Big data analytics for future electricity grids
This paper provides a survey of big data analytics applications and associated
implementation issues. The emphasis is placed on applications that are novel and have …
implementation issues. The emphasis is placed on applications that are novel and have …
Short-term power load probability density forecasting based on GLRQ-Stacking ensemble learning method
Y He, J Xiao, X An, C Cao, J Xiao - … Journal of Electrical Power & Energy …, 2022 - Elsevier
The high penetration rate of distributed energy brings severe challenges to the dispatch and
operation of power systems. Improving the accuracy of short-term power load forecasting …
operation of power systems. Improving the accuracy of short-term power load forecasting …
Multi-space collaboration framework based optimal model selection for power load forecasting
H Xian, J Che - Applied Energy, 2022 - Elsevier
In recent years, power load forecasting has become a hot and open issue in the field of
energy. However, the optimal model selection for power load forecasting is a tricky problem …
energy. However, the optimal model selection for power load forecasting is a tricky problem …
Forecasting electricity consumption in China's Pearl River Delta urban agglomeration under the optimal economic growth path with low-carbon goals: Based on data …
Y Rao, X Wang, H Li - Energy, 2024 - Elsevier
To reflect the trend of electricity consumption (EC) in China's Pearl River Delta (PRD) under
a balanced environment and economy, this paper proposes an EC forecasting framework …
a balanced environment and economy, this paper proposes an EC forecasting framework …
Impact of COVID-19 on electricity demand of Latin America and the Caribbean countries
EF Sánchez-Úbeda, J Portela, A Muñoz… - … Energy, Grids and …, 2022 - Elsevier
Governments worldwide have adopted different public health measures in order to slow
down the spread of COVID-19. As a result, the electricity demand has been impacted by the …
down the spread of COVID-19. As a result, the electricity demand has been impacted by the …
An effect of machine learning techniques in electrical load forecasting and optimization of renewable energy sources
SK Panda, P Ray - Journal of The Institution of Engineers (India): Series B, 2022 - Springer
The prediction of the load from a day ahead or a week ahead is called short-term load
forecasting. STLF using ANN gives better results in the power grid because the construction …
forecasting. STLF using ANN gives better results in the power grid because the construction …
Optimal Selection of Weather Stations for Electric Load Forecasting
For the proper planning and operation of any electric power system, it is essential to have a
reliable software tool that allows for accurate short-term forecasting of electric power …
reliable software tool that allows for accurate short-term forecasting of electric power …
[PDF][PDF] Artificial neural network based short term electrical load forecasting
In power generation, a 24-hour load profile can vary significantly throughout the day.
Therefore, power generation must be adjusted to reduce money loss due to excess …
Therefore, power generation must be adjusted to reduce money loss due to excess …
[HTML][HTML] msf, a forecasting library to predict short-term electricity demand based on multiple seasonal time series
Transmission system operators have a growing need for more accurate forecasting of
electricity demand. Current electricity systems largely require demand forecasting so that the …
electricity demand. Current electricity systems largely require demand forecasting so that the …
[HTML][HTML] Discovering Electric Vehicle Charging Locations Based on Clustering Techniques Applied to Vehicular Mobility Datasets
E Magsino, FMM Espiritu, KD Go - ISPRS International Journal of Geo …, 2024 - mdpi.com
With the proliferation of vehicular mobility traces because of inexpensive on-board sensors
and smartphones, utilizing them to further understand road movements have become easily …
and smartphones, utilizing them to further understand road movements have become easily …