Machine-learning methods for integrated renewable power generation: A comparative study of artificial neural networks, support vector regression, and Gaussian …
M Sharifzadeh, A Sikinioti-Lock, N Shah - Renewable and Sustainable …, 2019 - Elsevier
Renewable energy from wind and solar resources can contribute significantly to the
decarbonisation of the conventionally fossil-driven electricity grid. However, their seamless …
decarbonisation of the conventionally fossil-driven electricity grid. However, their seamless …
A review of machine learning methods applied to structural dynamics and vibroacoustic
Abstract The use of Machine Learning (ML) has rapidly spread across several fields of
applied sciences, having encountered many applications in Structural Dynamics and …
applied sciences, having encountered many applications in Structural Dynamics and …
A dual-stage attention-based recurrent neural network for time series prediction
The Nonlinear autoregressive exogenous (NARX) model, which predicts the current value of
a time series based upon its previous values as well as the current and past values of …
a time series based upon its previous values as well as the current and past values of …
Evaluation of statistical and machine learning models for time series prediction: Identifying the state-of-the-art and the best conditions for the use of each model
The choice of the most promising algorithm to model and predict a particular phenomenon is
one of the most prominent activities of the temporal data forecasting. Forecasting (or …
one of the most prominent activities of the temporal data forecasting. Forecasting (or …
Detection of false data injection cyber-attacks in DC microgrids based on recurrent neural networks
Cyber-physical systems (CPSs) are vulnerable to cyber-attacks. Nowadays, the detection of
cyber-attacks in microgrids as examples of CPS has become an important topic due to their …
cyber-attacks in microgrids as examples of CPS has become an important topic due to their …
National-scale electricity peak load forecasting: Traditional, machine learning, or hybrid model?
As the volatility of electricity demand increases owing to climate change and electrification,
the importance of accurate peak load forecasting is increasing. Traditional peak load …
the importance of accurate peak load forecasting is increasing. Traditional peak load …
Performance prediction, optimal design and operational control of thermal energy storage using artificial intelligence methods
Z He, W Guo, P Zhang - Renewable and Sustainable Energy Reviews, 2022 - Elsevier
Capable of storing and redistributing energy, thermal energy storage (TES) shows a
promising applicability in energy systems. Recently, artificial intelligence (AI) technique is …
promising applicability in energy systems. Recently, artificial intelligence (AI) technique is …
Echo state networks are universal
L Grigoryeva, JP Ortega - Neural Networks, 2018 - Elsevier
This paper shows that echo state networks are universal uniform approximants in the context
of discrete-time fading memory filters with uniformly bounded inputs defined on negative …
of discrete-time fading memory filters with uniformly bounded inputs defined on negative …
Conceptualizing digital twins
Properly arranging models, data sources, and their relations to engineer digital twins is
challenging. We propose a conceptual modeling framework for digital twins that captures the …
challenging. We propose a conceptual modeling framework for digital twins that captures the …
Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications
Artificial Neural Networks (ANNs) are being used increasingly to predict and forecast water
resources variables. In this paper, the steps that should be followed in the development of …
resources variables. In this paper, the steps that should be followed in the development of …