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Dec 29, 2023 · This paper proposes a methodology based on machine learning (ML) techniques for short-term kinetic energy forecasting available in power systems.
Aug 29, 2024 · It examines the entire forecasting process, including data pre-processing, feature extraction, deep-learning modeling and optimization, and results evaluation.
Sep 7, 2024 · This paper examines various present technologies and algorithms of load forecasting including digital twin (DT), data-mining (DM), federated learning (FL), and ...
May 10, 2024 · A dedicated Short-Term Power Load Forecasting (STPLF) framework for IES is proposed, which relies on a newly developed hybrid deep learning architecture.
Missing: Machines | Show results with:Machines
Mar 21, 2024 · This paper proposes a short-term PV power forecasting method based on a hybrid model of temporal convolutional networks and gated recurrent units
Missing: Machines | Show results with:Machines
Nov 20, 2023 · In this paper, we propose a short-term load forecasting framework based on graph neural networks and dilated 1D-CNN, called GLFN-TC.
Missing: Production Machines
Jul 19, 2024 · In this paper, a two-stage STLF model based on long short-term memory (LSTM) and multilayer perceptron (MLP), which improves the forecasting accuracy over the ...
Sep 6, 2024 · This machine learning technique allows a model to quickly adapt to new tasks by learning from past experiences.
Missing: Production | Show results with:Production
Feb 8, 2024 · This study focuses on energy forecasting in Iraq, using a previously unstudied dataset from 2019 to 2021, sourced from the Iraqi Ministry of Electricity.
4 days ago · These models are trained on a vast amount of time series data and are able to forecast time series without explicit task-specific training (zero-shot learning).