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Machine Learning for Sustainable Energy

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

Deadline for manuscript submissions: closed (1 January 2021) | Viewed by 2952

Special Issue Editors


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Guest Editor
Frankfurt am Main, Frankfurt Institute for Advanced Studies, Ruth-Moufang-Straße 1, 60438 Frankfurt am Main, Germany
Interests: machine learning; theoretical physics and phenomenology; astrophysics; black holes and neutron stars; phases of dense hot nuclear

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Guest Editor
AGH Krakow, aleja Adama Mickiewicza 30, 30-059 Kraków, Poland MDH Västerås, Högskoleplan 1, 722 20 Västerås, Sweden
Interests: Machine Learning, Renewable Energy, Energy Systems, Forecasting
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Frankfurt Institute for Advanced Studies, Ruth-Moufang-Straße 1, 60438 Frankfurt am Main, Germany
Interests: Machine Learning, Deep learning in physics/industry, Heavy-Ion collisions, QCD phase transition

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Guest Editor
Frankfurt Institute for Advanced Studies, Ruth-Moufang-Straße 1, 60438 Frankfurt am Main, Germany
Interests: ): Machine Learning, Deep Learning in Seismology, Seismic methods, Seismic hazard and microzonation, soil liquefaction

E-Mail Website
Guest Editor
Frankfurt Institute for Advanced Studies, Ruth-Moufang-Straße 1, 60438 Frankfurt am Main, Germany
Interests: renewable energy; energy systems; machine learning; applied math
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue covers selected topics on the combination of machine learning with research on sustainable energy.

Energy systems are transforming worldwide to mitigate carbon emissions and global warming. Machine learning, which is an ideal companion to renewable energy, can facilitate the process of energy sector transformation. Because the major sources of renewable energy, wind and solar, are in their very nature, variable, it is a challenging task for a modern society to depend on these sources. Therefore, there is a growing need for novel concepts enabling their efficient and reliable integration into energy systems. Machine learning can help in various ways by providing accurate predictions of future generation and demand, using optimal control to increase the utilization of renewable energy sources, or monitoring the state of the system.

We invite all colleagues to submit an original manuscript with novel research results on this general topic, including, but not limited to, applications of machine learning related to energy system analysis; renewable energy and renewable energy systems; the energy transition; weather data modeling; forecasting of relevant quantities, such as generation, demand, or electricity prices; demand-side management; peer-to-peer energy trading; use of big data in energy research; and other issues relevant to sustainable energy.

Prof. Dr. Horst Stoecker
Dr. Jakub Jurasz
Dr. Kai Zhou
Dr. Nishtha Srivastava
Dr. Alexander Kies
Guest Editors

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 submissions that pass pre-check are 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 2400 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

  • sustainable energy
  • machine learning
  • energy system analysis
  • future energy scenario
  • large-scale optimisation
  • energy informatics
  • energy transition
  • environmental sustainability
  • smart energy markets
  • artificial intelligence
  • big data

Published Papers (1 paper)

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Research

16 pages, 2297 KiB  
Article
Short-Term Solar Power Forecasting: A Combined Long Short-Term Memory and Gaussian Process Regression Method
by Ying Wang, Bo Feng, Qing-Song Hua and Li Sun
Sustainability 2021, 13(7), 3665; https://doi.org/10.3390/su13073665 - 25 Mar 2021
Cited by 28 | Viewed by 2528
Abstract
Solar power is considered a promising power generation candidate in dealing with climate change. Because of the strong randomness, volatility, and intermittence, its safe integration into the smart grid requires accurate short-term forecasting with the required accuracy. The use of solar power should [...] Read more.
Solar power is considered a promising power generation candidate in dealing with climate change. Because of the strong randomness, volatility, and intermittence, its safe integration into the smart grid requires accurate short-term forecasting with the required accuracy. The use of solar power should meet requirements proscribed by environmental law and safety standards applied for consumer protection. First, time-series-based solar power forecasting (SPF) model is developed with the time element and predicted weather information from the local meteorological station. Considering the data correlation, long short-term memory (LSTM) algorithm is utilized for short-term SPF. However, the point prediction provided by LSTM fails in revealing the underlying uncertainty range of the solar power output, which is generally needed in some stochastic optimization frameworks. A novel hybrid strategy combining LSTM and Gaussian process regression (GPR), namely LSTM-GPR, is proposed to obtain a highly accurate point prediction with a reliable interval estimation. The hybrid model is evaluated in comparison with other algorithms in terms of two aspects: Point prediction accuracy and interval forecasting reliability. Numerical investigations confirm the superiority of LSTM algorithm over the conventional neural networks. Furthermore, the performance of the proposed hybrid model is demonstrated to be slightly better than the individual LSTM model and significantly superior to the individual GPR model in both point prediction and interval forecasting, indicating a promising prospect for future SPF applications. Full article
(This article belongs to the Special Issue Machine Learning for Sustainable Energy)
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