[HTML][HTML] Modeling energy demand—a systematic literature review
PA Verwiebe, S Seim, S Burges, L Schulz… - Energies, 2021 - mdpi.com
In this article, a systematic literature review of 419 articles on energy demand modeling,
published between 2015 and 2020, is presented. This provides researchers with an …
published between 2015 and 2020, is presented. This provides researchers with an …
Forecasting China's electricity consumption using a new grey prediction model
A modified grey prediction model is employed to accurately forecast China's overall and
industrial electricity consumption. To this end, a novel optimized grey prediction model …
industrial electricity consumption. To this end, a novel optimized grey prediction model …
[HTML][HTML] Critical determinants of household electricity consumption in a rapidly growing city
Despite growing urban electricity consumption, information on actual energy use in the
household sector is still limited and causal factors leading to electricity consumption remain …
household sector is still limited and causal factors leading to electricity consumption remain …
Short-term prediction of COVID-19 spread using grey rolling model optimized by particle swarm optimization
Z Ceylan - Applied soft computing, 2021 - Elsevier
The prediction of the spread of coronavirus disease 2019 (COVID-19) is vital in taking
preventive and control measures to reduce human health damage. The Grey Modelling (1 …
preventive and control measures to reduce human health damage. The Grey Modelling (1 …
Optimal forecast combination based on neural networks for time series forecasting
Research indicates that forecast combination is one of the most important and effective
approaches for time series forecasting. The success of forecast combination depends on …
approaches for time series forecasting. The success of forecast combination depends on …
Exploration of low-cost green transition opportunities for China's power system under dual carbon goals
K Yuan, T Zhang, X Xie, S Du, X Xue… - Journal of Cleaner …, 2023 - Elsevier
To address the global challenge of climate change and achieve sustainable development,
decarbonizing the electric power system in China is considered the most imperative since …
decarbonizing the electric power system in China is considered the most imperative since …
A scalable approach based on deep learning for big data time series forecasting
JF Torres, A Galicia, A Troncoso… - Integrated Computer …, 2018 - content.iospress.com
This paper presents a method based on deep learning to deal with big data times series
forecasting. The deep feed forward neural network provided by the H2O big data analysis …
forecasting. The deep feed forward neural network provided by the H2O big data analysis …
Using a self-adaptive grey fractional weighted model to forecast Jiangsu's electricity consumption in China
X Zhu, Y Dang, S Ding - Energy, 2020 - Elsevier
The remarkable prediction performance of electricity consumption has always assumed
particular importance for electric power utility planning and economic development. On …
particular importance for electric power utility planning and economic development. On …
A hybrid optimized grey seasonal variation index model improved by whale optimization algorithm for forecasting the residential electricity consumption
X Xiong, X Hu, H Guo - Energy, 2021 - Elsevier
Forecasting the residential electricity consumption can provide an effective and sustainable
electricity supply in the rapid development of urbanization and industrialization. However, it …
electricity supply in the rapid development of urbanization and industrialization. However, it …
Exploiting fractional accumulation and background value optimization in multivariate interval grey prediction model and its application
H Huang, Z Tao, J Liu, J Cheng, H Chen - Engineering Applications of …, 2021 - Elsevier
In the context of small sample and poor information, the data often change rapidly and
interact with multiple factors which make it a challenge to analyse and predict multivariate …
interact with multiple factors which make it a challenge to analyse and predict multivariate …