Resiliency of forecasting methods in different application areas of smart grids: A review and future prospects
The cyber–physical infrastructure of a smart grid requires data-dependent artificial
intelligence (AI)-based forecasting schemes for predicting different aspects for the short-to …
intelligence (AI)-based forecasting schemes for predicting different aspects for the short-to …
[HTML][HTML] ELFNet: An Effective Electricity Load Forecasting Model Based on a Deep Convolutional Neural Network with a Double-Attention Mechanism
P Zhao, G Ling, X Song - Applied Sciences, 2024 - mdpi.com
Forecasting energy demand is critical to ensure the steady operation of the power system.
However, present approaches to estimating power load are still unsatisfactory in terms of …
However, present approaches to estimating power load are still unsatisfactory in terms of …
Feature Transfer and Rapid Adaptation for Few-Shot Solar Power Forecasting
X Ren, Y Wang, Z Cao, F Chen, Y Li, J Yan - Energies, 2023 - mdpi.com
A common dilemma with deep-learning-based solar power forecasting models is their heavy
dependence on a large amount of training data. Few-Shot Solar Power Forecasting (FSSPF) …
dependence on a large amount of training data. Few-Shot Solar Power Forecasting (FSSPF) …
An adaptive trimming approach to Bayesian additive regression trees
T Cao, J Wu, YG Wang - Complex & Intelligent Systems, 2024 - Springer
A machine learning technique merging Bayesian method called Bayesian Additive
Regression Trees (BART) provides a nonparametric Bayesian approach that further needs …
Regression Trees (BART) provides a nonparametric Bayesian approach that further needs …
Long-term load forecasting for smart grid
V Kumar, RK Mandal - Engineering Research Express, 2024 - iopscience.iop.org
The load forecasting problem is a complicated non-linear problem connected with the
weather, economy, and other complex factors. For electrical power systems, long-term load …
weather, economy, and other complex factors. For electrical power systems, long-term load …
Data-Driven Resilient Load Forecasting Model for Smart Metered Distribution System
The change in the electricity demand pattern globally due to sudden extreme weather
conditions or situations like COVID 19 pandemic has brought unanticipated challenges for …
conditions or situations like COVID 19 pandemic has brought unanticipated challenges for …
Review for Smart Grid Forecast
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Account Menu Find a journal Publish with us Track your research Search Cart Book cover …
Account Menu Find a journal Publish with us Track your research Search Cart Book cover …
[PDF][PDF] An Adaptive Trimmed Bayesian Additive Regression Trees
T Cao, J Wu, YG Wang - researchgate.net
A machine learning technique merging Bayesian method called Bayesian Additive
Regression Trees (BART) provides a nonparametric Bayesian approach that further needs …
Regression Trees (BART) provides a nonparametric Bayesian approach that further needs …