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Special Issue "Artificial Intelligence in Data Science For Energy Management in Sustainability"

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "Energy and Environment".

Deadline for manuscript submissions: closed (31 March 2019).

Special Issue Editor

Guest Editor
Prof. Dr. Karina Gibert

Knowledge Engineering and Machine Learning group at Intelligent Data Science and Artificial Intelligence Research Center, Department of Statistics and Operations Research, Research Institute of Science and Technology for Sustainability, Universitat Politècnica de Catalunya-BarcelonaTech (UPC), Barcelona, Catalonia, Spain
Website | E-Mail
Interests: data mining; data science; knowledge discovery from databases; multivariate statistics; clustering; artificial intelligence; machine learning; hybrid ai and statistics data mining methods; intelligent decision support systems; preprocessing; postprocessing; heterogeneous data analysis; health applications; environmental applications

Special Issue Information

Dear Colleagues,

This Special Issue focuses on the use of Artificial Intelligence and Data Science methods for a better understanding of phenomena related to energy production and consumption and any kind of impact on environment and sustainability. Original contributions are welcomed but are not limited to renewable energies, global transnational studies, and spatiotemporal modelling, including all steps from the entire Data Science process such as pre-processing, postprocessing, or the relationship between decision support and data science results. Methodological contributions, real applications, or reviews exploring the current challenges in sustainability related to the state-of-the-art in energy and how Artificial Intelligence and Data Science can help are welcomed.

Prof. Dr. Karina Gibert
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. Energies 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 1800 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

  • artificial intelligence 
  • data science 
  • data mining 
  • sustainability 
  • energy management

Published Papers (8 papers)

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Research

Open AccessArticle
“Dust in the Wind...”, Deep Learning Application to Wind Energy Time Series Forecasting
Energies 2019, 12(12), 2385; https://doi.org/10.3390/en12122385
Received: 29 May 2019 / Revised: 12 June 2019 / Accepted: 13 June 2019 / Published: 21 June 2019
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Abstract
To balance electricity production and demand, it is required to use different prediction techniques extensively. Renewable energy, due to its intermittency, increases the complexity and uncertainty of forecasting, and the resulting accuracy impacts all the different players acting around the electricity systems around [...] Read more.
To balance electricity production and demand, it is required to use different prediction techniques extensively. Renewable energy, due to its intermittency, increases the complexity and uncertainty of forecasting, and the resulting accuracy impacts all the different players acting around the electricity systems around the world like generators, distributors, retailers, or consumers. Wind forecasting can be done under two major approaches, using meteorological numerical prediction models or based on pure time series input. Deep learning is appearing as a new method that can be used for wind energy prediction. This work develops several deep learning architectures and shows their performance when applied to wind time series. The models have been tested with the most extensive wind dataset available, the National Renewable Laboratory Wind Toolkit, a dataset with 126,692 wind points in North America. The architectures designed are based on different approaches, Multi-Layer Perceptron Networks (MLP), Convolutional Networks (CNN), and Recurrent Networks (RNN). These deep learning architectures have been tested to obtain predictions in a 12-h ahead horizon, and the accuracy is measured with the coefficient of determination, the R² method. The application of the models to wind sites evenly distributed in the North America geography allows us to infer several conclusions on the relationships between methods, terrain, and forecasting complexity. The results show differences between the models and confirm the superior capabilities on the use of deep learning techniques for wind speed forecasting from wind time series data. Full article
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Open AccessArticle
An Evolutionary Computational Approach for the Problem of Unit Commitment and Economic Dispatch in Microgrids under Several Operation Modes
Energies 2019, 12(11), 2143; https://doi.org/10.3390/en12112143
Received: 17 April 2019 / Revised: 10 May 2019 / Accepted: 30 May 2019 / Published: 4 June 2019
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Abstract
In the last decades, new types of generation technologies have emerged and have been gradually integrated into the existing power systems, moving their classical architectures to distributed systems. Despite the positive features associated to this paradigm, new problems arise such as coordination and [...] Read more.
In the last decades, new types of generation technologies have emerged and have been gradually integrated into the existing power systems, moving their classical architectures to distributed systems. Despite the positive features associated to this paradigm, new problems arise such as coordination and uncertainty. In this framework, microgrids constitute an effective solution to deal with the coordination and operation of these distributed energy resources. This paper proposes a Genetic Algorithm (GA) to address the combined problem of Unit Commitment (UC) and Economic Dispatch (ED). With this end, a model of a microgrid is introduced together with all the control variables and physical constraints. To optimally operate the microgrid, three operation modes are introduced. The first two attend to optimize economical and environmental factors, while the last operation mode considers the errors induced by the uncertainties in the demand forecasting. Therefore, it achieves a robust design that guarantees the power supply for different confidence levels. Finally, the algorithm was applied to an example scenario to illustrate its performance. The achieved simulation results demonstrate the validity of the proposed approach. Full article
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Open AccessArticle
Current Status Investigation and Predicting Carbon Dioxide Emission in Latin American Countries by Connectionist Models
Energies 2019, 12(10), 1916; https://doi.org/10.3390/en12101916
Received: 24 March 2019 / Revised: 3 May 2019 / Accepted: 6 May 2019 / Published: 19 May 2019
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Abstract
Currently, one of the biggest concerns of human beings is greenhouse gas emissions, especially carbon dioxide emissions in developed and under-developed countries. In this study, connectionist models including LSSVM (Least Square Support Vector Machine) and evolutionary methods are employed for predicting the amount [...] Read more.
Currently, one of the biggest concerns of human beings is greenhouse gas emissions, especially carbon dioxide emissions in developed and under-developed countries. In this study, connectionist models including LSSVM (Least Square Support Vector Machine) and evolutionary methods are employed for predicting the amount of CO 2 emission in six Latin American countries, i.e., Brazil, Mexico, Argentina, Peru, Chile, Venezuela and Uruguay. The studied region is modelled based on the available input data in terms of million tons including oil (million tons), gas (million tons oil equivalent), coal (million tons oil equivalent), R e w (million tons oil equivalent) and Gross Domestic Product (GDP) in terms of billion U.S. dollars. Moreover, the available patents in the field of climate change mitigation in six Latin American countries, namely Brazil, Mexico, Argentina, Peru, Chile, Venezuela and Uruguay, have been reviewed and analysed. The results show that except Venezuela, all other mentioned countries have invested in renewable energy R&D activities. Brazil and Argentina have the highest share of renewable energies, which account for 60% and 72%, respectively. Full article
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Open AccessArticle
Predicting Energy Generation Using Forecasting Techniques in Catalan Reservoirs
Energies 2019, 12(10), 1832; https://doi.org/10.3390/en12101832
Received: 28 March 2019 / Revised: 24 April 2019 / Accepted: 1 May 2019 / Published: 14 May 2019
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Abstract
Reservoirs are natural or artificial lakes used as a source of water supply for society daily applications. In addition, hydroelectric power plants produce electricity while water flows through the reservoir. However, reservoirs are limited natural resources since water levels vary according to annual [...] Read more.
Reservoirs are natural or artificial lakes used as a source of water supply for society daily applications. In addition, hydroelectric power plants produce electricity while water flows through the reservoir. However, reservoirs are limited natural resources since water levels vary according to annual rainfalls and other natural events, and consequently, the energy generation. Therefore, forecasting techniques are helpful to predict water level, and thus, electricity production. This paper examines state-of-the-art methods to predict the water level in Catalan reservoirs comparing two approaches: using the water level uniquely, uni-variant; and adding meteorological data, multi-variant. With respect to relating works, our contribution includes a longer times series prediction keeping a high precision. The results return that combining Support Vector Machine and the multi-variant approach provides the highest precision with an R 2 value of 0.99. Full article
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Open AccessArticle
Bridging the Gap between Energy Consumption and Distribution through Non-Technical Loss Detection
Energies 2019, 12(9), 1748; https://doi.org/10.3390/en12091748
Received: 29 March 2019 / Revised: 25 April 2019 / Accepted: 30 April 2019 / Published: 8 May 2019
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Abstract
The application of Artificial Intelligence techniques in industry equips companies with new essential tools to improve their principal processes. This is especially true for energy companies, as they have the opportunity, thanks to the modernization of their installations, to exploit a large amount [...] Read more.
The application of Artificial Intelligence techniques in industry equips companies with new essential tools to improve their principal processes. This is especially true for energy companies, as they have the opportunity, thanks to the modernization of their installations, to exploit a large amount of data with smart algorithms. In this work we explore the possibilities that exist in the implementation of Machine-Learning techniques for the detection of Non-Technical Losses in customers. The analysis is based on the work done in collaboration with an international energy distribution company. We report on how the success in detecting Non-Technical Losses can help the company to better control the energy provided to their customers, avoiding a misuse and hence improving the sustainability of the service that the company provides. Full article
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Open AccessArticle
A Residential House Comparative Case Study Using Market Available Smart Plugs and EnAPlugs with Shared Knowledge
Energies 2019, 12(9), 1647; https://doi.org/10.3390/en12091647
Received: 21 February 2019 / Revised: 17 April 2019 / Accepted: 28 April 2019 / Published: 30 April 2019
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Abstract
Smart home devices currently available on the market can be used for remote monitoring and control. Energy management systems can take advantage of this and deploy solutions that can be implemented in our homes. One of the big enablers is smart plugs that [...] Read more.
Smart home devices currently available on the market can be used for remote monitoring and control. Energy management systems can take advantage of this and deploy solutions that can be implemented in our homes. One of the big enablers is smart plugs that allow the control of electrical resources while providing a retrofitting solution, hence avoiding the need for replacing the electrical devices. However, current so-called smart plugs lack the ability to understand the environment they are in, or the electrical appliance/resource they are controlling. This paper applies environment awareness smart plugs (EnAPlugs) able to provide enough data for energy management systems or act on its own, via a multi-agent approach. A case study is presented, which shows the application of the proposed approach in a house where 17 EnAPlugs are deployed. Results show the ability to shared knowledge and perform individual resource optimizations. This paper evidences that by integrating artificial intelligence on devices, energy advantages can be observed and used in favor of users, providing comfort and savings. Full article
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Open AccessArticle
Environmental Decision Support System for Biogas Upgrading to Feasible Fuel
Energies 2019, 12(8), 1546; https://doi.org/10.3390/en12081546
Received: 20 March 2019 / Revised: 11 April 2019 / Accepted: 18 April 2019 / Published: 24 April 2019
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Abstract
Biogas production is a growing market and the existing conversion technologies require different biogas quality and characteristics. In pursuance of assisting decision-makers in biogas upgrading an environmental decision support system (EDSS) was developed. Since the field is rapidly progressing, this tool is easily [...] Read more.
Biogas production is a growing market and the existing conversion technologies require different biogas quality and characteristics. In pursuance of assisting decision-makers in biogas upgrading an environmental decision support system (EDSS) was developed. Since the field is rapidly progressing, this tool is easily updatable with new data from technical and scientific literature through the knowledge acquisition level. By a thorough technology review, the diagnosis level evaluates a wide spectrum of technologies for eliminating siloxanes, H2S, and CO2 from biogas, which are scored in a supervision level based upon environmental, economic, social and technical criteria. The sensitivity of the user towards those criteria is regarded by the EDSS giving a response based on its preferences. The EDSS was validated with data from a case-study for removing siloxanes from biogas in a sewage plant. The tool described the flow diagram of treatment alternatives and estimated the performance and effluent quality, which matched the treatment currently given in the facility. Adsorption onto activated carbon was the best-ranked technology due to its great efficiency and maturity as a commercial technology. On the other hand, biological technologies obtained high scores when economic and environmental criteria were preferred. The sensitivity analysis proved to be effective allowing the identification of the challenges and opportunities for the technologies considered. Full article
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Open AccessArticle
Feature Selection Algorithms for Wind Turbine Failure Prediction
Energies 2019, 12(3), 453; https://doi.org/10.3390/en12030453
Received: 3 December 2018 / Revised: 27 January 2019 / Accepted: 28 January 2019 / Published: 31 January 2019
Cited by 1 | PDF Full-text (656 KB) | HTML Full-text | XML Full-text
Abstract
It is well known that each year the wind sector has profit losses due to wind turbine failures and operation and maintenance costs. Therefore, operations related to these actions are crucial for wind farm operators and linked companies. One of the key points [...] Read more.
It is well known that each year the wind sector has profit losses due to wind turbine failures and operation and maintenance costs. Therefore, operations related to these actions are crucial for wind farm operators and linked companies. One of the key points for failure prediction on wind turbine using SCADA data is to select the optimal or near optimal set of inputs that can feed the failure prediction (prognosis) algorithm. Due to a high number of possible predictors (from tens to hundreds), the optimal set of inputs obtained by exhaustive-search algorithms is not viable in the majority of cases. In order to tackle this issue, show the viability of prognosis and select the best set of variables from more than 200 analogous variables recorded at intervals of 5 or 10 min by the wind farm’s SCADA, in this paper a thorough study of automatic input selection algorithms for wind turbine failure prediction is presented and an exhaustive-search-based quasi-optimal (QO) algorithm, which has been used as a reference, is proposed. In order to evaluate the performance, a k-NN classification algorithm is used. Results showed that the best automatic feature selection method in our case-study is the conditional mutual information (CMI), while the worst one is the mutual information feature selection (MIFS). Furthermore, the effect of the number of neighbours (k) is tested. Experiments demonstrate that k = 1 is the best option if the number of features is higher than 3. The experiments carried out in this work have been extracted from measures taken along an entire year and corresponding to gearbox and transmission systems of Fuhrländer wind turbines. Full article
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