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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
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 …
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 …
Empirical mode decomposition based ensemble deep learning for load demand time series forecasting
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 …
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 …
A survey on hyperparameters optimization algorithms of forecasting models in smart grid
Forecasting in the smart grid (SG) plays a vital role in maintaining the balance between
demand and supply of electricity, efficient energy management, better planning of energy …
demand and supply of electricity, efficient energy management, better planning of energy …
Multi-step forecasting for big data time series based on ensemble learning
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 …
Stacking ensemble learning for short-term electricity consumption forecasting
The ability to predict short-term electric energy demand would provide several benefits, both
at the economic and environmental level. For example, it would allow for an efficient use of …
at the economic and environmental level. For example, it would allow for an efficient use of …
Convolutional neural networks for energy time series forecasting
I Koprinska, D Wu, Z Wang - 2018 international joint conference …, 2018 - ieeexplore.ieee.org
We investigate the application of convolutional neural networks for energy time series
forecasting. In particular, we consider predicting the photovoltaic solar power and electricity …
forecasting. In particular, we consider predicting the photovoltaic solar power and electricity …