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 …
Data-driven next-generation smart grid towards sustainable energy evolution: techniques and technology review
Meteorological changes urge engineering communities to look for sustainable and clean
energy technologies to keep the environment safe by reducing CO 2 emissions. The …
energy technologies to keep the environment safe by reducing CO 2 emissions. The …
Long short-term memory network-based metaheuristic for effective electric energy consumption prediction
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 …
Fault diagnosis of angle grinders and electric impact drills using acoustic signals
Electric motors use about 68% of total generated electricity. Fault diagnosis of electrical
motors is an important task, because it allows saving a large amount of money and time. An …
motors is an important task, because it allows saving a large amount of money and time. An …
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 …
A novel extreme learning machine based kNN classification method for dealing with big data
Abstract kNN algorithm, as an effective data mining technique, is always attended for
supervised classification. On the other hand, the previously proposed kNN finding methods …
supervised classification. On the other hand, the previously proposed kNN finding methods …
[HTML][HTML] A new approach based on association rules to add explainability to time series forecasting models
Abstract Machine learning and deep learning have become the most useful and powerful
tools in the last years to mine information from large datasets. Despite the successful …
tools in the last years to mine information from large datasets. Despite the successful …
[HTML][HTML] A new hybrid method for predicting univariate and multivariate time series based on pattern forecasting
Time series forecasting has become indispensable for multiple applications and industrial
processes. Currently, a large number of algorithms have been developed to forecast time …
processes. Currently, a large number of algorithms have been developed to forecast time …
HSIC bottleneck based distributed deep learning model for load forecasting in smart grid with a comprehensive survey
Load forecasting is a vital part of smart grids for predicting the required electrical power
using artificial intelligence (AI). Deep learning is broadly used for load forecasting in the …
using artificial intelligence (AI). Deep learning is broadly used for load forecasting in the …
A comparative study of time series forecasting methods for short term electric energy consumption prediction in smart buildings
Smart buildings are equipped with sensors that allow monitoring a range of building systems
including heating and air conditioning, lighting and the general electric energy consumption …
including heating and air conditioning, lighting and the general electric energy consumption …