[HTML][HTML] Theory-guided deep-learning for electrical load forecasting (TgDLF) via ensemble long short-term memory
Electricity constitutes an indispensable source of secondary energy in modern society.
Accurate and robust short-term electrical load forecasting is essential for more effective …
Accurate and robust short-term electrical load forecasting is essential for more effective …
[HTML][HTML] An adaptive deep-learning load forecasting framework by integrating transformer and domain knowledge
Electrical energy is essential in today's society. Accurate electrical load forecasting is
beneficial for better scheduling of electricity generation and saving electrical energy. In this …
beneficial for better scheduling of electricity generation and saving electrical energy. In this …
Missing well logs prediction using deep learning integrated neural network with the self-attention mechanism
J Wang, J Cao, J Fu, H Xu - Energy, 2022 - Elsevier
Well logs are employed for analyzing lithology, determining formation parameters, and
evaluating oil and gas reservoirs. However, in practice, well logs are often incomplete or …
evaluating oil and gas reservoirs. However, in practice, well logs are often incomplete or …
S-wave velocity inversion and prediction using a deep hybrid neural network
J Wang, J Cao, S Zhao, Q Qi - Science China Earth Sciences, 2022 - Springer
The S-wave velocity is a critical petrophysical parameter in reservoir description, prestack
seismic inversion, and geomechanical analysis. However, obtaining the S-wave velocity …
seismic inversion, and geomechanical analysis. However, obtaining the S-wave velocity …
Missing sonic logs generation for gas hydrate-bearing sediments via hybrid networks combining deep learning with rock physics modeling
Logging-while-drilling (LWD) sonic data are critical for marine gas hydrate reservoir
evaluation and production prediction. However, acquiring complete acoustic logs …
evaluation and production prediction. However, acquiring complete acoustic logs …
A vector-to-sequence based multilayer recurrent network surrogate model for history matching of large-scale reservoir
History matching can estimate the parameter of spatially varying geological properties and
provide reliable numerical models for reservoir development and management. However, in …
provide reliable numerical models for reservoir development and management. However, in …
Fault diagnosis, service restoration, and data loss mitigation through multi-agent system in a smart power distribution grid
Smart power distribution grid is equipped with different sensors and smart meters for getting
measurements at different nodes. Additionally, for monitoring and control purpose Intelligent …
measurements at different nodes. Additionally, for monitoring and control purpose Intelligent …
An expert system for insect pest population dynamics prediction
Avocado (Persea americana) production is increasing in Kenya, with both small and
largeholder farming for domestic and export markets. However, one of main challenges that …
largeholder farming for domestic and export markets. However, one of main challenges that …
A method for well log data generation based on a spatio-temporal neural network
Well logging helps geologists find hidden oil, natural gas and other resources. However,
well log data are systematically insufficient because they can only be obtained by drilling …
well log data are systematically insufficient because they can only be obtained by drilling …
MS-CGAN: Fusion of conditional generative adversarial networks and multi-scale spatio-temporal features for lithology identification
P Zhang, J Ren, F Zhao, X Li, H He, Y Jia… - Journal of Applied …, 2024 - Elsevier
Lithology identification constitutes a crucial undertaking in formation evaluation and
reservoir characterization. However, the need for improved precision arises in conventional …
reservoir characterization. However, the need for improved precision arises in conventional …