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[HTML][HTML] Next-generation energy systems for sustainable smart cities: Roles of transfer learning
Smart cities attempt to reach net-zero emissions goals by reducing wasted energy while
improving grid stability and meeting service demand. This is possible by adopting next …
improving grid stability and meeting service demand. This is possible by adopting next …
Development and application of an evolutionary deep learning framework of LSTM based on improved grasshopper optimization algorithm for short-term load …
Accurate short-term load forecasting (STLF) plays an important role in the daily operation of
a smart grid. In order to forecast short-term load more effectively, this article proposes an …
a smart grid. In order to forecast short-term load more effectively, this article proposes an …
Pseudo-correlation problem and its solution for the transfer forecasting of short-term natural gas loads
N Wei, L Yin, C Yin, J Liu, S Wang, W Qiao… - Gas Science and …, 2023 - Elsevier
Considering the information protection of users and system failure of natural gas enterprises,
complete large-scale data are difficult to obtain, which is a critical issue for data-driven …
complete large-scale data are difficult to obtain, which is a critical issue for data-driven …
Optimal siting and sizing of EV charging station using stochastic power flow analysis for voltage stability
Existing literature on planning for electric vehicle charging station (EVCS) fails to consider
uncertain factors in power systems, such as load fluctuations and the impact of EV …
uncertain factors in power systems, such as load fluctuations and the impact of EV …
Energy metaverse: the conceptual framework with a review of the state-of-the-art methods and technologies
The transition to green energy systems is vital for addressing climate change, with a focus
on renewable sources like wind and solar. This change requires substantial investment …
on renewable sources like wind and solar. This change requires substantial investment …
[HTML][HTML] Optimal EV scheduling and voltage security via an online bi-layer steady-state assessment method considering uncertainties
Steady-state voltage security region (SVSR) is an important criterion that can intuitively and
effectively evaluate the power system's operation security and overall safety margin …
effectively evaluate the power system's operation security and overall safety margin …
A short-term energy consumption forecasting method for attention mechanisms based on spatio-temporal deep learning
M Han, L Fan - Computers and Electrical Engineering, 2024 - Elsevier
Short-term energy consumption forecasting is the foundation of anomaly detection,
scheduling and energy-saving control in manufacturing system. Existing methods do not …
scheduling and energy-saving control in manufacturing system. Existing methods do not …
An improved transfer learning strategy for short-term cross-building energy prediction using data incremental
The available modelling data shortage issue makes it difficult to guarantee the performance
of data-driven building energy prediction (BEP) models for both the newly built buildings and …
of data-driven building energy prediction (BEP) models for both the newly built buildings and …
Transfer learning for renewable energy systems: A survey
Currently, numerous machine learning (ML) techniques are being applied in the field of
renewable energy (RE). These techniques may not perform well if they do not have enough …
renewable energy (RE). These techniques may not perform well if they do not have enough …
A multi-information fusion model for short term load forecasting of an architectural complex considering spatio-temporal characteristics
Building load forecasting is critical for energy saving and carbon emission reduction, as
building loads account for a rising percentage of energy consumption. Existing load …
building loads account for a rising percentage of energy consumption. Existing load …