Enhancing user experience in VR environments through AI-driven adaptive UI design
This paper presents a new approach to improving user experience in virtual reality (VR)
environments using AI-driven user interface (UI) design. The proposed system uses …
environments using AI-driven user interface (UI) design. The proposed system uses …
Dense skip attention based deep learning for day-ahead electricity price forecasting
The forecasting of the day-ahead electricity price (DAEP) has become more of interest to
decision makers in the liberalized market, as it can help optimize bidding strategies and …
decision makers in the liberalized market, as it can help optimize bidding strategies and …
A review of control strategies for optimized microgrid operations
Microgrids (MGs) have emerged as a promising solution for providing reliable and
sustainable electricity, particularly in underserved communities and remote areas …
sustainable electricity, particularly in underserved communities and remote areas …
Short-Term Electricity Price Forecasting Based on the Two-Layer VMD Decomposition Technique and SSA-LSTM
F Guo, S Deng, W Zheng, A Wen, J Du, G Huang… - Energies, 2022 - mdpi.com
Accurate electricity price forecasting (EPF) can provide a necessary basis for market
decision making by power market participants to reduce the operating cost of the power …
decision making by power market participants to reduce the operating cost of the power …
Mining latent patterns with multi-scale decomposition for electricity demand and price forecasting using modified deep graph convolutional neural networks
Accurate forecasting of time series of electrical demand and prices facilitate power system
operators and planners allocate resources efficiently. A novel approach for mining latent …
operators and planners allocate resources efficiently. A novel approach for mining latent …
Day-ahead electricity price forecasting employing a novel hybrid frame of deep learning methods: A case study in NSW, Australia
YQ Tan, YX Shen, XY Yu, X Lu - Electric Power Systems Research, 2023 - Elsevier
Day-ahead electricity price forecasting plays a vital role in electricity markets under
liberalization and deregulation, which can provide references for participants in bidding …
liberalization and deregulation, which can provide references for participants in bidding …
Optimizing bidding strategy in electricity market based on graph convolutional neural network and deep reinforcement learning
Formulating optimal bidding strategies is pivotal for market participants to enhance electricity
market profits. The main challenge for finding optimal bidding strategies is how to deal with …
market profits. The main challenge for finding optimal bidding strategies is how to deal with …
Locational marginal price forecasting using svr-based multi-output regression in electricity markets
Electricity markets provide valuable data for regulators, operators, and investors. The use of
machine learning methods for electricity market data could provide new insights about the …
machine learning methods for electricity market data could provide new insights about the …
Graph convolutional networks-based method for uncertainty quantification of building design loads
Uncertainty quantification of building design loads is essential to efficient and reliable
building energy planning in the design stage. Current data-driven methods struggle to …
building energy planning in the design stage. Current data-driven methods struggle to …
Multi-task Graph Adaptive Learning for Multivariate Electricity Price Short-Term Forecasting in Australia's National Electricity Market
Accurate electricity price short-term forecasting plays an essential role in the digitization of
the electricity market. However, due to the expansion of renewable energy resources and …
the electricity market. However, due to the expansion of renewable energy resources and …