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Application of Information Measures for Analysis and Predictability of Renewable Energy Time Series

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A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Information Theory, Probability and Statistics".

Deadline for manuscript submissions: closed (31 May 2019) | Viewed by 14073

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Special Issue Editors

Prof. Dr. Dragutin T. Mihailović

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Guest Editor

Faculty of Agriculture, University of Novi Sad, Novi Sad 21000, Serbia
Interests: complex systems; information theory; climate modeling; UV radiation; water resources; chaos theory; modeling of environmental interfaces; environmental physics; psychophysics

Prof. Dr. Miloud Bessafi

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Guest Editor

Faculty of Sciences and Technology, LE²P-Energy Lab, University of La Réunion, La Réunion 97715, France
Interests: solar radiation mapping and prediction; fractal theory; climate change; tropical meteorology; atmospheric physics

Special Issue Information

Dear Colleagues,

Renewable energy is energy that is collected from carbon-free resources, which are naturally provided on a human time scale, such as solar radiation, wind, rain, tides, biomass, waves and geothermal heat. This energy often provides energy in four important areas: Electricity generation, air and water heating/cooling, transportation and energy for off-grid services, which can be either stand-alone power systems or mini-grids typically supplying a smaller community or small islands with electricity. In the past few decades, the renewable energy has become one of the attractors for policy makers and for the worldwide scientific community, both on a theoretical and practical level.

The work on Renewable Energy includes three equally-important parts: (i) reliability of the measuring procedure, (ii) analysis of measured time series often carrying hidden physical information that cannot be established by traditional methods from different mathematical fields and (iii) predictability of the behavior of those time series, which are essentially connected. In this Special Issue in the focus will be research that addresses Renewable Energy problems using Information Theory approaches, by introducing a novel development of Information Theory for specific applications, and/or by solving a new Renewable Energy problem using the tools of Information Theory. Submissions at the edge of Information Theory, Renewable Energy, and other disciplines are also welcome.

Prof. Dr. Dragutin T. Mihailović
Prof. Dr. Miloud Bessafi
Guest Editors

Manuscript Submission Information

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Keywords

renewable energy
physics
complex systems
predictability
Shannon entropy
Kolmogorov complexity measures
Kolmogorov time
information theory
nonlinearity

Published Papers (4 papers)

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Research

21 pages, 10071 KiB

Open AccessArticle

Algorithmic Probability Method Versus Kolmogorov Complexity with No-Threshold Encoding Scheme for Short Time Series: An Analysis of Day-To-Day Hourly Solar Radiation Time Series over Tropical Western Indian Ocean

by Miloud Bessafi, Dragutin T. Mihailović, Peng Li, Anja Mihailović and Jean-Pierre Chabriat

Entropy 2019, 21(6), 552; https://doi.org/10.3390/e21060552 - 31 May 2019

Cited by 3 | Viewed by 2917

Abstract

The complexity of solar radiation fluctuations received on the ground is nowadays of great interest for solar resource in the context of climate change and sustainable development. Over tropical maritime area, there are small inhabited islands for which the prediction of the solar resource at the daily and infra-daily time scales are important to optimize their solar energy systems. Recently, studies show that the theory of the information is a promising way to measure the solar radiation intermittency. Kolmogorov complexity (KC) is a useful tool to address the question of predictability. Nevertheless, this method is inaccurate for small time series size. To overcome this drawback, a new encoding scheme is suggested for converting hourly solar radiation time series values into a binary string for calculation of Kolmogorov complexity (KC-ES). To assess this new approach, we tested this method using the 2004–2006 satellite hourly solar data for the western part of the Indian Ocean. The results were compared with the algorithmic probability (AP) method which is used as the benchmark method to compute the complexity for short string. These two methods are a new approach to compute the complexity of short solar radiation time series. We show that KC-ES and AP methods give comparable results which are in agreement with the physical variability of solar radiation. During the 2004–2006 period, an important interannual SST (sea surface temperature) anomaly over the south of Mozambique Channel encounters in 2005, a strong MJO (Madden–Julian oscillation) took place in May 2005 over the equatorial Indian Ocean, and nine tropical cyclones crossed the western part of the Indian Ocean in 2004–2005 and 2005–2006 austral summer. We have computed KC-ES of the solar radiation time series for these three events. The results show that the Kolmogorov complexity with suggested encoding scheme (KC-ES) gives competitive measure of complexity in regard to the AP method also known as Solomonoff probability. Full article

(This article belongs to the Special Issue Application of Information Measures for Analysis and Predictability of Renewable Energy Time Series)

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18 pages, 3018 KiB

Open AccessArticle

The Choice of an Appropriate Information Dissimilarity Measure for Hierarchical Clustering of River Streamflow Time Series, Based on Calculated Lyapunov Exponent and Kolmogorov Measures

by Dragutin T. Mihailović, Emilija Nikolić-Đorić, Slavica Malinović-Milićević, Vijay P. Singh, Anja Mihailović, Tatijana Stošić, Borko Stošić and Nusret Drešković

Entropy 2019, 21(2), 215; https://doi.org/10.3390/e21020215 - 23 Feb 2019

Cited by 11 | Viewed by 3923

Abstract

The purpose of this paper was to choose an appropriate information dissimilarity measure for hierarchical clustering of daily streamflow discharge data, from twelve gauging stations on the Brazos River in Texas (USA), for the period 1989–2016. For that purpose, we selected and compared the average-linkage clustering hierarchical algorithm based on the compression-based dissimilarity measure (NCD), permutation distribution dissimilarity measure (PDDM), and Kolmogorov distance (KD). The algorithm was also compared with K-means clustering based on Kolmogorov complexity (KC), the highest value of Kolmogorov complexity spectrum (KCM), and the largest Lyapunov exponent (LLE). Using a dissimilarity matrix based on NCD, PDDM, and KD for daily streamflow, the agglomerative average-linkage hierarchical algorithm was applied. The key findings of this study are that: (i) The KD clustering algorithm is the most suitable among others; (ii) ANOVA analysis shows that there exist highly significant differences between mean values of four clusters, confirming that the choice of the number of clusters was suitably done; and (iii) from the clustering we found that the predictability of streamflow data of the Brazos River given by the Lyapunov time (LT), corrected for randomness by Kolmogorov time (KT) in days, lies in the interval from two to five days. Full article

(This article belongs to the Special Issue Application of Information Measures for Analysis and Predictability of Renewable Energy Time Series)

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18 pages, 12266 KiB

Open AccessArticle

Spatial and Temporal Non-Linear Dynamics Analysis and Predictability of Solar Radiation Time Series for La Reunion Island (France)

by Miloud Bessafi, Dragutin T. Mihailović, Slavica Malinović-Milićević, Anja Mihailović, Guillaume Jumaux, François Bonnardot, Yannick Fanchette and Jean-Pierre Chabriat

Entropy 2018, 20(12), 946; https://doi.org/10.3390/e20120946 - 08 Dec 2018

Cited by 3 | Viewed by 3884

Abstract

Analysis of daily solar irradiation variability and predictability in space and time is important for energy resources planning, development, and management. The natural intermittency of solar irradiation is mainly triggered by atmospheric turbulent conditions, radiative transfer, optical properties of cloud and aerosol, moisture and atmospheric stability, orographic and thermal forcing, which introduce additional complexity into the phenomenological records. To address this question for daily solar irradiation data recorded during the period 2011–2015, at 32 stations measuring solar irradiance on La Reunion French tropical Indian Ocean Island, we use the tools of non-linear dynamics: the intermittency and chaos analysis, the largest Lyapunov exponent, Sample entropy, the Kolmogorov complexity and its derivatives (Kolmogorov complexity spectrum and its highest value), and spatial weighted Kolmogorov complexity combined with Hamming distance to assess complexity and corresponding predictability. Finally, we have clustered the Kolmogorov time (that quantifies the time span beyond which randomness significantly influences predictability) for daily cumulative solar irradiation for all stations. We show that under the record-breaking 2011–2012 La Nina event and preceding a very strong El-Nino 2015–2016 event, the predictability of daily incident solar energy over La Réunion is affected. Full article

(This article belongs to the Special Issue Application of Information Measures for Analysis and Predictability of Renewable Energy Time Series)

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12 pages, 2634 KiB

Open AccessArticle

Computational Simulation of Entropy Generation in a Combustion Chamber Using a Single Burner

by Souad Morsli, Amina Sabeur, Mohammed El Ganaoui and Harry Ramenah

Entropy 2018, 20(12), 922; https://doi.org/10.3390/e20120922 - 03 Dec 2018

Cited by 8 | Viewed by 2900

Abstract

In this study, we examine the behavior of a propane diffusion flame with air in a burner; the computational investigations are achieved for each case employing the Fluent package. The graphs generated illustrate the influence of flow parameters, the effects of the oxygen percentage in the air, and the effects of the equivalence ratio φ on the entropy generation, the temperature gradients, and the Bejan number. The obtained results show that incorporation of hydrogen with propane reduced both temperature and carbon monoxide emission. Full article

(This article belongs to the Special Issue Application of Information Measures for Analysis and Predictability of Renewable Energy Time Series)

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