Short-term electricity load and price forecasting by a new optimal LSTM-NN based prediction algorithm
Nowadays, a basic commodity for a human being to lead a standard lifestyle with human
comfort irrespective of the nature of environmental conditions is electric power. The …
comfort irrespective of the nature of environmental conditions is electric power. The …
Electric load forecasting based on deep learning and optimized by heuristic algorithm in smart grid
Accurate electric load forecasting is important due to its application in the decision making
and operation of the power grid. However, the electric load profile is a complex signal due to …
and operation of the power grid. However, the electric load profile is a complex signal due to …
[HTML][HTML] Forecasting spot electricity prices: Deep learning approaches and empirical comparison of traditional algorithms
In this paper, a novel modeling framework for forecasting electricity prices is proposed.
While many predictive models have been already proposed to perform this task, the area of …
While many predictive models have been already proposed to perform this task, the area of …
A new short-term wind speed forecasting method based on fine-tuned LSTM neural network and optimal input sets
In recent years, clean energies, such as wind power have been developed rapidly.
Especially, wind power generation becomes a significant source of energy in some power …
Especially, wind power generation becomes a significant source of energy in some power …
Short-term electricity price and load forecasting in isolated power grids based on composite neural network and gravitational search optimization algorithm
Electricity price forecasting is a key aspect for market participants to maximize their
economic efficiency in deregulated markets. Nevertheless, due to its non-linearity and non …
economic efficiency in deregulated markets. Nevertheless, due to its non-linearity and non …
A proposed intelligent short-term load forecasting hybrid models of ANN, WNN and KF based on clustering techniques for smart grid
HHH Aly - Electric Power Systems Research, 2020 - Elsevier
Smart grid is one of the most important topics to be covered with the increasing penetration
of renewable energy in the power system grid to improve grid energy efficiency by managing …
of renewable energy in the power system grid to improve grid energy efficiency by managing …
Day-ahead electricity price forecasting via the application of artificial neural network based models
Traditionally, short-term electricity price forecasting has been essential for utilities and
generation companies. However, the deregulation of electricity markets created a …
generation companies. However, the deregulation of electricity markets created a …
A new feature selection technique for load and price forecast of electrical power systems
Load and price forecasts are necessary for optimal operation planning in competitive
electricity markets. However, most of the load and price forecast methods suffer from lack of …
electricity markets. However, most of the load and price forecast methods suffer from lack of …
Day-ahead electricity price forecasting with high-dimensional structures: Univariate vs. multivariate modeling frameworks
We conduct an extensive empirical study on short-term electricity price forecasting (EPF) to
address the long-standing question if the optimal model structure for EPF is univariate or …
address the long-standing question if the optimal model structure for EPF is univariate or …
[HTML][HTML] Forecasting day-ahead electricity prices in Europe: The importance of considering market integration
Motivated by the increasing integration among electricity markets, in this paper we propose
two different methods to incorporate market integration in electricity price forecasting and to …
two different methods to incorporate market integration in electricity price forecasting and to …