A review of wind speed and wind power forecasting with deep neural networks
The use of wind power, a pollution-free and renewable form of energy, to generate electricity
has attracted increasing attention. However, intermittent electricity generation resulting from …
has attracted increasing attention. However, intermittent electricity generation resulting from …
[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 …
Energy forecasting: A review and outlook
Forecasting has been an essential part of the power and energy industry. Researchers and
practitioners have contributed thousands of papers on forecasting electricity demand and …
practitioners have contributed thousands of papers on forecasting electricity demand and …
Short-term load forecasting for industrial customers based on TCN-LightGBM
Y Wang, J Chen, X Chen, X Zeng… - … on Power Systems, 2020 - ieeexplore.ieee.org
Accurate and rapid load forecasting for industrial customers has been playing a crucial role
in modern power systems. Due to the variability of industrial customers' activities, individual …
in modern power systems. Due to the variability of industrial customers' activities, individual …
Taxonomy research of artificial intelligence for deterministic solar power forecasting
With the world-wide deployment of solar energy for a sustainable and renewable future, the
stochastic and volatile nature of solar power pose significant challenges to the reliable …
stochastic and volatile nature of solar power pose significant challenges to the reliable …
Load forecasting techniques for power system: Research challenges and survey
The main and pivot part of electric companies is the load forecasting. Decision-makers and
think tank of power sectors should forecast the future need of electricity with large accuracy …
think tank of power sectors should forecast the future need of electricity with large accuracy …
Intelligent multi-microgrid energy management based on deep neural network and model-free reinforcement learning
In this paper, an intelligent multi-microgrid (MMG) energy management method is proposed
based on deep neural network (DNN) and model-free reinforcement learning (RL) …
based on deep neural network (DNN) and model-free reinforcement learning (RL) …
Deep learning in smart grid technology: A review of recent advancements and future prospects
The current electric power system witnesses a significant transition into Smart Grids (SG) as
a promising landscape for high grid reliability and efficient energy management. This …
a promising landscape for high grid reliability and efficient energy management. This …
A review of deep learning methods applied on load forecasting
A Almalaq, G Edwards - 2017 16th IEEE international …, 2017 - ieeexplore.ieee.org
The utility industry has invested widely in smart grid (SG) over the past decade. They
considered it the future electrical grid while the information and electricity are delivered in …
considered it the future electrical grid while the information and electricity are delivered in …
Deep learning methods and applications for electrical power systems: A comprehensive review
AK Ozcanli, F Yaprakdal… - International Journal of …, 2020 - Wiley Online Library
Over the past decades, electric power systems (EPSs) have undergone an evolution from an
ordinary bulk structure to intelligent flexible systems by way of advanced electronics and …
ordinary bulk structure to intelligent flexible systems by way of advanced electronics and …