Neural networks: An overview of early research, current frameworks and new challenges
This paper presents a comprehensive overview of modelling, simulation and implementation
of neural networks, taking into account that two aims have emerged in this area: the …
of neural networks, taking into account that two aims have emerged in this area: the …
Time-series data mining
P Esling, C Agon - ACM Computing Surveys (CSUR), 2012 - dl.acm.org
In almost every scientific field, measurements are performed over time. These observations
lead to a collection of organized data called time series. The purpose of time-series data …
lead to a collection of organized data called time series. The purpose of time-series data …
Time series prediction using artificial wavelet neural network and multi-resolution analysis: Application to wind speed data
B Doucoure, K Agbossou, A Cardenas - Renewable Energy, 2016 - Elsevier
The aim of this work is to develop a prediction method for renewable energy sources in
order to achieve an intelligent management of a microgrid system and to promote the …
order to achieve an intelligent management of a microgrid system and to promote the …
Analysis and application of forecasting models in wind power integration: A review of multi-step-ahead wind speed forecasting models
Wind energy, which is clean, inexhaustible and free, has been used to mitigate the crisis of
conventional resource depletion. However, wind power is difficult to implement on a large …
conventional resource depletion. However, wind power is difficult to implement on a large …
Proton exchange membrane fuel cell degradation prediction based on adaptive neuro-fuzzy inference systems
This paper studies the prediction of the output voltage reduction caused by degradation
during nominal operating condition of a PEM fuel cell stack. It proposes a methodology …
during nominal operating condition of a PEM fuel cell stack. It proposes a methodology …
A method to improve the stability and accuracy of ANN-and SVM-based time series models for long-term groundwater level predictions
H Yoon, Y Hyun, K Ha, KK Lee, GB Kim - Computers & geosciences, 2016 - Elsevier
The prediction of long-term groundwater level fluctuations is necessary to effectively
manage groundwater resources and to assess the effects of changes in rainfall patterns on …
manage groundwater resources and to assess the effects of changes in rainfall patterns on …
Multi-step-ahead wind speed forecasting based on optimal feature selection and a modified bat algorithm with the cognition strategy
T Niu, J Wang, K Zhang, P Du - Renewable Energy, 2018 - Elsevier
With the arrival of big data, data mining analysis and high-performance forecasting of wind
speed is increasingly attracting close attention. Despite the fact that massive investigations …
speed is increasingly attracting close attention. Despite the fact that massive investigations …
An ensemble multi-step M-RMLSSVR model based on VMD and two-group strategy for day-ahead short-term load forecasting
F Yuan, J Che - Knowledge-Based Systems, 2022 - Elsevier
Accurate prediction of the power load is one of the keys to guarantee stable operation of
power construction. However, with the surge of power load, the uncertainty of multi-step …
power construction. However, with the surge of power load, the uncertainty of multi-step …
Novel optimization approach for realized volatility forecast of stock price index based on deep reinforcement learning model
Y Yu, Y Lin, X Hou, X Zhang - Expert Systems with Applications, 2023 - Elsevier
Accurately predicting volatility has always been the focus of government decision-making
departments, financial regulators and academia. Therefore, it is very crucial to precisely …
departments, financial regulators and academia. Therefore, it is very crucial to precisely …
Recursive wind speed forecasting based on Hammerstein Auto-Regressive model
Abstract A new Wind Speed Forecasting (WSF) model, suitable for a short term 1–24 h
forecast horizon, is developed by adapting Hammerstein model to an Autoregressive …
forecast horizon, is developed by adapting Hammerstein model to an Autoregressive …