[HTML][HTML] Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark
While the field of electricity price forecasting has benefited from plenty of contributions in the
last two decades, it arguably lacks a rigorous approach to evaluating new predictive …
last two decades, it arguably lacks a rigorous approach to evaluating new predictive …
[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 deep learning based hybrid framework for day-ahead electricity price forecasting
R Zhang, G Li, Z Ma - IEEE Access, 2020 - ieeexplore.ieee.org
With the deregulation of the electric energy industry, accurate electricity price forecasting
(EPF) is increasingly significant to market participants' bidding strategies and uncertainty risk …
(EPF) is increasingly significant to market participants' bidding strategies and uncertainty risk …
Multi-horizon electricity load and price forecasting using an interpretable multi-head self-attention and EEMD-based framework
MF Azam, MS Younis - IEEE Access, 2021 - ieeexplore.ieee.org
Accurate system marginal price and load forecasts play a pivotal role in economic power
dispatch, system reliability and planning. Price forecasting helps electricity buyers and …
dispatch, system reliability and planning. Price forecasting helps electricity buyers and …
A deep learning assisted adaptive nonlinear deloading strategy for wind turbine generator integrated with an interconnected power system for enhanced load …
The existing linear and quadratic deloading strategies with constant deloading factor, fail to
effectively handle the nonlinear characteristics of WTGs. This work proposes a novel deep …
effectively handle the nonlinear characteristics of WTGs. This work proposes a novel deep …
GHTnet: Tri-Branch deep learning network for real-time electricity price forecasting
H Yang, KR Schell - Energy, 2022 - Elsevier
A highly accurate electricity price prediction model is of the utmost importance for multiple
power systems tasks, such as generation dispatch and bidding. Due to the liberalization of …
power systems tasks, such as generation dispatch and bidding. Due to the liberalization of …
Exploring time-series forecasting models for dynamic pricing in digital signage advertising
Audience attention is vital in Digital Signage Advertising (DSA), as it has a significant impact
on the pricing decision to advertise on those media. Various environmental factors affect the …
on the pricing decision to advertise on those media. Various environmental factors affect the …
[HTML][HTML] Short-and long-term forecasting of electricity prices using embedding of calendar information in neural networks
A Wagner, E Ramentol, F Schirra, H Michaeli - Journal of Commodity …, 2022 - Elsevier
Electricity prices strongly depend on seasonality of different time scales, therefore any
forecasting of electricity prices has to account for it. Neural networks have proven successful …
forecasting of electricity prices has to account for it. Neural networks have proven successful …
PVHybNet: A hybrid framework for predicting photovoltaic power generation using both weather forecast and observation data
Photovoltaics has gained popularity as a renewable energy source in recent decades. The
main challenge for this energy source is the instability in the amount of generated energy …
main challenge for this energy source is the instability in the amount of generated energy …
Locational marginal price forecasting using convolutional long-short term memory-based generative adversarial network
In wholesale electricity markets, locational marginal prices (LMPs) are strongly spatio-
temporal correlated. Most previous data-driven studies on LMP forecasting only leveraged …
temporal correlated. Most previous data-driven studies on LMP forecasting only leveraged …