搜索页_Energy+consumption+forecasting

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在“Elsevier电子期刊”中，命中：352条，耗时：0.0739925 秒

在所有数据库中总计命中：4,543条

1.Forecasting Chinese carbon emissions from fossil energy consumption using non-linear grey multivariable models

作者：Zheng-Xin Wang^a ; ^b ; ^{zxwang@zufe.edu.cn} ; De-Jun Ye^b

关键词：Fossil energy consumption ; Carbon emission ; Economic growth ; Non-linear grey multivariable model ; Forecasting

刊名：Journal of Cleaner Production

出版年：2017

2.A new strategy based on power demand forecasting to the management of multi-energy district boilers equipped with hot water tanks

作者：Mouchira Labidi^a ; ^b ; Julien Eynard^b ; ^c ; Olivier Faugeroux^b ; ^c ; Sté ; phane Grieu^b ; ^c ; ^{stephane.grieu@promes.cnrs.fr}

关键词：Multi-energy district boiler ; Thermal energy storage ; Predictive strategy ; MPC ; Power demand forecasting

刊名：Applied Thermal Engineering

出版年：2017

3.Forecasting how residential urban form affects the regional carbon savings and costs of retrofitting and decentralized energy supply

作者：Anthony Hargreaves^a ; ^{a.j.hargreaves@bham.ac.uk} ; Vicky Cheng^b ; Sandip Deshmukh^c ; Matthew Leach^d ; Koen Steemers^e

关键词：Building-scale ; Sustainable technologies ; Housing typologies ; Urban modelling ; Decarbonisation of supply

刊名：Applied Energy

出版年：2017

4.Novel grey prediction model with nonlinear optimized time response method for forecasting of electricity consumption in China

作者：Ning Xu^a ; ^{nuaa_xuning@163.com} ; ^{xuning@nuaa.edu.cn} ; Yaoguo Dang^b ; Yande Gong^a

关键词：Grey prediction model ; Particle swarm optimization ; Initial value ; Electricity consumption

刊名：Energy

出版年：2017

5.A grey neural network and input-output combined forecasting model. Primary energy consumption forecasts in Spanish economic sectors

作者：Xiuli Liu^a ; ^b ; Blanca Moreno^c ; ^{morenob@uniovi.es" class="auth_mail" title="E-mail the corresponding author} ; Ana Salomé ; Garcí ; a^c

关键词：Primary energy consumption ; Combined forecasting model ; Input-output ; Grey methods

刊名：Energy

出版年：2016

6.Long-term electric energy consumption forecasting via artificial cooperative search algorithm

作者：S. Hr. Aghay Kaboli ; J. Selvaraj ; N.A. Rahim ; ^{nasrudin@um.edu.my" class="auth_mail" title="E-mail the corresponding author}

关键词：Artificial cooperative search ; Economic indicator ; Electric energy consumption ; Forecasting

刊名：Energy

出版年：2016

7.Time series forecasting for building energy consumption using weighted Support Vector Regression with differential evolution optimization technique

作者：Fan Zhang^a ; Chirag Deb^b ; ^{chirag.deb@u.nus.edu" class="auth_mail" title="E-mail the corresponding author} ; ^{chirag.arian@gmail.com" class="auth_mail" title="E-mail the corresponding author} ; Siew Eang Lee^b ; Junjing Yang^b ; Kwok Wei Shah^b

关键词：Building energy consumption ; Energy forecasting ; Support vector regression ; Differential evolution algorithm ; Institutional building

刊名：Energy & Buildings

出版年：2016

8.Energy Forecasting for Event Venues: Big Data and Prediction Accuracy

作者：Katarina Grolinger^a ; ^{kgroling@uwo.ca" class="auth_mail" title="E-mail the corresponding author} ; Alexandra L&rsquo ; Heureux^a ; ^{alheure2@uwo.ca" class="auth_mail" title="E-mail the corresponding author} ; Miriam A.M. Capretz^a ; ^{mcapretz@uwo.ca" class="auth_mail" title="E-mail the corresponding author} ; Luke Seewald^b ; ^{seewaldl@londonhydro.com" class="auth_mail" title="E-mail the corresponding author}

关键词：Energy prediction ; Energy forecasting ; Smart meters ; Big Data ; Sensor-based forecasting ; Machine learning

刊名：Energy & Buildings

出版年：2016

9.Modeling and forecasting energy consumption for residential buildings in Algeria using bottom-up approach

作者：Ré ; bha Ghedamsi^a ; ^{rghedamsi@gmail.com" class="auth_mail" title="E-mail the corresponding author} ; Noureddine Settou^a ; Abderrahmane Gouareh^b ; Adem Khamouli^c ; Nadia Saifi^a ; Bakhta Recioui^b ; Boubekker Dokkar^a

关键词：Energy consumption ; Residential building ; Forecasting ; Bottom-up model ; GIS

刊名：Energy & Buildings

出版年：2016

10.Application of ARIMA for forecasting energy consumption and GHG emission: A case study of an Indian pig iron manufacturing organization

作者：Parag Sen^a ; ^{paragbelurmath@gmail.com" class="auth_mail" title="E-mail the corresponding author} ; Mousumi Roy^b ; Parimal Pal^a

关键词：Environmentally conscious manufacturing programs ; ARIMA ; Forecasting ; Energy consumption ; GHG emission ; Pig iron manufacturing

刊名：Energy

出版年：2016

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