Special Issue "Artificial Intelligence for Renewable Energy Systems"

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Energy Sustainability".

Deadline for manuscript submissions: 31 December 2019.

Special Issue Editor

Dr. Ekaitz Zulueta
E-Mail Website
Guest Editor
Department of System Engineering and Automatic Control– Engineering College of Vitoria-Gasteiz, University of the Basque Country, Nieves Cano, 12, 01006, Vitoria-Gasteiz, Spain
Interests: Wind Energy; Photovoltaic Energy Control; Energy Harvesters design and Control; Computational Intelligence

Special Issue Information

Dear Colleagues,

This Special Issue focuses on Artificial Intelligence applied to Renewable Energy Systems. The influence of Artificial Intelligence (AI) is rapidly increasing in all Engineering areas, but in particular in Renewable Energy Systems. The main goal of this Special Issue is to show the most relevant advances in AI application on this domain. Nowadays, several interesting intelligent techniques have been developed for Renewable Energy Systems. There are many applications, such as Wind Turbine Control or Photovoltaic Panel and Power Electronics Control, that recent years have achieved a great improvement. Smart grid control and management are also very relevant fields for AI applications. Additionally, Hybrid renewable energy plants (such as wind/PV plants) with battery energy storage systems for providing ancillary services to the electricity grid are also of high interest, since control algorithms are important in order to optimize energy management services and offer different grid control applications. Another important research subject is the time series forecast in renewable energy systems. This is due to their stochastics behavior in many aspects as energy resources, energy consumption, system availability etc. Furthermore, intelligent design and control of Energy Harvester application are also of great interest. The Energy storage control is a key research topic to which the current special issue is devoted. Hydrogen based energy system is another relevant research topic due to its increasing importance as energy vector in Automotive: for example, an outstanding application is the Energy Management Strategy in order to reduce the hydrogen consumption.

Dr. Ekaitz Zulueta
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Intelligent techniques applied to Wind Energy
  • Photovoltaic Energy Control
  • Hydrogen related technologies
  • Energy Storage
  • Smart Grid
  • Power Network Control
  • Energy Harvester Design and Control
  • Artificial Intelligence based Design
  • Artificial Neural Networks applied to Energy systems
  • Hybrid renewable energy Plants
  • Battery Energy Storage Systems
  • Forecast in Renewable Energy Systems

Published Papers (4 papers)

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Research

Open AccessArticle
An Innovative Home Energy Management Model with Coordination among Appliances using Game Theory
Sustainability 2019, 11(22), 6287; https://doi.org/10.3390/su11226287 - 08 Nov 2019
Abstract
The feature of bidirectional communication in a smart grid involves the interaction between consumer and utility for optimizing the energy consumption of the users. For optimal management of the energy at the end user, several demand side management techniques are implemented. This work [...] Read more.
The feature of bidirectional communication in a smart grid involves the interaction between consumer and utility for optimizing the energy consumption of the users. For optimal management of the energy at the end user, several demand side management techniques are implemented. This work proposes a home energy management system, where consumption of household appliances is optimized using a hybrid technique. This technique is developed from cuckoo search algorithm and earthworm algorithm. However, there is a problem in such home energy management systems, that is, an uncertain behavior of the user that can lead to force start or stop of an appliance, deteriorating the purpose of scheduling of appliances. In order to solve this issue, coordination among appliances for rescheduling is incorporated in home energy management system using game theory. The appliances of the home are categorized in three different groups and their electricity cost is computed through the real-time pricing signals. Optimization schemes are implemented and their performance is scrutinized with and without coordination among the appliances. Simulation outcomes display that our proposed technique has minimized the total electricity cost by 50.6% as compared to unscheduled cost. Moreover, coordination among appliances has helped in increasing the user comfort by reducing the waiting time of appliances. The Shapley value has outperformed the Nash equilibrium and zero sum by achieving the maximum reduction in waiting time of appliances. Full article
(This article belongs to the Special Issue Artificial Intelligence for Renewable Energy Systems)
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Open AccessArticle
The Prediction Model of Characteristics for Wind Turbines Based on Meteorological Properties Using Neural Network Swarm Intelligence
Sustainability 2019, 11(17), 4803; https://doi.org/10.3390/su11174803 - 03 Sep 2019
Abstract
In order to produce more efficient, sustainable-clean energy, accurate prediction of wind turbine design parameters provide to work the system efficiency at the maximum level. For this purpose, this paper appears with the aim of obtaining the optimum prediction of the turbine parameter [...] Read more.
In order to produce more efficient, sustainable-clean energy, accurate prediction of wind turbine design parameters provide to work the system efficiency at the maximum level. For this purpose, this paper appears with the aim of obtaining the optimum prediction of the turbine parameter efficiently. Firstly, the motivation to achieve an accurate wind turbine design is presented with the analysis of three different models based on artificial neural networks comparatively given for maximum energy production. It is followed by the implementation of wind turbine model and hybrid models developed by using both neural network and optimization models. In this study, the ANN-FA hybrid structure model is firstly used and also ANN coefficients are trained by FA to give a new approach in literature for wind turbine parameters’ estimation. The main contribution of this paper is that seven important wind turbine parameters are predicted. Aiming to fill the mentioned research gap, this paper outlines combined forecasting turbine design approaches and presents wind turbine performance in detail. Furthermore, the present study also points out the possible further research directions of combined techniques so as to help researchers in the field develop more effective wind turbine design according to geographical conditions. Full article
(This article belongs to the Special Issue Artificial Intelligence for Renewable Energy Systems)
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Open AccessArticle
A Novel on Transmission Line Tower Big Data Analysis Model Using Altered K-means and ADQL
Sustainability 2019, 11(13), 3499; https://doi.org/10.3390/su11133499 - 26 Jun 2019
Abstract
This study sought to propose a big data analysis and prediction model for transmission line tower outliers to assess when something is wrong with transmission line tower big data based on deep reinforcement learning. The model enables choosing automatic cluster K values based [...] Read more.
This study sought to propose a big data analysis and prediction model for transmission line tower outliers to assess when something is wrong with transmission line tower big data based on deep reinforcement learning. The model enables choosing automatic cluster K values based on non-labeled sensor big data. It also allows measuring the distance of action between data inside a cluster with the Q-value representing network output in the altered transmission line tower big data clustering algorithm containing transmission line tower outliers and old Deep Q Network. Specifically, this study performed principal component analysis to categorize transmission line tower data and proposed an automatic initial central point approach through standard normal distribution. It also proposed the A-Deep Q-Learning algorithm altered from the deep Q-Learning algorithm to explore policies based on the experiences of clustered data learning. It can be used to perform transmission line tower outlier data learning based on the distance of data within a cluster. The performance evaluation results show that the proposed model recorded an approximately 2.29%~4.19% higher prediction rate and around 0.8% ~ 4.3% higher accuracy rate compared to the old transmission line tower big data analysis model. Full article
(This article belongs to the Special Issue Artificial Intelligence for Renewable Energy Systems)
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Open AccessArticle
A Method for Rockburst Prediction in the Deep Tunnels of Hydropower Stations Based on the Monitored Microseismicity and an Optimized Probabilistic Neural Network Model
Sustainability 2019, 11(11), 3212; https://doi.org/10.3390/su11113212 - 10 Jun 2019
Cited by 1
Abstract
Hydropower is one of the most important renewable energy sources. However, the safe construction of hydropower stations is seriously affected by disasters like rockburst, which, in turn, restricts the sustainable development of hydropower energy. In this paper, a method for rockburst prediction in [...] Read more.
Hydropower is one of the most important renewable energy sources. However, the safe construction of hydropower stations is seriously affected by disasters like rockburst, which, in turn, restricts the sustainable development of hydropower energy. In this paper, a method for rockburst prediction in the deep tunnels of hydropower stations based on the use of real-time microseismic (MS) monitoring information and an optimized probabilistic neural network (PNN) model is proposed. The model consists of the mean impact value algorithm (MIVA), the modified firefly algorithm (MFA), and PNN (MIVA-MFA-PNN model). The MIVA is used to reduce the interference from redundant information in the multiple MS parameters in the input layer of the PNN. The MFA is used to optimize the parameter smoothing factor in the PNN and reduce the error caused by artificial determination. Three improvements are made in the MFA compared to the standard firefly algorithm. The proposed rockburst prediction method is tested by 93 rockburst cases with different intensities that occurred in parts of the deep diversion and drainage tunnels of the Jinping II hydropower station, China (with a maximum depth of 2525 m). The results show that the rates of correct rockburst prediction of the test samples and learning samples are 100% and 86.75%, respectively. However, when a common PNN model combined with monitored microseismicity is used, the related rates are only 80.0% and 61.45%, respectively. The proposed method can provide a reference for rockburst prediction in MS monitored deep tunnels of hydropower projects. Full article
(This article belongs to the Special Issue Artificial Intelligence for Renewable Energy Systems)
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