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Model Selection, Adaptation, and Combination for Transfer Learning in Wind and Photovoltaic Power Forecasts [article]

Jens Schreiber, Bernhard Sick
2022 arXiv   pre-print
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]

Jens Schreiber
2019 arXiv   pre-print
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]

Shitu Zhang, Zhixun Zhu, Yang Li
2021 arXiv   pre-print
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

Shitu Zhang, Zhixun Zhu, Yang Li
2021 Energies  
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

Yuping Wang, Weidong Li
2021 Frontiers in Energy Research  
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

Md Mijanur Rahman, Mohammad Shakeri, Sieh Kiong Tiong, Fatema Khatun, Nowshad Amin, Jagadeesh Pasupuleti, Mohammad Kamrul Hasan
2021 Sustainability  
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

Jung-Pin Lai, Yu-Ming Chang, Chieh-Huang Chen, Ping-Feng Pai
2020 Applied Sciences  
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

Shunli Wang, Siyu Jin, Dekui Bai, Yongcun Fan, Haotian Shi, Carlos Fernandez
2021 Energy Reports  
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

Mohsen Salimi, Amir Mosavi, Sina Faizollahzadeh Ardabili, Majid Amidpour, Timon Rabczuk, Shahabodin Shamshirband
2020 Zenodo  
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

Chengcheng Gu, Hua Li
2022 Energies  
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

Senthil Kumar Paramasivan
2021 Revue d'intelligence artificielle : Revue des Sciences et Technologies de l'Information  
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

Krishna Kumar, R. Saini
2021 EAI Endorsed Transactions on Industrial Networks and Intelligent Systems  
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

Jesús Ferrero Bermejo, Juan F. Gómez Fernández, Fernando Olivencia Polo, Adolfo Crespo Márquez
2019 Applied Sciences  
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

Xing Deng, Haijian Shao, Chunlong Hu, Dengbiao Jiang, Yingtao Jiang
2020 CMES - Computer Modeling in Engineering & Sciences  
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

Miguel Aybar-Mejía, Junior Villanueva, Deyslen Mariano-Hernández, Félix Santos, Angel Molina-Garcia
2021 Electronics  
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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