Deep learning for time series forecasting: a survey
Time series forecasting has become a very intensive field of research, which is even
increasing in recent years. Deep neural networks have proved to be powerful and are …
increasing in recent years. Deep neural networks have proved to be powerful and are …
A survey of machine learning models in renewable energy predictions
The use of renewable energy to reduce the effects of climate change and global warming
has become an increasing trend. In order to improve the prediction ability of renewable …
has become an increasing trend. In order to improve the prediction ability of renewable …
Long short-term memory network-based metaheuristic for effective electric energy consumption prediction
SK Hora, R Poongodan, RP De Prado, M Wozniak… - Applied Sciences, 2021 - mdpi.com
The Electric Energy Consumption Prediction (EECP) is a complex and important process in
an intelligent energy management system and its importance has been increasing rapidly …
an intelligent energy management system and its importance has been increasing rapidly …
A Review on Sustainable Energy Sources Using Machine Learning and Deep Learning Models
A Bhansali, N Narasimhulu, R Pérez de Prado… - Energies, 2023 - mdpi.com
Today, methodologies based on learning models are utilized to generate precise conversion
techniques for renewable sources. The methods based on Computational Intelligence (CI) …
techniques for renewable sources. The methods based on Computational Intelligence (CI) …
Data science in economics: comprehensive review of advanced machine learning and deep learning methods
This paper provides a comprehensive state-of-the-art investigation of the recent advances in
data science in emerging economic applications. The analysis is performed on the novel …
data science in emerging economic applications. The analysis is performed on the novel …
[HTML][HTML] Trends and gaps in photovoltaic power forecasting with machine learning
The share of solar energy in the electricity mix increases year after year. Knowing the
production of photovoltaic (PV) power at each instant of time is crucial for its integration into …
production of photovoltaic (PV) power at each instant of time is crucial for its integration into …
Stacked LSTM sequence-to-sequence autoencoder with feature selection for daily solar radiation prediction: A review and new modeling results
We review the latest modeling techniques and propose new hybrid SAELSTM framework
based on Deep Learning (DL) to construct prediction intervals for daily Global Solar …
based on Deep Learning (DL) to construct prediction intervals for daily Global Solar …
Investigating photovoltaic solar power output forecasting using machine learning algorithms
Solar power integration in electrical grids is complicated due to dependence on volatile
weather conditions. To address this issue, continuous research and development is required …
weather conditions. To address this issue, continuous research and development is required …
Solar irradiance forecasting based on direct explainable neural network
As the penetration of solar energy into electrical power and energy system expands in
recent years over the world, accurate solar irradiance forecasting is becoming highly …
recent years over the world, accurate solar irradiance forecasting is becoming highly …
A comprehensive review and analysis of solar forecasting techniques
In the last two decades, renewable energy has been paid immeasurable attention to toward
the attainment of electricity requirements for domestic, industrial, and agriculture sectors …
the attainment of electricity requirements for domestic, industrial, and agriculture sectors …