focuses on the optimization vehicle operations, the appropriate design of supportive infrastructures is seldom considered. This paper focuses on the energy efficiency of aerial logistic systems, and proposes a new original methodology to obtain a realistic estimate of the overall energy consumed by a swarm of drones employed for urban delivery, taking into account the extension of the area served and its specific features. The methodology proposed offers relevant information for the decision problems related to the appropriate sizing of the infrastructures, the dimensioning of the swarm of drones and the capacity of the energy storage system. The paper also reports a comparison with ground vehicles in the same scenarios, showing the relevant tradeoffs. The results obtained demonstrate how an appropriate design of the supportive infrastructures for urban aerial logistics may significantly impact the overall efficiency of the delivery system.
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Ji, Haofan. « Energy Consumption Analysis Model of Typical Cogeneration Systems ». Journal of Physics : Conference Series 2160, no 1 (1 janvier 2022) : 012046. http://dx.doi.org/10.1088/1742-6596/2160/1/012046.

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Abstract At present, China’s urban heating system consumes a lot of energy and is seriously polluted. Our government is working hard to develop urban natural gas regional heating systems to replace traditional coal-fired heating to reduce the serious impact of coal combustion emissions on the urban atmospheric environment during the heating season. On this basis, the characteristics of traditional energy efficiency comparison methods and the problems encountered by these traditional methods in the energy efficiency analysis and application of distributed energy cold, hot and power multigeneration systems in China are analyzed, and the comparable performance efficiency analysis methods suitable for the application of cold, hot and hot power multiple production applications of distributed energy are studied.
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Huang, Xiaoqi, Honggui Han et Junfei Qiao. « Energy consumption model for wastewater treatment process control ». Water Science and Technology 67, no 3 (1 février 2013) : 667–74. http://dx.doi.org/10.2166/wst.2012.563.

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Wastewater treatment must satisfy discharge requirements under specified constraints and have minimal operating costs (OC). The operating results of wastewater treatment processes (WWTPs) have significantly focused on both the energy consumption (EC) and effluent quality (EQ). To reflect the relationship between the EC and EQ of WWTPs directly, an extended Elman neural network-based energy consumption model (EENN-ECM) was studied for WWTP control in this paper. The proposed EENN-ECM was capable of predicting EC values in the treatment process. Moreover, the self-adaptive characteristic of the EENN ensured the modeling accuracy. A performance demonstration was carried out through a comparison of the EC between the benchmark simulation model No.1 (BSM1) and the EENN-ECM. The experimental results demonstrate that this EENN-ECM is more effective to model the EC of WWTPs.
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Imani Asrai, Reza, Stephen T. Newman et Aydin Nassehi. « A mechanistic model of energy consumption in milling ». International Journal of Production Research 56, no 1-2 (20 novembre 2017) : 642–59. http://dx.doi.org/10.1080/00207543.2017.1404160.

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HENI, Maher. « Energy Consumption Model in Ad Hoc Mobile Network ». International journal of Computer Networks & ; Communications 4, no 3 (31 mai 2012) : 207–17. http://dx.doi.org/10.5121/ijcnc.2012.4314.

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Grady, P. L., G. N. Mock, G. A. Pai et K. W. Throneburg. « A General Purpose Textile Plant Energy Consumption Model ». Textile Research Journal 59, no 3 (mars 1989) : 177–82. http://dx.doi.org/10.1177/004051758905900307.

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Mehboob, Nafeesa, Hany E. Z. Farag et Abdullah M. Sawas. « Energy Consumption Model for Indoor Cannabis Cultivation Facility ». IEEE Open Access Journal of Power and Energy 7 (2020) : 222–33. http://dx.doi.org/10.1109/oajpe.2020.3003540.

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Torrealdea, Francisco J., Cecilia Sarasola et Alicia d’Anjou. « Energy consumption and information transmission in model neurons ». Chaos, Solitons & ; Fractals 40, no 1 (avril 2009) : 60–68. http://dx.doi.org/10.1016/j.chaos.2007.07.050.

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Tian, Shanjun, et Shiyan Chang. « An agent-based model of household energy consumption ». Journal of Cleaner Production 242 (janvier 2020) : 118378. http://dx.doi.org/10.1016/j.jclepro.2019.118378.

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Bel, Albert, Toni Adame et Boris Bellalta. « An energy consumption model for IEEE 802.11ah WLANs ». Ad Hoc Networks 72 (avril 2018) : 14–26. http://dx.doi.org/10.1016/j.adhoc.2018.01.005.

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Berl, Andreas, et Hermann de Meer. « An energy consumption model for virtualized office environments ». Future Generation Computer Systems 27, no 8 (octobre 2011) : 1047–55. http://dx.doi.org/10.1016/j.future.2011.04.010.

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Vilajosana, Xavier, Qin Wang, Fabien Chraim, Thomas Watteyne, Tengfei Chang et Kristofer S. J. Pister. « A Realistic Energy Consumption Model for TSCH Networks ». IEEE Sensors Journal 14, no 2 (février 2014) : 482–89. http://dx.doi.org/10.1109/jsen.2013.2285411.

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