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Model Selection, Adaptation, and Combination for Transfer Learning in Wind and Photovoltaic Power Forecasts
[article]
2022
arXiv
pre-print
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We close these gaps by conducting the first thorough experiment for model selection and adaptation for transfer learning in renewable power forecast, adopting recent results from the field of computer ...
We further show how combining multiple models through ensembles can significantly improve the model selection and adaptation approach. ...
We thank Enercast GmBH for providing the PVREAL and WINDREAL datasets. We also thank Marek Herde, Mohammad Wazed Ali, and David Meier for their valuable input. ...
arXiv:2204.13293v3
fatcat:4am3hwcaungbfn4jtczv3wgaru
Transfer Learning in the Field of Renewable Energies – A Transfer Learning Framework Providing Power Forecasts Throughout the Lifecycle of Wind Farms After Initial Connection to the Electrical Grid
[article]
2019
arXiv
pre-print
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In the research field of vision, e.g., deep neural networks and transfer learning techniques achieve almost perfect classification scores within minutes. ...
Therefore, this article identifies critical challenges and shows potential solutions for power forecasts in the field of renewable energies. ...
The author's of [12] use a combination of nine autoencoders, deep neural networks, and transfer learning to limit the time required for training. ...
arXiv:1906.01168v1
fatcat:ly2cfxlwdfclngqfxn7kbrjikm
A critical review of data-driven transient stability assessment of power systems: principles, prospects and challenges
[article]
2021
arXiv
pre-print
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This paper makes a comprehensive review from the following four aspects: feature extraction and selection, model construction, online learning and rule extraction; and then, summarizes the challenges and ...
This review will be beneficial for relevant researchers to better understand the research status, key technologies and existing challenges in the field. ...
In reference [23] , enhanced feature selection and extraction methods are developed for reducing input features to a probabilistic neural network based TSA model. ...
arXiv:2111.00978v1
fatcat:byrrmsopbfdnxjghsw4vn7p4im
A Critical Review of Data-Driven Transient Stability Assessment of Power Systems: Principles, Prospects and Challenges
2021
Energies
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This paper makes a comprehensive review from the following four aspects: feature extraction and selection, model construction, online learning and rule extraction; and then, summarizes the challenges and ...
This review will be beneficial for relevant researchers to better understand the research status, key technologies, and existing challenges in the field. ...
In [23] , enhanced feature selection and extraction methods are developed for reducing input features to a probabilistic neural network-based TSA model. ...
doi:10.3390/en14217238
fatcat:ioui7lgvlvb6ne33peldzmmdla
Transfer-Based Deep Neural Network for Fault Diagnosis of New Energy Vehicles
2021
Frontiers in Energy Research
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To achieve satisfactory rolling bearing fault diagnosis of the new energy vehicle, a transfer-based deep neural network (DNN-TL) is proposed in this study by combining the benefits of both deep learning ...
(DL) and transfer learning (TL). ...
: low carbon energy applications, deep learning, transfer learning, fault diagnosis, energy vehicle Citation: Wang Y and Li W (2021) Transfer-Based Deep Neural Network for Fault Diagnosis of New Energy ...
doi:10.3389/fenrg.2021.796528
fatcat:mazqo6kwargo3joasj7vf7snt4
Prospective Methodologies in Hybrid Renewable Energy Systems for Energy Prediction Using Artificial Neural Networks
2021
Sustainability
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The uncertain nature of renewable energy resources and the intelligent ability of the neural network approach to process complex time series inputs have inspired the use of ANN methods in renewable energy ...
" (LSTM) models, have been offered in the applications of renewable energy forecasting. ...
Authors are also indebted to the Institute of Sustainable Energy (ISE) of the Universiti Tenaga Nasional (@UNITEN, The Energy University) for laboratory support. ...
doi:10.3390/su13042393
fatcat:l7is6ky4mbfk5pyluisd4g5mki
A Survey of Machine Learning Models in Renewable Energy Predictions
2020
Applied Sciences
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Secondly, this study depicts procedures, including data pre-processing techniques, parameter selection algorithms, and prediction performance measurements, used in machine-learning models for renewable-energy ...
First, this survey attempts to provide a review and analysis of machine-learning models in renewable-energy predictions. ...
Deep learning includes CNN [76] , deep neural network (DNN) [77] [78] [79] , long short-term memory [64, [80] [81] [82] [83] [84] [85] , and the other hybrid models used in multistep predictions of ...
doi:10.3390/app10175975
fatcat:zu7m53eufjh4rjkjwzgxcbn7ze
A critical review of improved deep learning methods for the remaining useful life prediction of lithium-ion batteries
2021
Energy Reports
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A critical review of improved deep learning methods for the remaining useful life prediction of lithium-ion batteries. Energy reports [online], 7, pages 5562-5574. ...
including the deep convolutional neural networks, deep reinforcement learning, long short term memory, and recurrent neural network. ...
An improved DCNN model is built in combination with the integrated and transfer learning of lithium-ion batteries (Shen et al., 2020a) . ...
doi:10.1016/j.egyr.2021.08.182
fatcat:vhkstottljgodcik6r422znn5i
State of the art of machine learning models in energy systems, a systematic review
2020
Zenodo
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In particular, the last two decades has seen a dramatic increase in the development and application of various types of ML models for energy systems. ...
Machine learning (ML) models have been widely used in diverse applications of energy systems such as design, modeling, complex mappings, system identification, performance prediction, and load forecasting ...
In this study, an energy disaggregation utilizing advanced deep learning and Long Short-Term Memory Recurrent Neural Network model was proposed. ...
doi:10.5281/zenodo.4056884
fatcat:yf6gjoevffc2xnzoyjlc3pxbii
Review on Deep Learning Research and Applications in Wind and Wave Energy
2022
Energies
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This paper systematically reviews and summarizes the different models, methods and applications where the deep learning method has been applied in wind and wave energy. ...
Wind energy and wave energy are considered to have enormous potential as renewable energy sources in the energy system to make great contributions in transitioning from fossil fuel to renewable energy. ...
) and U.S. ...
doi:10.3390/en15041510
fatcat:ww4363yzlfao7bqzsxt7j7l3nm
Deep Learning Based Recurrent Neural Networks to Enhance the Performance of Wind Energy Forecasting: A Review
2021
Revue d'intelligence artificielle : Revue des Sciences et Technologies de l'Information
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The present paper focuses on the critical analysis of wind energy forecasting using deep learning based Recurrent neural networks (RNN) models. ...
In the modern era, deep learning is a powerful technique in the field of wind energy forecasting. ...
[36] proposed the deep learning approach for predicting short-term wind speed by utilizing the combination of recurrent neural network and the infinite feature selection (Inf-FS). ...
doi:10.18280/ria.350101
fatcat:kngcr6knszchnhjebmdhnurs2y
Application of Artificial Intelligence for the Optimization of Hydropower Energy Generation
2021
EAI Endorsed Transactions on Industrial Networks and Intelligent Systems
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Hydropower is one of the most promising sources of renewable energy. However, a substantial initial investment requires for the construction of large civil structures. ...
Artificial Intelligence (AI) has become popular, which can be utilized for site selection, parameters assessment, and operation & maintenance optimization. ...
Deep learning is typically conducted using the architecture of a neural network. The term 'deep' refers to the number of layers in the network. ...
doi:10.4108/eai.6-8-2021.170560
fatcat:d7rozg5kbrc7vkfvndiiujqxnm
A Review of the Use of Artificial Neural Network Models for Energy and Reliability Prediction. A Study of the Solar PV, Hydraulic and Wind Energy Sources
2019
Applied Sciences
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To that end, Artificial Neural Network (ANN) models have proven to be a very interesting tool, and there are many relevant and interesting contributions using ANN models, with different purposes, but somehow ...
This document provides a precise review of the literature related to the use of ANN when predicting behaviors in energy production for the referred renewable energy sources. ...
energy, wind energy, wind power, neural network, prediction model, energy forecasting, failure detection, intelligence artificially, machine learning, deep learning), and as a result we have found different ...
doi:10.3390/app9091844
fatcat:i232sjtnibbb5g3zhqtl5sehj4
Wind Power Forecasting Methods Based on Deep Learning: A Survey
2020
CMES - Computer Modeling in Engineering & Sciences
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learning and transfer learning in wind speed and wind power forecasting modeling. ...
As an effective method of high-dimensional feature extraction, deep neural network can theoretically deal with arbitrary nonlinear transformation through proper structural design, such as adding noise ...
on the new testing sample, the deep neural network in combination with the transfer learning framework [Hu, Zhang and Zhou (2016) ] is given to extract a high-level representation of raw data and promote ...
doi:10.32604/cmes.2020.08768
fatcat:kjw6rmpjxfdqlp6l57bvzm4wny
A Review of Low-Voltage Renewable Microgrids: Generation Forecasting and Demand-Side Management Strategies
2021
Electronics
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microgrids and the future with relevant renewable energy source integration. ...
Recent contributions focused on the application of microgrids in Low-Voltage distribution networks are also analyzed and reviewed in detail. ...
recurrent neural network
RMSE, MAE
[80]
2019
Hours
Deep learning
Multi-headed convolutional
neural network
MBE, RMSE,
MAPE, MAE
[86]
2019
Hours
Deep learning
Deep recurrent neural network ...
doi:10.3390/electronics10172093
fatcat:76aojzjxxngwxgvo34x4qc2gmu
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