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Data Science and Big Data in Energy Forecasting with Applications

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

Deadline for manuscript submissions: closed (10 April 2019) | Viewed by 20921

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Dr. José C. Riquelme

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Guest Editor

Department of Languages and Computer Systems, University of Seville, 41012 Sevilla, Spain
Interests: machine learning; data mining; big data; smart grids
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Prof. Dr. Alicia Troncoso

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Guest Editor

Data Science & Big Data Lab, Pablo de Olavide University, ES-41013 Seville, Spain
Interests: time series; forecasting; data science and big data
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Prof. Dr. Francisco Martínez-Álvarez

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Guest Editor

Department of Computer Science, Pablo de Olavide University, ES-41013 Seville, Spain
Interests: big data; machine learning; time series; forecasting
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Special Issue Information

Dear Colleagues,

This Special Issue focuses on the forecasting of time series, with particular emphasis on energy-related data by means of data science and big data techniques. By energy, we understand any kind of energy, such as electrical, solar, microwave, wind, etc.

Very powerful approaches have been developed in the context of data science and big data analytics during the last years. Such approaches deal with large 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 time series forecasting.

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

Data science and big data in energy time series analysis.
Data science and big data in energy time series modelling.
Data science and big data in energy-related time series forecasting.
Data science and big data in non-parametric time series approaches.

Prof. Dr. José C. Riquelme
Prof. Dr. Alicia Troncoso
Prof. Dr. Francisco Martínez-Álvarez
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. 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 2600 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

energy
time series
forecasting
data science
big data

Published Papers (5 papers)

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Research

23 pages, 772 KiB

Open AccessArticle

A Comparative Study of Time Series Forecasting Methods for Short Term Electric Energy Consumption Prediction in Smart Buildings

by Federico Divina, Miguel García Torres, Francisco A. Goméz Vela and José Luis Vázquez Noguera

Energies 2019, 12(10), 1934; https://0-doi-org.brum.beds.ac.uk/10.3390/en12101934 - 20 May 2019

Cited by 63 | Viewed by 8453

Abstract

Smart buildings are equipped with sensors that allow monitoring a range of building systems including heating and air conditioning, lighting and the general electric energy consumption. Thees data can then be stored and analyzed. The ability to use historical data regarding electric energy consumption could allow improving the energy efficiency of such buildings, as well as help to spot problems related to wasting of energy. This problem is even more important when considering that buildings are some of the largest consumers of energy. In this paper, we are interested in forecasting the energy consumption of smart buildings, and, to this aim, we propose a comparative study of different forecasting strategies that can be used to this aim. To do this, we used the data regarding the electric consumption registered by thirteen buildings located in a university campus in the south of Spain. The empirical comparison of the selected methods on the different data showed that some methods are more suitable than others for this kind of problem. In particular, we show that strategies based on Machine Learning approaches seem to be more suitable for this task. Full article

(This article belongs to the Special Issue Data Science and Big Data in Energy Forecasting with Applications)

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26 pages, 4488 KiB

Open AccessArticle

Prediction of the Optimal Vortex in Synthetic Jets

by Soledad Le Clainche

Energies 2019, 12(9), 1635; https://0-doi-org.brum.beds.ac.uk/10.3390/en12091635 - 29 Apr 2019

Cited by 19 | Viewed by 2658

Abstract

This article presents three different low-order models to predict the main flow patterns in synthetic jets. The first model provides a simple theoretical approach based on experimental solutions explaining how to artificially generate the optimal vortex, which maximizes the production of thrust and system efficiency. The second model is a data-driven method that uses higher-order dynamic mode decomposition (HODMD). To construct this model, (i) Navier–Stokes equations are solved for a very short period of time providing a transient solution, (ii) a group of spatio-temporal data are collected containing the information of the transitory of the numerical simulations, and finally (iii) HODMD decomposes the solution as a Fourier-like expansion of modes that are extrapolated in time, providing accurate predictions of the large size structures describing the general flow dynamics, with a speed-up factor of 8.3 in the numerical solver. The third model is an extension of the second model, which combines HODMD with a low-rank approximation of the spatial domain, which is based on singular value decomposition (SVD). This novel approach reduces the memory requirements by 70% and reduces the computational time to generate the low-order model by 3, maintaining the speed-up factor to 8.3. This technique is suitable to predict the temporal flow patterns in a synthetic jet, showing that the general dynamics is driven by small amplitude variations along the streamwise direction. This new and efficient tool could also be potentially used for data forecasting or flow pattern identification in any type of big database. Full article

(This article belongs to the Special Issue Data Science and Big Data in Energy Forecasting with Applications)

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16 pages, 4343 KiB

Open AccessArticle

Application of Discrete-Interval Moving Seasonalities to Spanish Electricity Demand Forecasting during Easter

by Óscar Trull, J. Carlos García-Díaz and Alicia Troncoso

Energies 2019, 12(6), 1083; https://0-doi-org.brum.beds.ac.uk/10.3390/en12061083 - 21 Mar 2019

Cited by 19 | Viewed by 2389

Abstract

Forecasting electricity demand through time series is a tool used by transmission system operators to establish future operating conditions. The accuracy of these forecasts is essential for the precise development of activity. However, the accuracy of the forecasts is enormously subject to the calendar effect. The multiple seasonal Holt–Winters models are widely used due to the great precision and simplicity that they offer. Usually, these models relate this calendar effect to external variables that contribute to modification of their forecasts a posteriori. In this work, a new point of view is presented, where the calendar effect constitutes a built-in part of the Holt–Winters model. In particular, the proposed model incorporates discrete-interval moving seasonalities. Moreover, a clear example of the application of this methodology to situations that are difficult to treat, such as the days of Easter, is presented. The results show that the proposed model performs well, outperforming the regular Holt–Winters model and other methods such as artificial neural networks and Exponential Smoothing State Space Model with Box-Cox Transformation, ARMA Errors, Trend and Seasonal Components (TBATS) methods. Full article

(This article belongs to the Special Issue Data Science and Big Data in Energy Forecasting with Applications)

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18 pages, 718 KiB

Open AccessArticle

A Novel Hybrid Algorithm to Forecast Functional Time Series Based on Pattern Sequence Similarity with Application to Electricity Demand

by Francisco Martínez-Álvarez, Amandine Schmutz, Gualberto Asencio-Cortés and Julien Jacques

Energies 2019, 12(1), 94; https://0-doi-org.brum.beds.ac.uk/10.3390/en12010094 - 28 Dec 2018

Cited by 20 | Viewed by 3463

Abstract

The forecasting of future values is a very challenging task. In almost all scientific disciplines, the analysis of time series provides useful information and even economic benefits. In this context, this paper proposes a novel hybrid algorithm to forecast functional time series with arbitrary prediction horizons. It integrates a well-known clustering functional data algorithm into a forecasting strategy based on pattern sequence similarity, which was originally developed for discrete time series. The new approach assumes that some patterns are repeated over time, and it attempts to discover them and evaluate their immediate future. Hence, the algorithm first applies a clustering functional time series algorithm, i.e., it assigns labels to every data unit (it may represent either one hour, or one day, or any arbitrary length). As a result, the time series is transformed into a sequence of labels. Later, it retrieves the sequence of labels occurring just after the sample that we want to be forecasted. This sequence is searched for within the historical data, and every time it is found, the sample immediately after is stored. Once the searching process is terminated, the output is generated by weighting all stored data. The performance of the approach has been tested on real-world datasets related to electricity demand and compared to other existing methods, reporting very promising results. Finally, a statistical significance test has been carried out to confirm the suitability of the election of the compared methods. In conclusion, a novel algorithm to forecast functional time series is proposed with very satisfactory results when assessed in the context of electricity demand. Full article

(This article belongs to the Special Issue Data Science and Big Data in Energy Forecasting with Applications)

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15 pages, 5031 KiB

Open AccessArticle

Probabilistic Forecasting Model of Solar Power Outputs Based on the Naïve Bayes Classifier and Kriging Models

by Seungbeom Nam and Jin Hur

Energies 2018, 11(11), 2982; https://0-doi-org.brum.beds.ac.uk/10.3390/en11112982 - 01 Nov 2018

Cited by 28 | Viewed by 3323

Abstract

Solar power’s variability makes managing power system planning and operation difficult. Facilitating a high level of integration of solar power resources into a grid requires maintaining the fundamental power system so that it is stable when interconnected. Accurate and reliable forecasting helps to maintain the system safely given large-scale solar power resources; this paper therefore proposes a probabilistic forecasting approach to solar resources using the R statistics program, applying a hybrid model that considers spatio-temporal peculiarities. Information on how the weather varies at sites of interest is often unavailable, so we use a spatial modeling procedure called kriging to estimate precise data at the solar power plants. The kriging method implements interpolation with geographical property data. In this paper, we perform day-ahead forecasts of solar power based on the probability in one-hour intervals by using a Naïve Bayes Classifier model, which is a classification algorithm. We augment forecasting by taking into account the overall data distribution and applying the Gaussian probability distribution. To validate the proposed hybrid forecasting model, we perform a comparison of the proposed model with a persistence model using the normalized mean absolute error (NMAE). Furthermore, we use empirical data from South Korea’s meteorological towers (MET) to interpolate weather variables at points of interest. Full article

(This article belongs to the Special Issue Data Science and Big Data in Energy Forecasting with Applications)

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