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 …
An experimental review on deep learning architectures for time series forecasting
P Lara-Benítez, M Carranza-García… - International journal of …, 2021 - World Scientific
In recent years, deep learning techniques have outperformed traditional models in many
machine learning tasks. Deep neural networks have successfully been applied to address …
machine learning tasks. Deep neural networks have successfully been applied to address …
A review on time series forecasting techniques for building energy consumption
Energy consumption forecasting for buildings has immense value in energy efficiency and
sustainability research. Accurate energy forecasting models have numerous implications in …
sustainability research. Accurate energy forecasting models have numerous implications in …
Empirical mode decomposition based ensemble deep learning for load demand time series forecasting
X Qiu, Y Ren, PN Suganthan, GAJ Amaratunga - Applied soft computing, 2017 - Elsevier
Load demand forecasting is a critical process in the planning of electric utilities. An
ensemble method composed of Empirical Mode Decomposition (EMD) algorithm and deep …
ensemble method composed of Empirical Mode Decomposition (EMD) algorithm and deep …
[HTML][HTML] Electricity consumption forecasting based on ensemble deep learning with application to the Algerian market
The economic sector is one of the most important pillars of countries. Economic activities of
industry are intimately linked with the ability to meet their needs for electricity. Therefore …
industry are intimately linked with the ability to meet their needs for electricity. Therefore …
A deep LSTM network for the Spanish electricity consumption forecasting
Nowadays, electricity is a basic commodity necessary for the well-being of any modern
society. Due to the growth in electricity consumption in recent years, mainly in large cities …
society. Due to the growth in electricity consumption in recent years, mainly in large cities …
Forecasting energy consumption time series using machine learning techniques based on usage patterns of residential householders
Energy consumption in buildings is increasing because of social development and
urbanization. Forecasting the energy consumption in buildings is essential for improving …
urbanization. Forecasting the energy consumption in buildings is essential for improving …
Multi-step forecasting for big data time series based on ensemble learning
A Galicia, R Talavera-Llames, A Troncoso… - Knowledge-Based …, 2019 - Elsevier
This paper presents ensemble models for forecasting big data time series. An ensemble
composed of three methods (decision tree, gradient boosted trees and random forest) is …
composed of three methods (decision tree, gradient boosted trees and random forest) is …
[HTML][HTML] Modelling community-scale renewable energy and electric vehicle management for cold-climate regions using machine learning
With increasing environmental problems of fossil fuel-based devices and systems in
societies, diffusion and adoption of sustainability solutions such as renewable energy …
societies, diffusion and adoption of sustainability solutions such as renewable energy …
Building energy consumption prediction: An extreme deep learning approach
Building energy consumption prediction plays an important role in improving the energy
utilization rate through helping building managers to make better decisions. However, as a …
utilization rate through helping building managers to make better decisions. However, as a …