Special Issue "Predicting the Future—Big Data and Machine Learning"

A special issue of Energies (ISSN 1996-1073).

Deadline for manuscript submissions: closed (31 March 2020).

Special Issue Editor

Dr. Fernando Sánchez Lasheras
E-Mail Website
Guest Editor
Department of Mathematics, Oviedo University, Faculty of Sciences, calle Federico García Lorca 18 33007 Oviedo, Asturias, Spain
Interests: machine learning; deep learning; atmospheric turbulence; astronomy; adaptive optics; solar observation
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Special Issue Information

Dear Colleagues,

The Guest Editor is inviting submissions for a Special Issue of Energies on the subject of “Predicting the Future—Big Data and Machine Learning”. Due to the increase in the capabilities of microprocessors and to the advent of graphics processing units (GPUs) in recent decades, the use of machine learning methodologies has become popular in many fields of science and technology. This fact, together with the availability of large amounts of information, has meant that, nowadays, machine learning and Big Data have an important presence in the field of Energy.

This Special Issue will focus on applications of Big Data architectures and machine learning methodologies in the field of energy. Topics of interest for publication include, but are not limited to:

  • Big data architectures of power supply systems;
  • Energy exploration and exploitation: energy management modeling;
  • Energy in physical cosmology;
  • Energy saving and efficiency models;
  • Environmental effects of energy consumption;
  • Pollution forecasting related to the generation of energy;
  • Prediction of occupational health and safety outcomes in the energy industry;
  • Price forecast prediction of raw materials for energy production: coal, gas, oil, uranium, etc.;
  • Predictive analysis of energy resources;
  • Energy management of smart buildings.

We invite submission detailing innovative technical developments, reviews, case studies, and analyses, as well as assessments and papers from other disciplines which are relevant to the field of energy from the point of view of Big Data and machine learning applications.

Dr. Fernando Sánchez Lasheras
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 2000 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

  • big data architectures
  • machine learning
  • pollution forecast
  • raw materials price forecast
  • deep learning
  • convolutional neural networks

Published Papers (8 papers)

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Research

Article
Non-Intrusive Load Monitoring (NILM) for Energy Disaggregation Using Soft Computing Techniques
Energies 2020, 13(12), 3117; https://doi.org/10.3390/en13123117 - 16 Jun 2020
Cited by 6 | Viewed by 1232
Abstract
Non-intrusive load monitoring (NILM) has become an important subject of study, since it provides benefits to both consumers and utility companies. The analysis of smart meter signals is useful for identifying consumption patterns and user behaviors, in order to make predictions and optimizations [...] Read more.
Non-intrusive load monitoring (NILM) has become an important subject of study, since it provides benefits to both consumers and utility companies. The analysis of smart meter signals is useful for identifying consumption patterns and user behaviors, in order to make predictions and optimizations to anticipate the use of electrical appliances at home. However, the problem with this kind of analysis rests in how to isolate individual appliances from an aggregated consumption signal. In this work, we propose an unsupervised disaggregation method based on a controlled dataset obtained using smart meters in a standard household. By using soft computing techniques, the proposed methodology can identify the behavior of each of the devices from aggregated consumption records. In the approach developed in this work, it is possible to detect changes in power levels and to build a box model, consisting of a sequence of rectangles of different heights (power) and widths (time), which is highly adaptable to the real-life working conditions of household appliances. The system was developed and tested using data collected at households in France and the UK (UK-domestic appliance-level electricity (DALE) dataset). The proposed analysis method serves as a basis to be applied to large amounts of data collected by distribution companies with smart meters. Full article
(This article belongs to the Special Issue Predicting the Future—Big Data and Machine Learning)
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Article
Energy Multiphase Model for Biocoal Conversion Systems by Means of a Nodal Network
Energies 2020, 13(11), 2728; https://doi.org/10.3390/en13112728 - 29 May 2020
Cited by 2 | Viewed by 644
Abstract
The coal-producing territories in the world are facing the production of renewable energy in their thermal systems. The production of biocoal has emerged as one of the most promising thermo-energetic conversion technologies, intended as an alternative fuel to coal. The aim of this [...] Read more.
The coal-producing territories in the world are facing the production of renewable energy in their thermal systems. The production of biocoal has emerged as one of the most promising thermo-energetic conversion technologies, intended as an alternative fuel to coal. The aim of this research is to assess how the model of biomass to biocoal conversion in mining areas is applied for thermal systems engineering. The Central Asturian Coal Basin (CACB; Spain) is the study area. The methodology used allows for the analysis of the resource as well as the thermo-energetic conversion and the management of the bioenergy throughout the different phases in a process of analytical hierarchy. This is carried out using a multiphase mathematical algorithm based on the availability of resources, the thermo-energetic conversion, and the energy management in the area of study. Based on the working conditions, this research highlights the potential of forest biomass as a raw material for biocoal production as well as for electrical and thermal purposes. The selected node operates through the bioenergy-match mode, which has yielded outputs of 23 MWe and 172 MWth, respectively. Full article
(This article belongs to the Special Issue Predicting the Future—Big Data and Machine Learning)
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Article
Prediction of Health-Related Leave Days among Workers in the Energy Sector by Means of Genetic Algorithms
Energies 2020, 13(10), 2475; https://doi.org/10.3390/en13102475 - 14 May 2020
Cited by 1 | Viewed by 582
Abstract
In this research, a model is proposed for predicting the number of days absent from work due to sick or health-related leave among workers in the industry sector, according to ergonomic, social and work-related factors. It employs selected microdata from the Sixth European [...] Read more.
In this research, a model is proposed for predicting the number of days absent from work due to sick or health-related leave among workers in the industry sector, according to ergonomic, social and work-related factors. It employs selected microdata from the Sixth European Working Conditions Survey (EWCS) and combines a genetic algorithm with Multivariate Adaptive Regression Splines (MARS). The most relevant explanatory variables identified by the model can be included in the following categories: ergonomics, psychosocial factors, working conditions and personal data and physiological characteristics. These categories are interrelated, and it is difficult to establish boundaries between them. Any managing program has to act on factors that affect the employees’ general health status, process design, workplace environment, ergonomics and psychosocial working context, among others, to achieve success. This has an extensive field of application in the energy sector. Full article
(This article belongs to the Special Issue Predicting the Future—Big Data and Machine Learning)
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Article
Optimizing Predictor Variables in Artificial Neural Networks When Forecasting Raw Material Prices for Energy Production
Energies 2020, 13(8), 2017; https://doi.org/10.3390/en13082017 - 18 Apr 2020
Cited by 2 | Viewed by 641
Abstract
This paper applies a heuristic approach to optimize the predictor variables in artificial neural networks when forecasting raw material prices for energy production (coking coal, natural gas, crude oil and coal) to achieve a better forecast. Two goals are (1) to determine the [...] Read more.
This paper applies a heuristic approach to optimize the predictor variables in artificial neural networks when forecasting raw material prices for energy production (coking coal, natural gas, crude oil and coal) to achieve a better forecast. Two goals are (1) to determine the optimum number of time-delayed terms or past values forming the lagged variables and (2) to improve the forecast accuracy by adding intrinsic signals to the lagged variables. The conclusions clearly are in opposition to the actual scientific literature: when addressing the lagged variable size, the results do not confirm relationships among their size, representativeness and estimation accuracy. It is also possible to verify an important effect of the results on the lagged variable size. Finally, adding the order in the time series of the lagged variables to form the predictor variables improves the forecast accuracy in most cases. Full article
(This article belongs to the Special Issue Predicting the Future—Big Data and Machine Learning)
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Article
Crude Oil Prices Forecasting: An Approach of Using CEEMDAN-Based Multi-Layer Gated Recurrent Unit Networks
Energies 2020, 13(7), 1543; https://doi.org/10.3390/en13071543 - 25 Mar 2020
Cited by 5 | Viewed by 888
Abstract
Accurate prediction of crude oil prices is meaningful for reducing firm risks, stabilizing commodity prices and maintaining national financial security. Wrong crude oil price forecasts can bring huge losses to governments, enterprises, investors and even cause economic and social instability. Many classic econometrics [...] Read more.
Accurate prediction of crude oil prices is meaningful for reducing firm risks, stabilizing commodity prices and maintaining national financial security. Wrong crude oil price forecasts can bring huge losses to governments, enterprises, investors and even cause economic and social instability. Many classic econometrics and computational approaches show good performance for the ordinary time series prediction tasks, but not satisfactory in crude oil price predictions. They ignore the characteristics of non-linearity and non-stationarity of crude oil prices data, which hinder an accurate prediction and eventually lead to poor accuracy or the wrong result. Empirical mode decomposition (EMD) and ensemble EMD (EEMD) solve the problems of non-stationary time series forecasting, but they also generate new problems of mode mixing and reconstruction errors. We propose a hybrid method that is combination of the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and multi-layer gated recurrent unit (ML-GRU) neural network to solve the abovementioned issues. This not only deals with the issue of mode mixing effectively, but also makes the reconstruction error of data close to zero. Multi-layer GRU has an excellent ability of nonlinear data-fitting. The experimental results of real WTI crude oil dataset show that the proposed approach perform better in crude oil prices forecasts than some state-of-the-art models. Full article
(This article belongs to the Special Issue Predicting the Future—Big Data and Machine Learning)
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Article
Understanding and Modeling Climate Impacts on Photosynthetic Dynamics with FLUXNET Data and Neural Networks
Energies 2020, 13(6), 1322; https://doi.org/10.3390/en13061322 - 12 Mar 2020
Cited by 1 | Viewed by 952
Abstract
Global warming, which largely results from excessive carbon emission, has become an increasingly heated international issue due to its ever-detereorating trend and the profound consequences. Plants sequester a large amount of atmospheric CO 2 via photosynthesis, thus greatly mediating global warming. In this study, we aim to model the temporal dynamics of photosynthesis for two different vegetation types to further understand the controlling factors of photosynthesis machinery. We experimented with a feedforward neural network that does not utilize past histories, as well as two networks that integrate past and present information, long short-term memory and transformer. Our results showed that one single climate driver, shortwave radiation, carries the most information with respect to prediction of upcoming photosynthetic activities. We also demonstrated that photosynthesis and its interactions with climate drivers, such as temperature, precipitation, radiation, and vapor pressure deficit, has an internal system memory of about two weeks. Thus, the predictive model could be best trained with historical data over the past two weeks and could best predict temporal evolution of photosynthesis two weeks into the future. Full article
(This article belongs to the Special Issue Predicting the Future—Big Data and Machine Learning)
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Article
Multivariate Analysis to Relate CTOD Values with Material Properties in Steel Welded Joints for the Offshore Wind Power Industry
Energies 2019, 12(20), 4001; https://doi.org/10.3390/en12204001 - 21 Oct 2019
Cited by 3 | Viewed by 989
Abstract
The increasingly mechanical requirements of offshore structures have established the relevance of fracture mechanics-based quality control in welded joints. For this purpose, crack tip opening displacement (CTOD) at a given distance from the crack tip has been considered one of the most suited [...] Read more.
The increasingly mechanical requirements of offshore structures have established the relevance of fracture mechanics-based quality control in welded joints. For this purpose, crack tip opening displacement (CTOD) at a given distance from the crack tip has been considered one of the most suited parameters for modeling and control of crack growth, and it is broadly used at the industrial level. We have modeled, through multivariate analysis techniques, the relationships among CTOD values and other material properties (such as hardness, chemical composition, toughness, and microstructural morphology) in high-thickness offshore steel welded joints. In order to create this model, hundreds of tests were done on 72 real samples, which were welded with a wide range of real industrial parameters. The obtained results were processed and evaluated with different multivariate techniques, and we established the significance of all the chosen explanatory variables and the good predictive capability of the CTOD tests within the limits of the experimental variation. By establishing the use of this model, significant savings can be achieved in the manufacturing of wind generators, as CTOD tests are more expensive and complex than the proposed alternatives. Additionally, this model allows for some technical conclusions. Full article
(This article belongs to the Special Issue Predicting the Future—Big Data and Machine Learning)
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Article
Short-Term Load Forecasting for CCHP Systems Considering the Correlation between Heating, Gas and Electrical Loads Based on Deep Learning
Energies 2019, 12(17), 3308; https://doi.org/10.3390/en12173308 - 28 Aug 2019
Cited by 7 | Viewed by 1041
Abstract
Combined cooling, heating, and power (CCHP) systems is a distributed energy system that uses the power station or heat engine to generate electricity and useful heat simultaneously. Due to its wide range of advantages including efficiency, ecological, and financial, the CCHP will be [...] Read more.
Combined cooling, heating, and power (CCHP) systems is a distributed energy system that uses the power station or heat engine to generate electricity and useful heat simultaneously. Due to its wide range of advantages including efficiency, ecological, and financial, the CCHP will be the main direction of the integrated system. The accurate prediction of heating, gas, and electrical loads plays an essential role in energy management in CCHP systems. This paper combined long short-term memory (LSTM) network and convolutional neural network (CNN) to design a novel hybrid neural network for short-term loads forecasting considering their correlation. Pearson correlation coefficient will be utilized to measure the temporal correlation between current load and historical loads, and analyze the coupling between heating, gas and electrical loads. The dropout technique is proposed to solve the over-fitting of the network due to the lack of data diversity and network parameter redundancy. The case study shows that considering the coupling between heating, gas and electrical loads can effectively improve the forecasting accuracy, the performance of the proposed approach is better than that of the traditional methods. Full article
(This article belongs to the Special Issue Predicting the Future—Big Data and Machine Learning)
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