Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSx
We extend neural basis expansion analysis (NBEATS) to incorporate exogenous factors.
The resulting method, called NBEATSx, improves on a well-performing deep learning …
The resulting method, called NBEATSx, improves on a well-performing deep learning …
A review of deep learning techniques for forecasting energy use in buildings
J Runge, R Zmeureanu - Energies, 2021 - mdpi.com
Buildings account for a significant portion of our overall energy usage and associated
greenhouse gas emissions. With the increasing concerns regarding climate change, there …
greenhouse gas emissions. With the increasing concerns regarding climate change, there …
Distributional neural networks for electricity price forecasting
We present a novel approach to probabilistic electricity price forecasting which utilizes
distributional neural networks. The model structure is based on a deep neural network …
distributional neural networks. The model structure is based on a deep neural network …
Regularized quantile regression averaging for probabilistic electricity price forecasting
B Uniejewski, R Weron - Energy Economics, 2021 - Elsevier
Abstract Quantile Regression Averaging (QRA) has sparked interest in the electricity price
forecasting community after its unprecedented success in the Global Energy Forecasting …
forecasting community after its unprecedented success in the Global Energy Forecasting …
[HTML][HTML] Integrated day-ahead and intraday self-schedule bidding for energy storage systems using approximate dynamic programming
Most modern energy markets trade electricity in advance for technical reasons. Thus, market
participants must commit to delivering or consuming a certain amount of energy before the …
participants must commit to delivering or consuming a certain amount of energy before the …
Short-term electricity price forecasting by employing ensemble empirical mode decomposition and extreme learning machine
Day-ahead electricity price forecasting plays a critical role in balancing energy consumption
and generation, optimizing the decisions of electricity market participants, formulating …
and generation, optimizing the decisions of electricity market participants, formulating …
Error compensation enhanced day-ahead electricity price forecasting
D Kontogiannis, D Bargiotas, A Daskalopulu… - Energies, 2022 - mdpi.com
The evolution of electricity markets has led to increasingly complex energy trading dynamics
and the integration of renewable energy sources as well as the influence of several external …
and the integration of renewable energy sources as well as the influence of several external …
Forecasting electricity prices
Forecasting electricity prices is a challenging task and an active area of research since the
1990s and the deregulation of the traditionally monopolistic and government-controlled …
1990s and the deregulation of the traditionally monopolistic and government-controlled …
Forecasting imbalance price densities with statistical methods and neural networks
Despite the extensive research on electricity price forecasting, forecasting imbalance prices
is a relatively new topic. Interest, however, is growing because of the greater uncertainties …
is a relatively new topic. Interest, however, is growing because of the greater uncertainties …
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 …