Πλοήγηση ανά Θέμα "Long short-term memory"
Αποτελέσματα 1-18 από 18
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Application of deep learning and chaos theory for load forecasting in Greece
(2021)In this paper, a novel combination of deep learning recurrent neural network and Lyapunov time is proposed to forecast the consumption of electricity load, in Greece, in normal/abrupt change value areas. Our method verifies ... -
Applying Long Short-Term Memory Networks for natural gas demand prediction
(2019)Long Short-Term Memory (LSTM) algorithm encloses the characteristics of the advanced recurrent neural network methods and is used in this research study to forecast the natural gas demand in Greece in the short-term. LSTM ... -
A comparative analysis of Statistical and Computational Intelligence methodologies for the prediction of traffic-induced fine particulate matter and NO2
(2021)With the urbanization increase, urban mobility and transportation induce higher traffic volumes causing environmental, economic and social impacts. This is due to continuous usage of fossil fuel energy resources generating ... -
A Custom State LSTM Cell for Text Classification Tasks
(2022)Text classification is the task of assigning a class to a document. Machine Learning enables the automation of Text Classification Tasks, amongst others. Recent advances in the Machine Learning field, such as the introduction ... -
Deep Bidirectional and Unidirectional LSTM Neural Networks in Traffic Flow Forecasting from Environmental Factors
(2021)The application of deep learning techniques in several forecasting problems has been increased the last years, in many scientific fields. In this research, a deep learning structure is proposed, composed mainly of double ... -
Deep Learning Models for Yoga Pose Monitoring
(2022)Activity recognition is the process of continuously monitoring a person’s activity and movement. Human posture recognition can be utilized to assemble a self-guidance practice framework that permits individuals to accurately ... -
Energy Use Forecasting with the Use of a Nested Structure Based on Fuzzy Cognitive Maps and Artificial Neural Networks
(2022)The aim of this paper is to present a novel approach to energy use forecasting. We propose a nested fuzzy cognitive map in which each concept at a higher level can be decomposed into another fuzzy cognitive map, multilayer ... -
ETH analysis and predictions utilizing deep learning
(2020)This paper attempts to provide a data analysis of cryptocurrency markets. Such markets have been developed rapidly and their volatility poses significant research challenges and justifies intensive behavior analysis. For ... -
Exploring an ensemble of methods that combines fuzzy cognitive maps and neural networks in solving the time series prediction problem of gas consumption in Greece
(2019)This paper introduced a new ensemble learning approach, based on evolutionary fuzzy cognitive maps (FCMs), artificial neural networks (ANNs), and their hybrid structure (FCM-ANN), for time series prediction. The main aim ... -
Exploring ROI size in deep learning based lipreading
(2017)Automatic speechreading systems have increasingly exploited deep learning advances, resulting in dramatic gains over traditional methods. State-of-the-art systems typically employ convolutional neural networks (CNNs), ... -
HINDSIGHT: An R-based framework towards long short term memory (LSTM) optimization
(2018)Hyperparameter optimization is an important but often ignored part of successfully training Neural Networks (NN) since it is time consuming and rather complex. In this paper, we present HINDSIGHT, an open-source framework ... -
An Hour-Ahead Photovoltaic Power Forecasting Based on LSTM Model
(2021)The extensive integration of the large-scale Photovoltaic (PV) plants into the power grid requires the development of new forecasting methods, for the prediction of the PV output with high accuracy. Despite the statistical ... -
A Hybrid Bimodal LSTM Architecture for Cascading Thermal Energy Storage Modelling
(2022)Modelling of thermal energy storage (TES) systems is a complex process that requires the development of sophisticated computational tools for numerical simulation and optimization. Until recently, most modelling approaches ... -
Long Short-Term Memory (LSTM) Deep Neural Networks in Energy Appliances Prediction
(2019)The application of Long Short-Term Memory (LSTM) Deep Neural Networks has been increased the last years. This paper proposes a novel methodology based on a hybrid model using the Long Short-Term Memory (LSTM) Networks and ... -
A meta-modeling power consumption forecasting approach combining client similarity and causality
(2021)Power forecasting models offer valuable insights on the electricity consumption patterns of clients, enabling the development of advanced strategies and applications aimed at energy saving, increased energy efficiency, and ... -
A novel hybrid ensemble LSTM-FFNN forecasting model for very short-term and short-term PV generation forecasting
(2022)The increasing penetration of photovoltaic (PV) systems into the electrical energy systems brings forward several technical and economic issues that mostly relate to their unpredictable nature. A promising solution to many ... -
RNNs for Classification of Driving Behaviour
(2019)Recurrent neural networks are an obvious choice for driving behavior analysis by means of time series of measurements, obtained either from telematics or mobile phone sensors. This work investigates such an application, ... -
Stochastic Heuristic Optimization of Machine Learning Estimators for Short-Term Wind Power Forecasting
(2022)The continuous fluctuation of wind speed, wind direction and other climatic variables affects the power produced by wind turbines. Accurate short-term wind power prediction models are vital for the power industry to evaluate ...