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Advanced Optimization Methods and Big Data Applications in Energy Demand...
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Advanced Optimization Methods and Big Data Applications in Energy Demand Forecast

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Energy Science and Technology".

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 22341

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editors

Prof. Dr. Federico Divina

E-Mail Website1 Website2
Guest Editor
Division of Computer Science, Universidad Pablo de Olavide, ES-41013 Seville, Spain
Interests: knowledge extraction; soft computing; high performance computing; data streaming
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Francisco A. Gómez Vela

E-Mail Website
Guest Editor
School of Engineerings, Pablo de Olavide University, ES-41013 Seville, Spain
Interests: computer vision; machine learning; image and signal processing; 5G communications; IoT
Special Issues, Collections and Topics in MDPI journals
Dr. Miguel García-Torres

E-Mail Website
Guest Editor
Data Science and Big Data Lab, Universidad Pablo de Olavide, ES-41013 Seville, Spain
Interests: machine learning; bioinformatics; astrostatistics; big data
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The growing utilization of data collectors in energy systems has resulted in the collection of a very high volume of data. For instance, smart sensors are now widely used in energy production and energy consumption systems. It follows that such Big Data allow a number of opportunities and challenges for informed decision-making. 

Very powerful approaches have been developed in the context of data science and big data analytics in recent years. Such approaches deal with huge datasets, considering all samples and measurements, as well as including many additional features. With them, automated machine learning methods for extracting relevant patterns, high-performance computing or data visualization are now being successfully applied to energy demand forecasting. 

The aim of this Special Issue is to gather the latest advancements in energy demand forecast, and in particular with the use of advanced optimization methods and Big Data techniques. Here, by energy, we mean any kind of energy, e.g., electrical, solar, microwave, wind. 

We encourage researchers to share their original works in the fields of energy demand forecasting, with a particular emphasis on applications. Topics of primary interest include but are not limited to: 

  1. Advanced optimization methods for energy demand forecast;
  2. Big Data techniques for energy demand forecast;
  3. Optimization methods and big data in energy-related time series forecasting;
  4. Optimization methods and big data in nonparametric time series approaches;

Prof. Dr. Federico Divina
Prof. Dr. Francisco A. Gómez Vela
Prof. Dr. Miguel García-Torres
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 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. Applied Sciences 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.

Published Papers (6 papers)

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Editorial

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2 pages, 152 KiB  
Editorial
Advanced Optimization Methods and Big Data Applications in Energy Demand Forecast
by Federico Divina, Francisco Gómez-Vela and Miguel García-Torres
Appl. Sci. 2021, 11(3), 1261; https://0-doi-org.brum.beds.ac.uk/10.3390/app11031261 - 30 Jan 2021
Viewed by 1308
Abstract
The use of data collectors in energy systems is growing more and more [...] Full article
(This article belongs to the Special Issue Advanced Optimization Methods and Big Data Applications in Energy Demand Forecast)

Research

Jump to: Editorial

22 pages, 10073 KiB  
Article
The Impact of Data Filtration on the Accuracy of Multiple Time-Domain Forecasting for Photovoltaic Power Plants Generation
by Stanislav A. Eroshenko, Alexandra I. Khalyasmaa, Denis A. Snegirev, Valeria V. Dubailova, Alexey M. Romanov and Denis N. Butusov
Appl. Sci. 2020, 10(22), 8265; https://doi.org/10.3390/app10228265 - 21 Nov 2020
Cited by 7 | Viewed by 1798
Abstract
The paper reports the forecasting model for multiple time-domain photovoltaic power plants, developed in response to the necessity of bad weather days’ accurate and robust power generation forecasting. We provide a brief description of the piloted short-term forecasting system and place under close [...] Read more.
The paper reports the forecasting model for multiple time-domain photovoltaic power plants, developed in response to the necessity of bad weather days’ accurate and robust power generation forecasting. We provide a brief description of the piloted short-term forecasting system and place under close scrutiny the main sources of photovoltaic power plants’ generation forecasting errors. The effectiveness of the empirical approach versus unsupervised learning was investigated in application to source data filtration in order to improve the power generation forecasting accuracy for unstable weather conditions. The k-nearest neighbors’ methodology was justified to be optimal for initial data filtration, based on the clusterization results, associated with peculiar weather and seasonal conditions. The photovoltaic power plants’ forecasting accuracy improvement was further investigated for a one hour-ahead time-domain. It was proved that operational forecasting could be implemented based on the results of short-term day-ahead forecast mismatches predictions, which form the basis for multiple time-domain integrated forecasting tools. After a comparison of multiple time series forecasting approaches, operational forecasting was realized based on the second-order autoregression function and applied to short-term forecasting errors with the resulting accuracy of 87%. In the concluding part of the article the authors from the points of view of computational efficiency and scalability proposed the hardware system composition. Full article
(This article belongs to the Special Issue Advanced Optimization Methods and Big Data Applications in Energy Demand Forecast)
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14 pages, 401 KiB  
Article
Hybridizing Deep Learning and Neuroevolution: Application to the Spanish Short-Term Electric Energy Consumption Forecasting
by Federico Divina, José Francisco Torres Maldonado, Miguel García-Torres, Francisco Martínez-Álvarez and Alicia Troncoso
Appl. Sci. 2020, 10(16), 5487; https://0-doi-org.brum.beds.ac.uk/10.3390/app10165487 - 07 Aug 2020
Cited by 13 | Viewed by 2760
Abstract
The electric energy production would be much more efficient if accurate estimations of the future demand were available, since these would allow allocating only the resources needed for the production of the right amount of energy required. With this motivation in mind, we [...] Read more.
The electric energy production would be much more efficient if accurate estimations of the future demand were available, since these would allow allocating only the resources needed for the production of the right amount of energy required. With this motivation in mind, we propose a strategy, based on neuroevolution, that can be used to this aim. Our proposal uses a genetic algorithm in order to find a sub-optimal set of hyper-parameters for configuring a deep neural network, which can then be used for obtaining the forecasting. Such a strategy is justified by the observation that the performances achieved by deep neural networks are strongly dependent on the right setting of the hyper-parameters, and genetic algorithms have shown excellent search capabilities in huge search spaces. Moreover, we base our proposal on a distributed computing platform, which allows its use on a large time-series. In order to assess the performances of our approach, we have applied it to a large dataset, related to the electric energy consumption registered in Spain over almost 10 years. Experimental results confirm the validity of our proposal since it outperforms all other forecasting techniques to which it has been compared. Full article
(This article belongs to the Special Issue Advanced Optimization Methods and Big Data Applications in Energy Demand Forecast)
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18 pages, 944 KiB  
Article
Analysis of the Impact of Residential Property and Equipment on Building Energy Efficiency and Consumption—A Data Mining Approach
by Mahsa Nazeriye, Abdorrahman Haeri and Francisco Martínez-Álvarez
Appl. Sci. 2020, 10(10), 3589; https://0-doi-org.brum.beds.ac.uk/10.3390/app10103589 - 22 May 2020
Cited by 6 | Viewed by 2282
Abstract
Human living could become very difficult due to a lack of energy. The household sector plays a significant role in energy consumption. Trying to optimize and achieve efficient energy consumption can lead to large-scale energy savings. The aim of this paper is to [...] Read more.
Human living could become very difficult due to a lack of energy. The household sector plays a significant role in energy consumption. Trying to optimize and achieve efficient energy consumption can lead to large-scale energy savings. The aim of this paper is to identify the equipment and property affecting energy efficiency and consumption in residential homes. For this purpose, a hybrid data-mining approach based on K-means algorithms and decision trees is presented. To analyze the approach, data is modeled once using the approach and then without it. A data set of residential homes of England and Wales is arranged in low, medium and high consumption clusters. The C5.0 algorithm is run on each cluster to extract factors affecting energy efficiency. The comparison of the modeling results, and also their accuracy, prove that the approach employed has the ability to extract the findings with greater accuracy and detail than in other cases. The installation of boilers, using cavity walls, and installing insulation could improve energy efficiency. Old homes and the usage of economy 7 electricity have an unfavorable effect on energy efficiency, but the approach shows that each cluster behaved differently in these factors related to energy efficiency and has unique results. Full article
(This article belongs to the Special Issue Advanced Optimization Methods and Big Data Applications in Energy Demand Forecast)
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16 pages, 7384 KiB  
Article
Stability of Multiple Seasonal Holt-Winters Models Applied to Hourly Electricity Demand in Spain
by Óscar Trull, J. Carlos García-Díaz and Alicia Troncoso
Appl. Sci. 2020, 10(7), 2630; https://0-doi-org.brum.beds.ac.uk/10.3390/app10072630 - 10 Apr 2020
Cited by 16 | Viewed by 3037
Abstract
Electricity management and production depend heavily on demand forecasts made. Any mismatch between the energy demanded with respect to that produced supposes enormous losses for the consumer. Transmission System Operators use time series-based tools to forecast accurately the future demand and set the [...] Read more.
Electricity management and production depend heavily on demand forecasts made. Any mismatch between the energy demanded with respect to that produced supposes enormous losses for the consumer. Transmission System Operators use time series-based tools to forecast accurately the future demand and set the production program. One of the most effective and highly used methods are Holt-Winters. Recently, the incorporation of the multiple seasonal Holt-Winters methods has improved the accuracy of the predictions. These forecasts, depend greatly on the parameters with which the model is constructed. The forecasters need to deal with these parameters values when operating the model. In this article, the parameters space of the multiple seasonal Holt-Winters models applied to electricity demand in Spain is analysed and discussed. The parameters stability analysis leads to forecasters better understanding the behaviour of the predictions and managing their exploitation efficiently. The analysis addresses different time windows, depending on the period of the year as well as different training set sizes. The results show the influence of the calendar effect on these parameters and if it is necessary or not to update them in order to obtain a good accuracy over time. Full article
(This article belongs to the Special Issue Advanced Optimization Methods and Big Data Applications in Energy Demand Forecast)
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17 pages, 851 KiB  
Article
Temporal Convolutional Networks Applied to Energy-Related Time Series Forecasting
by Pedro Lara-Benítez, Manuel Carranza-García, José M. Luna-Romera and José C. Riquelme
Appl. Sci. 2020, 10(7), 2322; https://0-doi-org.brum.beds.ac.uk/10.3390/app10072322 - 28 Mar 2020
Cited by 128 | Viewed by 10233
Abstract
Modern energy systems collect high volumes of data that can provide valuable information about energy consumption. Electric companies can now use historical data to make informed decisions on energy production by forecasting the expected demand. Many deep learning models have been proposed to [...] Read more.
Modern energy systems collect high volumes of data that can provide valuable information about energy consumption. Electric companies can now use historical data to make informed decisions on energy production by forecasting the expected demand. Many deep learning models have been proposed to deal with these types of time series forecasting problems. Deep neural networks, such as recurrent or convolutional, can automatically capture complex patterns in time series data and provide accurate predictions. In particular, Temporal Convolutional Networks (TCN) are a specialised architecture that has advantages over recurrent networks for forecasting tasks. TCNs are able to extract long-term patterns using dilated causal convolutions and residual blocks, and can also be more efficient in terms of computation time. In this work, we propose a TCN-based deep learning model to improve the predictive performance in energy demand forecasting. Two energy-related time series with data from Spain have been studied: the national electric demand and the power demand at charging stations for electric vehicles. An extensive experimental study has been conducted, involving more than 1900 models with different architectures and parametrisations. The TCN proposal outperforms the forecasting accuracy of Long Short-Term Memory (LSTM) recurrent networks, which are considered the state-of-the-art in the field. Full article
(This article belongs to the Special Issue Advanced Optimization Methods and Big Data Applications in Energy Demand Forecast)
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