[HTML][HTML] Artificial intelligence for electricity supply chain automation
Abstract The Electricity Supply Chain is a system of enabling procedures to optimize
processes ranging from production to transportation and consumption of electricity. The …
processes ranging from production to transportation and consumption of electricity. The …
Aggregating prophet and seasonal trend decomposition for time series forecasting of Italian electricity spot prices
The cost of electricity and gas has a direct influence on the everyday routines of people who
rely on these resources to keep their businesses running. However, the value of electricity is …
rely on these resources to keep their businesses running. However, the value of electricity is …
Multivariable short-term electricity price forecasting using artificial intelligence and multi-input multi-output scheme
P Jiang, Y Nie, J Wang, X Huang - Energy Economics, 2023 - Elsevier
In deregulated power markets, electricity price forecasting is the most valuable tool.
However, with inherent electricity price characteristics, such as high frequency and volatility …
However, with inherent electricity price characteristics, such as high frequency and volatility …
Short-term electricity price forecasting based on similarity day screening, two-layer decomposition technique and Bi-LSTM neural network
K Wang, M Yu, D Niu, Y Liang, S Peng, X Xu - Applied Soft Computing, 2023 - Elsevier
Electricity price forecasting (EPF) has been challenged by the widespread grid integration of
renewable energy (RE), so it is critical to develop a highly accurate and reliable EPF model …
renewable energy (RE), so it is critical to develop a highly accurate and reliable EPF model …
[HTML][HTML] A new framework for electricity price forecasting via multi-head self-attention and CNN-based techniques in the competitive electricity market
Due to recent technical improvements, the smart grid has become a feasible platform for
electricity market participants to successfully regulate their bidding process based on …
electricity market participants to successfully regulate their bidding process based on …
[HTML][HTML] Modeling and forecasting electricity consumption amid the COVID-19 pandemic: Machine learning vs. nonlinear econometric time series models
Accurately modeling and forecasting electricity consumption remains a challenging task due
to the large number of the statistical properties that characterize this time series such as …
to the large number of the statistical properties that characterize this time series such as …
Structure optimization of ensemble learning methods and seasonal decomposition approaches to energy price forecasting in Latin America: A case study about …
The energy price influences the interest in investment, which leads to economic
development. An estimate of the future energy price can support the planning of industrial …
development. An estimate of the future energy price can support the planning of industrial …
[HTML][HTML] Half-hourly electricity price prediction with a hybrid convolution neural network-random vector functional link deep learning approach
Digital technologies with predictive modelling capabilities are revolutionizing electricity
markets, especially in demand-side management. Accurate electricity price prediction is …
markets, especially in demand-side management. Accurate electricity price prediction is …
A whale optimization algorithm-based multivariate exponential smoothing grey-holt model for electricity price forecasting
FE Sapnken, AK Tazehkandgheshlagh… - Expert Systems with …, 2024 - Elsevier
Accurately forecasting electricity prices is essential for a variety of stakeholders in the energy
sector, including market investors, policymakers, and consumers. However, existing …
sector, including market investors, policymakers, and consumers. However, existing …
[HTML][HTML] Electricity market price forecasting using ELM and Bootstrap analysis: A case study of the German and Finnish Day-Ahead markets
S Loizidis, A Kyprianou, GE Georghiou - Applied Energy, 2024 - Elsevier
Electricity market liberalization and the absence of cost-efficient energy storage
technologies have led to the transformation of state-owned electricity companies into …
technologies have led to the transformation of state-owned electricity companies into …