Hristos Tyralis

Civil Engineer, PhD candidate
montchrister@gmail.com
+30-2107722860

Participation in research projects

Participation as Researcher

  1. Maintenance, upgrading and extension of the Decision Support System for the management of the Athens water resource system

Published work

Publications in scientific journals

  1. A. Tegos, H. Tyralis, D. Koutsoyiannis, and K. H. Hamed, An R function for the estimation of trend signifcance under the scaling hypothesis- application in PET parametric annual time series, Open Water Journal, 4 (1), 66–71, 6, 2017.
  2. H. Tyralis, A. Tegos, A. Delichatsiou, N. Mamassis, and D. Koutsoyiannis, A perpetually interrupted interbasin water transfer as a modern Greek drama: Assessing the Acheloos to Pinios interbasin water transfer in the context of integrated water resources management, Open Water Journal, 4 (1), 113–128, 12, 2017.
  3. H. Tyralis, N. Mamassis, and Y. Photis, Spatial analysis of the electrical energy demand in Greece, Energy Policy, 102 (340-352), doi:10.1016/j.enpol.2016.12.033, March 2017.
  4. H. Tyralis, N. Mamassis, and Y. Photis, Spatial Analysis of Electrical Energy Demand Patterns in Greece: Application of a GIS-based Methodological Framework, Energy Procedia, 97 (262-269), 8 pages, doi:10.1016/j.egypro.2016.10.071, November 2016.
  5. H. Tyralis, and D. Koutsoyiannis, A Bayesian statistical model for deriving the predictive distribution of hydroclimatic variables, Climate Dynamics, 42 (11-12), 2867–2883, doi:10.1007/s00382-013-1804-y, 2014.
  6. H. Tyralis, D. Koutsoyiannis, and S. Kozanis, An algorithm to construct Monte Carlo confidence intervals for an arbitrary function of probability distribution parameters, Computational Statistics, 28 (4), 1501–1527, doi:10.1007/s00180-012-0364-7, 2013.
  7. H. Tyralis, and D. Koutsoyiannis, Simultaneous estimation of the parameters of the Hurst-Kolmogorov stochastic process, Stochastic Environmental Research & Risk Assessment, 25 (1), 21–33, 2011.

Conference publications and presentations with evaluation of abstract

  1. H. Tyralis, P. Dimitriadis, T. Iliopoulou, K. Tzouka, and D. Koutsoyiannis, Dependence of long-term persistence properties of precipitation on spatial and regional characteristics, European Geosciences Union General Assembly 2017, Geophysical Research Abstracts, Vol. 19, Vienna, EGU2017-3711, doi:10.13140/RG.2.2.13252.83840/1, European Geosciences Union, 2017.
  2. G. Papacharalampous, H. Tyralis, and D. Koutsoyiannis, Investigation of the effect of the hyperparameter optimization and the time lag selection in time series forecasting using machine learning algorithms, European Geosciences Union General Assembly 2017, Geophysical Research Abstracts, Vol. 19, Vienna, 19, GU2017-3072-1, European Geosciences Union, 2017.
  3. G. Papacharalampous, H. Tyralis, and D. Koutsoyiannis, Multi-step ahead streamflow forecasting for the operation of hydropower reservoirs, European Geosciences Union General Assembly 2017, Geophysical Research Abstracts, Vol. 19, Vienna, 19, EGU2017-3069, European Geosciences Union, 2017.
  4. G. Papacharalampous, H. Tyralis, and D. Koutsoyiannis, Comparison between stochastic and machine learning methods for hydrological multi-step ahead forecasting: All forecasts are wrong!, European Geosciences Union General Assembly 2017, Geophysical Research Abstracts, Vol. 19, Vienna, 19, GU2017-3068-2, European Geosciences Union, 2017.
  5. V. Daniil, G. Pouliasis, E. Zacharopoulou, E. Demetriou, G. Manou, M. Chalakatevaki, I. Parara, C. Georganta, P. Stamou, S. Karali, E. Hadjimitsis, G. Koudouris, E. Moschos, D. Roussis, K. Papoulakos, A. Koskinas, G. Pollakis, N. Gournari, K. Sakellari, Y. Moustakis, N. Mamassis, A. Efstratiadis, H. Tyralis, P. Dimitriadis, T. Iliopoulou, G. Karakatsanis, K. Tzouka, E. Deligiannis, V. Tsoukala, P. Papanicolaou, and D. Koutsoyiannis, The uncertainty of atmospheric processes in planning a hybrid renewable energy system for a non-connected island, European Geosciences Union General Assembly 2017, Geophysical Research Abstracts, Vol. 19, Vienna, EGU2017-16781-4, doi:10.13140/RG.2.2.29610.62406, European Geosciences Union, 2017.
  6. E. Hadjimitsis, E. Demetriou, K. Sakellari, H. Tyralis, P. Dimitriadis, T. Iliopoulou, and D. Koutsoyiannis, Investigation of the stochastic nature of temperature and humidity for energy management, European Geosciences Union General Assembly 2017, Geophysical Research Abstracts, Vol. 19, Vienna, 19, EGU2017-10164-5, European Geosciences Union, 2017.
  7. E. Moschos, G. Manou, C. Georganta, P. Dimitriadis, T. Iliopoulou, H. Tyralis, D. Koutsoyiannis, and V. Tsoukala, Investigation of the stochastic nature of wave processes for renewable resources management: a pilot application in a remote island in the Aegean sea, European Geosciences Union General Assembly 2017, Geophysical Research Abstracts, Vol. 19, Vienna, EGU2017-10225-3, doi:10.13140/RG.2.2.30226.66245, European Geosciences Union, 2017.
  8. A. Koskinas, E. Zacharopoulou, G. Pouliasis, I. Engonopoulos, K. Mavroyeoryos, E. Deligiannis, G. Karakatsanis, P. Dimitriadis, T. Iliopoulou, D. Koutsoyiannis, and H. Tyralis, Simulation of electricity demand in a remote island for optimal planning of a hybrid renewable energy system, European Geosciences Union General Assembly 2017, Geophysical Research Abstracts, Vol. 19, Vienna, 19, EGU2017-10495-4, doi:10.13140/RG.2.2.10529.81767, European Geosciences Union, 2017.
  9. A. Sotiriadou, A. Petsiou, E. Feloni, P. Kastis, T. Iliopoulou, Y. Markonis, H. Tyralis, P. Dimitriadis, and D. Koutsoyiannis, Stochastic investigation of precipitation process for climatic variability identification, European Geosciences Union General Assembly 2016, Geophysical Research Abstracts, Vol. 18, Vienna, EGU2016-15137-5, doi:10.13140/RG.2.2.28955.46881, European Geosciences Union, 2016.
  10. H. Tyralis, N. Mamassis, and Y. Photis, Spatial analysis of electricity demand patterns in Greece: Application of a GIS-based methodological framework, European Geosciences Union General Assembly 2016, Geophysical Research Abstracts, Vol. 18, Vienna, European Geosciences Union, 2016.
  11. H. Tyralis, G. Karakatsanis, K. Tzouka, and N. Mamassis, Analysis of the electricity demand of Greece for optimal planning of a large-scale hybrid renewable energy system, European Geosciences Union General Assembly 2015, Geophysical Research Abstracts, Vol. 17, Vienna, EGU2015-5643, European Geosciences Union, 2015.
  12. A. Koukouvinos, D. Nikolopoulos, A. Efstratiadis, A. Tegos, E. Rozos, S.M. Papalexiou, P. Dimitriadis, Y. Markonis, P. Kossieris, H. Tyralis, G. Karakatsanis, K. Tzouka, A. Christofides, G. Karavokiros, A. Siskos, N. Mamassis, and D. Koutsoyiannis, Integrated water and renewable energy management: the Acheloos-Peneios region case study, European Geosciences Union General Assembly 2015, Geophysical Research Abstracts, Vol. 17, Vienna, EGU2015-4912, doi:10.13140/RG.2.2.17726.69440, European Geosciences Union, 2015.
  13. A. M. Filippidou, A. Andrianopoulos, C. Argyrakis, L. E. Chomata, V. Dagalaki, X. Grigoris, T. S. Kokkoris, M. Nasioka, K. A. Papazoglou, S.M. Papalexiou, H. Tyralis, and D. Koutsoyiannis, Comparison of climate time series produced by General Circulation Models and by observed data on a global scale, European Geosciences Union General Assembly 2014, Geophysical Research Abstracts, Vol. 16, Vienna, EGU2014-8529, doi:10.13140/RG.2.2.33887.87200, European Geosciences Union, 2014.
  14. H. Tyralis, and D. Koutsoyiannis, Simultaneous use of observations and deterministic model outputs to forecast persistent stochastic processes, Facets of Uncertainty: 5th EGU Leonardo Conference – Hydrofractals 2013 – STAHY 2013, Kos Island, Greece, doi:10.13140/RG.2.1.3230.4889, European Geosciences Union, International Association of Hydrological Sciences, International Union of Geodesy and Geophysics, 2013.
  15. H. Tyralis, and D. Koutsoyiannis, A Bayesian approach to hydroclimatic prognosis using the Hurst-Kolmogorov stochastic process, European Geosciences Union General Assembly 2012, Geophysical Research Abstracts, Vol. 14, Vienna, doi:10.13140/RG.2.2.24273.74089, European Geosciences Union, 2012.
  16. P. Kossieris, D. Koutsoyiannis, C. Onof, H. Tyralis, and A. Efstratiadis, HyetosR: An R package for temporal stochastic simulation of rainfall at fine time scales, European Geosciences Union General Assembly 2012, Geophysical Research Abstracts, Vol. 14, Vienna, 11718, European Geosciences Union, 2012.
  17. D. Koutsoyiannis, S. Kozanis, and H. Tyralis, A general Monte Carlo method for the construction of confidence intervals for a function of probability distribution parameters, European Geosciences Union General Assembly 2011, Geophysical Research Abstracts, Vol. 13, Vienna, EGU2011-1489, doi:10.13140/RG.2.2.33147.31527, European Geosciences Union, 2011.
  18. H. Tyralis, and D. Koutsoyiannis, Performance evaluation and interdependence of parameter estimators of the Hurst-Kolmogorov stochastic process, European Geosciences Union General Assembly 2010, Geophysical Research Abstracts, Vol. 12, Vienna, EGU2010-10476, doi:10.13140/RG.2.2.27118.00322, European Geosciences Union, 2010.

Various publications

  1. H. Tyralis, Integrated management of surface water resources of Acheloos and Peneios river basins, May 2015.
  2. H. Tyralis, An introduction to R programming language, 39 pages, November 2011.
  3. H. Tyralis, A brief introduction to Bayesian statistics, 29 pages, November 2011.

Academic works

  1. H. Tyralis, Spatial and temporal analysis of electricity demandin Greece, MSc thesis, 95 pages, July 2016.
  2. H. Tyralis, Use of Bayesian techniques in hydroclimatic prognosis, PhD thesis, 166 pages, Department of Water Resources and Environmental Engineering – National Technical University of Athens, November 2015.

Research reports

  1. A. Koukouvinos, A. Efstratiadis, D. Nikolopoulos, H. Tyralis, A. Tegos, N. Mamassis, and D. Koutsoyiannis, Case study in the Acheloos-Thessaly system, Combined REnewable Systems for Sustainable ENergy DevelOpment (CRESSENDO), 98 pages, Department of Water Resources and Environmental Engineering – National Technical University of Athens, October 2015.
  2. A. Efstratiadis, N. Mamassis, Y. Markonis, P. Kossieris, and H. Tyralis, Methodological framework for optimal planning and management of water and renewable energy resources, Combined REnewable Systems for Sustainable ENergy DevelOpment (CRESSENDO), 154 pages, Department of Water Resources and Environmental Engineering – National Technical University of Athens, April 2015.

Miscellaneous works

  1. H. Tyralis, and A. Efstratiadis, "National Programme for the Management and Protection of Water Resources" and "Impacts of climate change to surface and groundwater resources of Greece": Comparative presentation, September 2012.
  2. H. Tyralis, A brief introduction to Bayesian statistics, 24 pages, Department of Water Resources and Environmental Engineering – National Technical University of Athens, April 2011.

Details on research projects

Participation as Researcher

  1. Maintenance, upgrading and extension of the Decision Support System for the management of the Athens water resource system

    Duration: October 2008–November 2011

    Budget: €72 000

    Project director: N. Mamassis

    Principal investigator: D. Koutsoyiannis

    This research project includes the maintenance, upgrading and extension of the Decision Support System that developed by NTUA for EYDAP in the framework of the research project “Updating of the supervision and management of the water resources’ system for the water supply of the Athens’ metropolitan area”. The project is consisted of the following parts: (a) Upgrading of the Data Base, (b)Upgrading and extension of hydrometeorological network, (c) upgrading of the hydrometeorological data process software, (d) upgrading and extension of the Hydronomeas software, (e) hydrological data analysis and (f) support to the preparation of the annual master plans

Published work in detail

Publications in scientific journals

  1. A. Tegos, H. Tyralis, D. Koutsoyiannis, and K. H. Hamed, An R function for the estimation of trend signifcance under the scaling hypothesis- application in PET parametric annual time series, Open Water Journal, 4 (1), 66–71, 6, 2017.

    We present an R function for testing the significant trend of time series. Te function calculates trend significance using a modified Mann-Kendall test, which takes into account the well-known physical behavior of the Hurst-Kolmogorov dynamics. Te function is tested at 10 stations in Greece, with approximately 50 years of PET data with the use of a recent parametric approach. A significant downward trend was detected at two stations. Te R software is now suitable for extensive use in several fields of the scientific community, allowing a physical consistent of a trend analysis.

    Full text: http://www.itia.ntua.gr/en/getfile/1703/1/documents/2017OW_An_R_FunctionForTrendSignificance.pdf (326 KB)

    Additional material:

    See also: http://scholarsarchive.byu.edu/openwater/vol4/iss1/6/

  1. H. Tyralis, A. Tegos, A. Delichatsiou, N. Mamassis, and D. Koutsoyiannis, A perpetually interrupted interbasin water transfer as a modern Greek drama: Assessing the Acheloos to Pinios interbasin water transfer in the context of integrated water resources management, Open Water Journal, 4 (1), 113–128, 12, 2017.

    Interbasin water transfer is a primary instrument of water resources management directly related with the integrated development of the economy, society and environment. Here we assess the project of the interbasin water transfer from the river Acheloos to the river Pinios basin which has intrigued the Greek society, the politicians and scientists for decades. Te set of criteria we apply originate from a previous study reviewing four interbasin water transfers and assessing whether an interbasin water transfer is compatible with the concept of integrated water resources management. In this respect, we assess which of the principles of the integrated water resources management the Acheloos to Pinios interbasin water transfer project does or does not satisfy. While the project meets the criteria of real surplus and deficit, of sustainability and of sound science, i.e., the criteria mostly related to the engineering part, it fails to meet the criteria of good governance and balancing of existing rights with needs, i.e., the criteria associated with social aspects of the project. Te non-fulfillment of the latter criteria is the consequence of chronic diseases of the Greek society, which become obvious in the case study

    Full text: http://www.itia.ntua.gr/en/getfile/1702/1/documents/2017OW_AcheloosToPiniosInterbasinWaterTransfer.pdf (2744 KB)

    See also: http://scholarsarchive.byu.edu/openwater/vol4/iss1/11/

  1. H. Tyralis, N. Mamassis, and Y. Photis, Spatial analysis of the electrical energy demand in Greece, Energy Policy, 102 (340-352), doi:10.1016/j.enpol.2016.12.033, March 2017.

    The Electrical Energy Demand (EED) of the agricultural, commercial and industrial sector in Greece, as well as its use for domestic activities, public and municipal authorities and street lighting are analysed spatially using Geographical Information System and spatial statistical methods. The analysis is performed on data which span from 2008 to 2012 and have annual temporal resolution and spatial resolution down to the NUTS (Nomenclature of Territorial Units for Statistics) level 3. The aim is to identify spatial patterns of the EED and its transformations such as the ratios of the EED to socioeconomic variables, i.e. the population, the total area, the population density and the Gross Domestic Product (GDP). Based on the analysis, Greece is divided in five regions, each one with a different development model, i.e. Attica and Thessaloniki which are two heavily populated major poles, Thessaly and Central Greece which form a connected geographical region with important agricultural and industrial sector, the islands and some coastal areas which are characterized by an important commercial sector and the rest Greek areas. The spatial patterns can provide additional information for policy decision about the electrical energy management and better representation of the regional socioeconomic conditions.

    Full text: http://www.itia.ntua.gr/en/getfile/1674/1/documents/electr_GIS_R_2016.pdf (2357 KB)

  1. H. Tyralis, N. Mamassis, and Y. Photis, Spatial Analysis of Electrical Energy Demand Patterns in Greece: Application of a GIS-based Methodological Framework, Energy Procedia, 97 (262-269), 8 pages, doi:10.1016/j.egypro.2016.10.071, November 2016.

    We investigate various uses of the Electrical Energy Demand (EED) in Greece (agricultural, commercial, domestic, industrial use) and we examine their relationships with variables such as population and the Gross Domestic Product. The analysis is performed on data from the year 2012 and have spatial resolution down to the level of prefecture. We both visualize the results of the analysis and we perform spatial cluster and outlier analysis. The definition of the spatial patterns of the aforementioned variables in a GIS environment provides insight of the regional development model in Greece.

    Full text: http://www.itia.ntua.gr/en/getfile/1672/1/documents/SpatialAnalyis.pdf (1019 KB)

  1. H. Tyralis, and D. Koutsoyiannis, A Bayesian statistical model for deriving the predictive distribution of hydroclimatic variables, Climate Dynamics, 42 (11-12), 2867–2883, doi:10.1007/s00382-013-1804-y, 2014.

    Recent publications have provided evidence that hydrological processes exhibit a scaling behaviour, also known as the Hurst phenomenon. An appropriate way to model this behaviour is to use the Hurst-Kolmogorov stochastic process. The Hurst-Kolmogorov process entails high autocorrelations even for large lags, as well as high variability even at climatic scales. A problem that, thus, arises is how to incorporate the observed past hydroclimatic data in deriving the predictive distribution of hydroclimatic processes at climatic time scales. Here with the use of Bayesian techniques we create a framework to solve the aforementioned problem. We assume that there is no prior information for the parameters of the process and use a non-informative prior distribution. We apply this method with real-world data to derive the posterior distribution of the parameters and the posterior predictive distribution of various 30-year moving average climatic variables. The marginal distributions we examine are the normal and the truncated normal (for nonnegative variables). We also compare the results with two alternative models, one that assumes independence in time and one with Markovian dependence, and the results are dramatically different. The conclusion is that this framework is appropriate for the prediction of future hydroclimatic variables conditional on the observations.

    Additional material:

    See also: http://dx.doi.org/10.1007/s00382-013-1804-y

  1. H. Tyralis, D. Koutsoyiannis, and S. Kozanis, An algorithm to construct Monte Carlo confidence intervals for an arbitrary function of probability distribution parameters, Computational Statistics, 28 (4), 1501–1527, doi:10.1007/s00180-012-0364-7, 2013.

    We derive a new algorithm for calculating an exact confidence interval for a parameter of location or scale family, based on a two-sided hypothesis test on the parameter of interest, using some pivotal quantities. We use this algorithm to calculate approximate confidence intervals for the parameter or a function of the parameter of one-parameter continuous distributions. After appropriate heuristic modifications of the algorithm we use it to obtain approximate confidence intervals for a parameter or a function of parameters for multi-parameter continuous distributions. The advantage of the algorithm is that it is general and gives a fast approximation of an exact confidence interval. Some asymptotic (analytical) results are shown which validate the use of the method under certain regularity conditions. In addition, numerical results of the method compare well with those obtained by other known methods of the literature on the exponential, the normal, the gamma and the Weibull distribution.

    Additional material:

    See also: http://dx.doi.org/10.1007/s00180-012-0364-7

    Works that cite this document: View on Google Scholar or ResearchGate

    Other works that reference this work (this list might be obsolete):

    1. Campos, J. N.B., F. A. Souza Filho and H. V.C. Lima, Risks and uncertainties in reservoir yield in highly variable intermittent rivers: Case of the Castanhão Reservoir in semi-arid Brazil, Hydrological Sciences Journal, 59 (6), 1184-1195, 2014.

  1. H. Tyralis, and D. Koutsoyiannis, Simultaneous estimation of the parameters of the Hurst-Kolmogorov stochastic process, Stochastic Environmental Research & Risk Assessment, 25 (1), 21–33, 2011.

    Various methods for estimating the self-similarity parameter (Hurst parameter, H) of a Hurst-Kolmogorov stochastic process (HKp) from a time series are available. Most of them rely on some asymptotic properties of processes with Hurst-Kolmogorov behaviour and only estimate the self-similarity parameter. Here we show that the estimation of the Hurst parameter affects the estimation of the standard deviation, a fact that was not given appropriate attention in the literature. We propose the Least Squares based on Variance estimator, and we investigate numerically its performance, which we compare to the Least Squares based on Standard Deviation estimator, as well as the maximum likelihood estimator after appropriate streamlining of the latter. These three estimators rely on the structure of the HKp and estimate simultaneously its Hurst parameter and standard deviation. In addition, we test the performance of the three methods for a range of sample sizes and H values, through a simulation study and we compare it with other estimators of the literature.

    Additional material:

    See also: http://dx.doi.org/10.1007/s00477-010-0408-x

    Other works that reference this work (this list might be obsolete):

    1. Bakker, A. M. R., and B. J. J. M. van den Hurk, Estimation of persistence and trends in geostrophic wind speed for the assessment of wind energy yields in Northwest Europe, Climate Dynamics, 39 (3-4), 767-782, 2012.
    2. Prass, T. S., J. M. Bravo, R. T. Clarke, W. Collischonn, and S. R. C. Lopes, Comparison of forecasts of mean monthly water level in the Paraguay River, Brazil, from two fractionally differenced models, Water Resour. Res., 48, W05502, doi: 10.1029/2011WR011358, 2012.
    3. Bakker, A., J. Coelingh and B. van den Hurk, Long-term trends in the wind supply in the Netherlands, Proceedings EWEA 2012 Annual Event, Copenhagen, Denmark, 2012.
    4. Navarro, X., F. Porée, A. Beuchée and G. Carrault, Performance analysis of Hurst exponent estimators using surrogate-data and fractional lognormal noise models: Application to breathing signals from preterm infants, Digital Signal Processing, 10.1016/j.dsp.2013.04.007, 2013.
    5. Serinaldi, F., L. Zunino and O. Rosso, Complexity–entropy analysis of daily stream flow time series in the continental United States, Stochastic Environmental Research and Risk Assessment, 28 (7), 1685-1708, 2014.
    6. Szolgayova, E., G. Laaha, G. Blöschl and C. Bucher, Factors influencing long range dependence in streamflow of European rivers, Hydrological Processes, 28 (4), 1573-1586, 2014.
    7. Serinaldi, F., and C.G. Kilsby, The importance of prewhitening in change point analysis under persistence, Stochastic Environmental Research and Risk Assessment, 10.1007/s00477-015-1041-5, 2015.

Conference publications and presentations with evaluation of abstract

  1. H. Tyralis, P. Dimitriadis, T. Iliopoulou, K. Tzouka, and D. Koutsoyiannis, Dependence of long-term persistence properties of precipitation on spatial and regional characteristics, European Geosciences Union General Assembly 2017, Geophysical Research Abstracts, Vol. 19, Vienna, EGU2017-3711, doi:10.13140/RG.2.2.13252.83840/1, European Geosciences Union, 2017.

    The long-term persistence (LTP), else known in hydrological science as the Hurst phenomenon, is a behaviour observed in geophysical processes in which wet years or dry years are clustered to respective long time periods. A common practice for evaluating the presence of the LTP is to model the geophysical time series with the Hurst-Kolmogorov process (HKp) and estimate its Hurst parameter H where high values of H indicate strong LTP. We estimate H of the mean annual precipitation using instrumental data from approximately 1 500 stations which cover a big area of the earth’s surface and span from 1916 to 2015. We regress the H estimates of all stations on their spatial and regional characteristics (i.e. their location, elevation and Köppen-Geiger climate class) using a random forest algorithm. Furthermore, we apply the Mann-Kendall test under the LTP assumption (MKt-LTP) to all time series to assess the significance of observed trends of the mean annual precipitation. To summarize the results, the LTP seems to depend mostly on the location of the stations, while the predictive value of the fitted regression model is good. Thus when investigating for LTP properties we recommend that the local characteristics should be considered. Additionally, the application of the MKt-LTP suggests that no significant monotonic trend can characterize the global precipitation. Dominant positive significant trends are observed mostly in main climate type D (snow), while in the other climate types the percentage of stations with positive significant trends was approximately equal to that of negative significant trends. Furthermore, 50% of all stations do not exhibit significant trends at all.

    Full text: http://www.itia.ntua.gr/en/getfile/1695/1/documents/EGU2017-3711presentation_.pdf (1608 KB)

    Additional material:

  1. G. Papacharalampous, H. Tyralis, and D. Koutsoyiannis, Investigation of the effect of the hyperparameter optimization and the time lag selection in time series forecasting using machine learning algorithms, European Geosciences Union General Assembly 2017, Geophysical Research Abstracts, Vol. 19, Vienna, 19, GU2017-3072-1, European Geosciences Union, 2017.

    The hyperparameter optimization and the time lag selection are considered to be of great importance in time series forecasting using machine learning (ML) algorithms. To investigate their effect on the ML forecasting performance we conduct several large-scale simulation experiments. Within each of the latter we compare 12 methods on 2 000 simulated time series from the family of Autoregressive Fractionally Integrated Moving Average (ARFIMA) models. The methods are defined by the set {ML algorithm, hyperparameter selection procedure, time lags}. We compare three ML algorithms, i.e. Neural Networks (NN), Random Forests (RF) and Support Vector Machines (SVM), two procedures for hyperparameter selection i.e. predefined hyperparameters or defined after optimization and two regression matrices (using time lag 1 or 1, …, 21). After splitting each simulated time series into a fitting and a testing set, we fit the models to the former set and compare their performance on the latter one. We quantify the methods’ performance using several metrics proposed in the literature and benchmark methods. Furthermore, we conduct a sensitivity analysis on the length of the fitting set to examine how it affects the robustness of our results. The findings indicate that the hyperparameter optimization mostly has a small effect on the forecasting performance. This is particularly important, because the hyperparameter optimization is computationally intensive. On the other hand, the time lag selection seems to mostly significantly affect the methods performance when using the NN algorithm, while we observe a similar behaviour for the RF algorithm albeit to a smaller extent.

    Additional material:

  1. G. Papacharalampous, H. Tyralis, and D. Koutsoyiannis, Multi-step ahead streamflow forecasting for the operation of hydropower reservoirs, European Geosciences Union General Assembly 2017, Geophysical Research Abstracts, Vol. 19, Vienna, 19, EGU2017-3069, European Geosciences Union, 2017.

    Multi-step ahead forecasting is of practical interest for the operation of hydropower reservoirs.We conduct several large scale simulation experiments using both streamflow data and simulated time series to provide generalized results concerning the variation over time of the error values in multi-step ahead forecasting. In more detail, we apply several popular forecasting methods to each time series as explained subsequently. Each time series is split into a fitting and a testing set. We fit the models to the former set and we test their forecasting performance in the latter set. Lastly, we compute the error and the absolute error at each time step of the forecast horizon for each test and carry out a statistical analysis on the formed data sets. Furthermore, we perform a sensitivity analysis on the length of the fitting set to examine how it affects the results.

    Additional material:

  1. G. Papacharalampous, H. Tyralis, and D. Koutsoyiannis, Comparison between stochastic and machine learning methods for hydrological multi-step ahead forecasting: All forecasts are wrong!, European Geosciences Union General Assembly 2017, Geophysical Research Abstracts, Vol. 19, Vienna, 19, GU2017-3068-2, European Geosciences Union, 2017.

    Machine learning (ML) is considered to be a promising approach to hydrological processes forecasting. We conduct a comparison between several stochastic and ML point estimation methods by performing large-scale computational experiments based on simulations. The purpose is to provide generalized results, while the respective comparisons in the literature are usually based on case studies. The stochastic methods used include simple methods, models from the frequently used families of Autoregressive Moving Average (ARMA), Autoregressive Fractionally Integrated Moving Average (ARFIMA) and Exponential Smoothing models. The ML methods used are Random Forests (RF), Support Vector Machines (SVM) and Neural Networks (NN). The comparison refers to the multi-step ahead forecasting properties of the methods. A total of 20 methods are used, among which 9 are the ML methods. 12 simulation experiments are performed, while each of them uses 2 000 simulated time series of 310 observations. The time series are simulated using stochastic processes from the families of ARMA and ARFIMA models. Each time series is split into a fitting (first 300 observations) and a testing set (last 10 observations). The comparative assessment of the methods is based on 18 metrics, that quantify the methods’ performance according to several criteria related to the accurate forecasting of the testing set, the capturing of its variation and the correlation between the testing and forecasted values. The most important outcome of this study is that there is not a uniformly better or worse method. However, there are methods that are regularly better or worse than others with respect to specific metrics. It appears that, although a general ranking of the methods is not possible, their classification based on their similar or contrasting performance in the various metrics is possible to some extent. Another important conclusion is that more sophisticated methods do not necessarily provide better forecasts compared to simpler methods. It is pointed out that the ML methods do not differ dramatically from the stochastic methods, while it is interesting that the NN, RF and SVM algorithms used in this study offer potentially very good performance in terms of accuracy. It should be noted that, although this study focuses on hydrological processes, the results are of general scientific interest. Another important point in this study is the use of several methods and metrics. Using fewer methods and fewer metrics would have led to a very different overall picture, particularly if those fewer metrics corresponded to fewer criteria. For this reason, we consider that the proposed methodology is appropriate for the evaluation of forecasting methods.

    Additional material:

  1. V. Daniil, G. Pouliasis, E. Zacharopoulou, E. Demetriou, G. Manou, M. Chalakatevaki, I. Parara, C. Georganta, P. Stamou, S. Karali, E. Hadjimitsis, G. Koudouris, E. Moschos, D. Roussis, K. Papoulakos, A. Koskinas, G. Pollakis, N. Gournari, K. Sakellari, Y. Moustakis, N. Mamassis, A. Efstratiadis, H. Tyralis, P. Dimitriadis, T. Iliopoulou, G. Karakatsanis, K. Tzouka, E. Deligiannis, V. Tsoukala, P. Papanicolaou, and D. Koutsoyiannis, The uncertainty of atmospheric processes in planning a hybrid renewable energy system for a non-connected island, European Geosciences Union General Assembly 2017, Geophysical Research Abstracts, Vol. 19, Vienna, EGU2017-16781-4, doi:10.13140/RG.2.2.29610.62406, European Geosciences Union, 2017.

    Non-connected islands to the electric gird are often depending on oil-fueled power plants with high unit cost. A hybrid energy system with renewable resources such as wind and solar plants could reduce this cost and also offer more environmental friendly solutions. However, atmospheric processes are characterized by high uncertainty that does not permit harvesting and utilizing full of their potential. Therefore, a more sophisticated framework that somehow incorporates this uncertainty could improve the performance of the system. In this context, we describe several stochastic and financial aspects of this framework. Particularly, we investigate the cross-correlation between several atmospheric processes and the energy demand, the possibility of mixing renewable resources with the conventional ones and in what degree of reliability, and critical financial subsystems such as weather derivatives. A pilot application of the above framework is also presented for a remote island in the Aegean Sea.

    Full text: http://www.itia.ntua.gr/en/getfile/1689/1/documents/EGU2017oral_16781_final.pdf (3038 KB)

    Additional material:

  1. E. Hadjimitsis, E. Demetriou, K. Sakellari, H. Tyralis, P. Dimitriadis, T. Iliopoulou, and D. Koutsoyiannis, Investigation of the stochastic nature of temperature and humidity for energy management, European Geosciences Union General Assembly 2017, Geophysical Research Abstracts, Vol. 19, Vienna, 19, EGU2017-10164-5, European Geosciences Union, 2017.

    Atmospheric temperature and dew point, in addition to their role in atmospheric processes, influence the management of energy systems since they highly affect the energy demand and production. Both temperature and humidity depend on the climate conditions and geographical location. In this context, we analyze numerous of observations around the globe and we investigate the long-term behaviour and periodicities of the temperature and humidity processes. Also, we present and apply a parsimonious stochastic double-cyclostationary model for these processes to an island in the Aegean Sea and investigate their link to energy management.

    Additional material:

  1. E. Moschos, G. Manou, C. Georganta, P. Dimitriadis, T. Iliopoulou, H. Tyralis, D. Koutsoyiannis, and V. Tsoukala, Investigation of the stochastic nature of wave processes for renewable resources management: a pilot application in a remote island in the Aegean sea, European Geosciences Union General Assembly 2017, Geophysical Research Abstracts, Vol. 19, Vienna, EGU2017-10225-3, doi:10.13140/RG.2.2.30226.66245, European Geosciences Union, 2017.

    The large energy potential of ocean dynamics is not yet being efficiently harvested mostly due to several technological and financial drawbacks. Nevertheless, modern renewable energy systems include wave and tidal energy in cases of nearshore locations. Although the variability of tidal waves can be adequately predictable, wind-generated waves entail a much larger uncertainty due to their dependence to the wind process. Recent research has shown, through estimation of the wave energy potential in coastal areas of the Aegean Sea, that installation of wave energy converters in nearshore locations could be an applicable scenario, assisting the electrical network of Greek islands. In this context, we analyze numerous of observations and we investigate the long-term behaviour of wave height and wave period processes. Additionally, we examine the case of a remote island in the Aegean sea, by estimating the local wave climate through past analysis data and numerical methods, and subsequently applying a parsimonious stochastic model to a theoretical scenario of wave energy production.

    Full text: http://www.itia.ntua.gr/en/getfile/1685/1/documents/EGU2017-10225-3_poster.pdf (3588 KB)

    Additional material:

  1. A. Koskinas, E. Zacharopoulou, G. Pouliasis, I. Engonopoulos, K. Mavroyeoryos, E. Deligiannis, G. Karakatsanis, P. Dimitriadis, T. Iliopoulou, D. Koutsoyiannis, and H. Tyralis, Simulation of electricity demand in a remote island for optimal planning of a hybrid renewable energy system, European Geosciences Union General Assembly 2017, Geophysical Research Abstracts, Vol. 19, Vienna, 19, EGU2017-10495-4, doi:10.13140/RG.2.2.10529.81767, European Geosciences Union, 2017.

    We simulate the electrical energy demand in the remote island of Astypalaia. To this end we first obtain information regarding the local socioeconomic conditions and energy demand. Secondly, the available hourly demand data are analysed at various time scales (hourly, weekly, daily, seasonal). The cross-correlations between the electrical energy demand and the mean daily temperature as well as other climatic variables for the same time period are computed. Also, we investigate the cross-correlation between those climatic variables and other variables related to renewable energy resources from numerous observations around the globe in order to assess the impact of each one to a hybrid renewable energy system. An exploratory data analysis including all variables is performed with the purpose to find hidden relationships. Finally, the demand is simulated considering all the periodicities found in the analysis. The simulation time series will be used in the development of a framework for planning of a hybrid renewable energy system in Astypalaia.

    Full text: http://www.itia.ntua.gr/en/getfile/1684/2/documents/EGU2017_CrossCorr-EnergyDemand.pdf (2668 KB)

    Additional material:

  1. A. Sotiriadou, A. Petsiou, E. Feloni, P. Kastis, T. Iliopoulou, Y. Markonis, H. Tyralis, P. Dimitriadis, and D. Koutsoyiannis, Stochastic investigation of precipitation process for climatic variability identification, European Geosciences Union General Assembly 2016, Geophysical Research Abstracts, Vol. 18, Vienna, EGU2016-15137-5, doi:10.13140/RG.2.2.28955.46881, European Geosciences Union, 2016.

    The precipitation process is important not only to hydrometeorology but also to renewable energy resources management. We use a dataset consisting of daily and hourly records around the globe to identify statistical variability with emphasis on the last period. Specifically, we investigate the occurrence of mean, maximum and minimum values and we estimate statistical properties such as marginal probability distribution function and the type of decay of the climacogram (i.e. mean process variance vs. scale).

    Acknowledgement: This research is conducted within the frame of the undergraduate course "Stochastic Methods in Water Resources" of the National Technical University of Athens (NTUA). The School of Civil Engineering of NTUA provided moral support for the participation of the students in the Assembly.

    Full text: http://www.itia.ntua.gr/en/getfile/1658/1/documents/RainP.pdf (3820 KB)

    Additional material:

  1. H. Tyralis, N. Mamassis, and Y. Photis, Spatial analysis of electricity demand patterns in Greece: Application of a GIS-based methodological framework, European Geosciences Union General Assembly 2016, Geophysical Research Abstracts, Vol. 18, Vienna, European Geosciences Union, 2016.

    We investigate various uses of electricity demand in Greece (agricultural, commercial, domestic, industrial use as well as use for public and municipal authorities and street lighting) and we examine their relation with variables such as population, total area, population density and the Gross Domestic Product. The analysis is performed on data which span from 2008 to 2012 and have annual temporal resolution and spatial resolution down to the level of prefecture. We both visualize the results of the analysis and we perform cluster and outlier analysis using the Anselin local Moran's I statistic as well as hot spot analysis using the Getis-Ord Gi* statistic. The definition of the spatial patterns and relationships of the aforementioned variables in a GIS environment provides meaningful insight and better understanding of the regional development model in Greece and justifies the basis for an energy demand forecasting methodology.

    Full text:

  1. H. Tyralis, G. Karakatsanis, K. Tzouka, and N. Mamassis, Analysis of the electricity demand of Greece for optimal planning of a large-scale hybrid renewable energy system, European Geosciences Union General Assembly 2015, Geophysical Research Abstracts, Vol. 17, Vienna, EGU2015-5643, European Geosciences Union, 2015.

    The Greek electricity system is examined for the period 2002-2014. The demand load data are analysed at various time scales (hourly, daily, seasonal and annual) and they are related to the mean daily temperature and the gross domestic product (GDP) of Greece for the same time period. The prediction of energy demand, a product of the Greek Independent Power Transmission Operator, is also compared with the demand load. Interesting results about the change of the electricity demand scheme after the year 2010 are derived. This change is related to the decrease of the GDP, during the period 2010-2014. The results of the analysis will be used in the development of an energy forecasting system which will be a part of a framework for optimal planning of a large-scale hybrid renewable energy system in which hydropower plays the dominant role.

    Full text:

  1. A. Koukouvinos, D. Nikolopoulos, A. Efstratiadis, A. Tegos, E. Rozos, S.M. Papalexiou, P. Dimitriadis, Y. Markonis, P. Kossieris, H. Tyralis, G. Karakatsanis, K. Tzouka, A. Christofides, G. Karavokiros, A. Siskos, N. Mamassis, and D. Koutsoyiannis, Integrated water and renewable energy management: the Acheloos-Peneios region case study, European Geosciences Union General Assembly 2015, Geophysical Research Abstracts, Vol. 17, Vienna, EGU2015-4912, doi:10.13140/RG.2.2.17726.69440, European Geosciences Union, 2015.

    Within the ongoing research project “Combined Renewable Systems for Sustainable Energy Development” (CRESSENDO), we have developed a novel stochastic simulation framework for optimal planning and management of large-scale hybrid renewable energy systems, in which hydropower plays the dominant role. The methodology and associated computer tools are tested in two major adjacent river basins in Greece (Acheloos, Peneios) extending over 15 500 km2 (12% of Greek territory). River Acheloos is characterized by very high runoff and holds ~40% of the installed hydropower capacity of Greece. On the other hand, the Thessaly plain drained by Peneios – a key agricultural region for the national economy – usually suffers from water scarcity and systematic environmental degradation. The two basins are interconnected through diversion projects, existing and planned, thus formulating a unique large-scale hydrosystem whose future has been the subject of a great controversy. The study area is viewed as a hypothetically closed, energy-autonomous, system, in order to evaluate the perspectives for sustainable development of its water and energy resources. In this context we seek an efficient configuration of the necessary hydraulic and renewable energy projects through integrated modelling of the water and energy balance. We investigate several scenarios of energy demand for domestic, industrial and agricultural use, assuming that part of the demand is fulfilled via wind and solar energy, while the excess or deficit of energy is regulated through large hydroelectric works that are equipped with pumping storage facilities. The overall goal is to examine under which conditions a fully renewable energy system can be technically and economically viable for such large spatial scale.

    Full text:

    Additional material:

    See also: http://dx.doi.org/10.13140/RG.2.2.17726.69440

  1. A. M. Filippidou, A. Andrianopoulos, C. Argyrakis, L. E. Chomata, V. Dagalaki, X. Grigoris, T. S. Kokkoris, M. Nasioka, K. A. Papazoglou, S.M. Papalexiou, H. Tyralis, and D. Koutsoyiannis, Comparison of climate time series produced by General Circulation Models and by observed data on a global scale, European Geosciences Union General Assembly 2014, Geophysical Research Abstracts, Vol. 16, Vienna, EGU2014-8529, doi:10.13140/RG.2.2.33887.87200, European Geosciences Union, 2014.

    Outputs of General Circulation Models (GCMs) for precipitation are compared with time series produced from observations. Comparison is made on global and hemispheric spatial scale and on annual time scale. Various time periods are examined, distinguishing periods before and after publishing of model outputs. Historical climate time series are compared with the outputs of GCMs for the 20th century and those for the A1B, B1 and A2 emission scenarios for the 21st century. Several indices are examined, i.e. the estimated means, variances, Hurst parameters, cross-correlations etc.

    Acknowledgement: This research is conducted within the frame of the undergraduate course "Stochastic Methods in Water Resources" of the National Technical University of Athens (NTUA). The School of Civil Engineering of NTUA provided moral support for the participation of the students in the Assembly.

    Full text:

    Additional material:

    See also: http://dx.doi.org/10.13140/RG.2.2.33887.87200

  1. H. Tyralis, and D. Koutsoyiannis, Simultaneous use of observations and deterministic model outputs to forecast persistent stochastic processes, Facets of Uncertainty: 5th EGU Leonardo Conference – Hydrofractals 2013 – STAHY 2013, Kos Island, Greece, doi:10.13140/RG.2.1.3230.4889, European Geosciences Union, International Association of Hydrological Sciences, International Union of Geodesy and Geophysics, 2013.

    We combine a time series of a geophysical process with the output of a deterministic model, which simulates the aforementioned process in the past also providing future predictions. The purpose is to convert the single prediction of the deterministic model for the future evolution of the time series into a stochastic prediction. The time series is modelled by a stationary persistent normal stochastic process. The output of the deterministic model comprises the simulation of the historical part of the process and its deterministic future prediction. The complexity of the deterministic model is assumed to be irrelevant to our framework. A multivariate stochastic process, whose first variable is the true (observable) process and the second variable is a process representing the deterministic model, is formed. The covariance matrix function is computed and the distribution of the unobserved part of the stochastic process is calculated conditional on the observations and the output of the deterministic model.

    Full text:

    See also: http://dx.doi.org/10.13140/RG.2.1.3230.4889

  1. H. Tyralis, and D. Koutsoyiannis, A Bayesian approach to hydroclimatic prognosis using the Hurst-Kolmogorov stochastic process, European Geosciences Union General Assembly 2012, Geophysical Research Abstracts, Vol. 14, Vienna, doi:10.13140/RG.2.2.24273.74089, European Geosciences Union, 2012.

    It has now been well recognized that hydrological processes exhibit a scaling behaviour, also known as the Hurst phenomenon. An appropriate way to model this behaviour is to use the Hurst-Kolmogorov stochastic process. This process is associated with large scale fluctuations and also enhanced uncertainty in the parameter estimation. When we have to make a prognosis for the future evolution of the process, the total uncertainty must be evaluated. The proper technique to do this is provided by Bayesian methods. We develop a Bayesian framework with Monte Carlo implementation for the uncertainty estimation of future prognoses assuming a Hurst-Kolmogorov stochastic process with a non-informative prior distribution of parameters. We derive the posterior distribution of the parameters and use it to make inference for future hydroclimatic variables.

    Full text:

    See also: http://dx.doi.org/10.13140/RG.2.2.24273.74089

  1. P. Kossieris, D. Koutsoyiannis, C. Onof, H. Tyralis, and A. Efstratiadis, HyetosR: An R package for temporal stochastic simulation of rainfall at fine time scales, European Geosciences Union General Assembly 2012, Geophysical Research Abstracts, Vol. 14, Vienna, 11718, European Geosciences Union, 2012.

    A complete software package for the temporal stochastic simulation of rainfall process at fine time scales is developed in the R programming environment. This includes several functions for sequential simulation or disaggregation. Specifically, it uses the Bartlett-Lewis rectangular pulses rainfall model for rainfall generation and proven disaggregation techniques which adjust the finer scale (hourly) values in order to obtain the required coarser scale (daily) value, without affecting the stochastic structure implied by the model. Additionally, a repetition scheme is incorporated in order to improve the Bartlett-Lewis model performance without significant increase of computational time. Finally, the package includes an enhanced version of the evolutionary annealing-simplex optimization method for the estimation of Bartlett-Lewis parameters. Multiple calibration criteria are introduced, in order to reproduce the statistical characteristics of rainfall at various time scales. This upgraded version of the original HYETOS program (Koutsoyiannis, D., and Onof C., A computer program for temporal stochastic disaggregation using adjusting procedures, European Geophysical Society, 2000) operates on several modes and combinations thereof (depending on data availability), with many options and graphical capabilities. The package, under the name HyetosR, is available free in the CRAN package repository.

    Remarks:

    Software page: http://itia.ntua.gr/en/softinfo/3/

    Full text:

    Other works that reference this work (this list might be obsolete):

    1. #Montesarchio, V., F. Napolitano, E. Ridolfi and L. Ubertini, A comparison of two rainfall disaggregation models, In Numerical Analysis and Applied Mathematics ICNAAM 2012: International Conference of Numerical Analysis and Applied Mathematics, AIP Conference Proceedings, Vol. 1479, 1796-1799, 2012.
    2. #Villani, V., L. Cattaneo, A. L. Zollo, and P. Mercogliano, Climate data processing with GIS support: Description of bias correction and temporal downscaling tools implemented in Clime software, Euro-Mediterranean Center on Climate Change (RMCC) Research Papers, RP0262, 2015.
    3. Förster, K., F. Hanzer, B. Winter, T. Marke, and U. Strasser, An open-source MEteoroLOgical observation time series DISaggregation Tool (MELODIST v0.1.1), Geoscientific Model Development, 9, 2315-2333, doi:10.5194/gmd-9-2315-2016, 2016.

  1. D. Koutsoyiannis, S. Kozanis, and H. Tyralis, A general Monte Carlo method for the construction of confidence intervals for a function of probability distribution parameters, European Geosciences Union General Assembly 2011, Geophysical Research Abstracts, Vol. 13, Vienna, EGU2011-1489, doi:10.13140/RG.2.2.33147.31527, European Geosciences Union, 2011.

    We derive an algorithm which calculates an exact confidence interval for a distributional parameter of location or scale family, based on a two-sided hypothesis test on the parameter of interest, using some pivotal quantities. We use this algorithm to calculate approximate confidence intervals for the parameter or a function of the parameter of one-parameter distributions. We show that these approximate intervals are asymptotically exact. We modify the algorithm and use it to obtain approximate confidence intervals for a parameter or a function of parameters for multi-parameter distributions. We compare the results of the method with those obtained by known methods of the literature for the normal, the gamma and the Weibull distribution and find them satisfactory. We conclude that the proposed method can yield approximate confidence intervals, based on Monte Carlo simulations, in a generic way, irrespectively of the distribution function, as well as of the type of the parameters or the function of parameters.

    Full text:

    See also: http://dx.doi.org/10.13140/RG.2.2.33147.31527

  1. H. Tyralis, and D. Koutsoyiannis, Performance evaluation and interdependence of parameter estimators of the Hurst-Kolmogorov stochastic process, European Geosciences Union General Assembly 2010, Geophysical Research Abstracts, Vol. 12, Vienna, EGU2010-10476, doi:10.13140/RG.2.2.27118.00322, European Geosciences Union, 2010.

    We investigate three methods for simultaneous estimation of the Hurst parameter (H) and the standard deviation (σ) for a Hurst-Kolmogorov stochastic process, namely the maximum likelihood method and two methods based on the variation of the standard deviation or of the variance with time scale. We show that the simultaneous estimation of the two parameters is important, albeit not given appropriate attention in the literature, because of the interdependence of the two parameter estimators. In addition, we test the performance of the three methods for a range of sample sizes and H values, through a simulation study and we compare it with other known results for other estimators of the literature.

    Full text:

    See also: http://dx.doi.org/10.13140/RG.2.2.27118.00322

Various publications

  1. H. Tyralis, Integrated management of surface water resources of Acheloos and Peneios river basins, May 2015.

    Full text:

  1. H. Tyralis, An introduction to R programming language, 39 pages, November 2011.

    Full text: http://www.itia.ntua.gr/en/getfile/1230/1/documents/TYralisNaresuanRlang2011.pdf (547 KB)

  1. H. Tyralis, A brief introduction to Bayesian statistics, 29 pages, November 2011.

    Full text: http://www.itia.ntua.gr/en/getfile/1229/1/documents/TYralisNaresuan2011.pdf (657 KB)

Academic works

  1. H. Tyralis, Spatial and temporal analysis of electricity demandin Greece, MSc thesis, 95 pages, July 2016.

    In this study we analyse the electricity demand in Greece for the time period 2002-2014 and we simulate the electricity demand in Greece and Thessaly. We visualize the electricity demand in Greece for the time period 2002-2014. We search the relationship between the energy demand and the Gross Domestic Product (GDP) and the temperature. The analysis shows that in general the conclusions of Psiloglou et al. (2009) are still valid. However, since 2010, when the GDP began to decline, the pattern of the energy demand has changed in the winter. Moreover, we investigate the effectiveness of the energy demand forecasting system of the Independent Transmission System Operator (IPTO). The results of the analysis are useful for forecasting the energy demand and for generating synthetic time series. The data and the code of this work are available as supporting material. We analyse the spatial patterns of the energy demand. We present patterns of energy demand for various uses and combinations of energy demand variables for various uses with variables such as the GDP, the population, the area and the population density for the time period 2008-2012. We perform clusters and outliers analysis, hot spot analysis and grouping analysis. The most important results of these analyses are presented in the main body of work, while the total of 1 125 Figures produced during the work, are provided as supporting material. We believe that the results are useful to understand issues related to the spatial distribution of the energy demand in Greece and the developmental orientation of the state, to improve previous works, which use socioeconomic variables. We simulate the total energy demand in Greece and Thessaly, after the removal of the energy demand for agricultural use. Furthermore, we simulate the energy demand for agricultural use in Thessaly. The simulation concerns a long time period. It is performed to assess the long-term properties of an energy project. Therefore, it neglects features such as the dependence on initial conditions. The simulation of energy demand is performed after the removal of the annual averages and the observed periodicities. The synthetic series is produced using a bootstrap method with blocks of random length derived from a geometric distribution. The removed elements are added to the synthetic time series. Summarizing the results of the analysis in the time domain, we observe during the day two local maxima at approximately 12:00 and 20:00, and two local minima at about 04:00 and 16:00. We observe daily maxima of the energy demand on Wednesdays or Thursdays and daily minima on Sundays. The shape of the daily energy demand is almost independent of the month. We observe local maxima of the monthly energy demand in January and July and local minima in October and April. The December monthly energy demand increased significantly after the hydrological year 2011. We observe less variation in energy demand in the period between December and April. A linear model for the relationship between the energy demand and the GDP seems reasonable. Moreover, we observe that the GDP increase results in increased energy demand (or vice versa). Nevertheless, the GDP increase in the hydrological year 2008 was followed by a decrease in energy demand. We observe a global minimum of the energy demand for temperatures at about 17-18° C, and local maxima of temperatures at about 3° C and 32° C. A regression line for the energy demand (the y axis) and temperature (the x axis) would be a convex curve. The IPTO’s daily predictions usually overestimate the energy demand. The results of the investigation will be useful for the construction of a short and a medium-term energy demand forecasting model. Regarding the spatial analysis, we show some Figures from the support material which present significant results. It seems that in Greece’s middle there is an area which is classified as industrial but also agricultural. The Greek islands are mainly characterized as commercial, while the area around Athens is characterized by high values of household energy use. Regarding its development model Greece is divided to the main continental part, which includes areas in the middle and North, to Central Greece and to island regions. The island regions are similar to the Athens area and Peloponnese in specific cases. These results may be useful for the management of the electricity grid and for finding optimal policies for the development model of the state. In regard to the simulation, we did not remove the industrial energy demand use from the data. The industrial use is characterized by different periodicities compared e.g. with the household use. The result of interest was the production of a synthetic time series to simulate a power system and assess its long-term performance. For this specific application it was sufficient to examine only the time series electricity demand. However, if we are interested in forecasting we must take into account the forecasts of temperature, GDP etc. In this case it is necessary to correlate the electricity demand with other variables.

    Full text: http://www.itia.ntua.gr/en/getfile/1626/1/documents/Spatio-temporal_energy_analysis_Greece_Msc_thesis.pdf (7210 KB)

    Additional material:

  1. H. Tyralis, Use of Bayesian techniques in hydroclimatic prognosis, PhD thesis, 166 pages, Department of Water Resources and Environmental Engineering – National Technical University of Athens, November 2015.

    Additional material:

Research reports

  1. A. Koukouvinos, A. Efstratiadis, D. Nikolopoulos, H. Tyralis, A. Tegos, N. Mamassis, and D. Koutsoyiannis, Case study in the Acheloos-Thessaly system, Combined REnewable Systems for Sustainable ENergy DevelOpment (CRESSENDO), 98 pages, Department of Water Resources and Environmental Engineering – National Technical University of Athens, October 2015.

    This report describes the validation of methodologies and computer tools that have been developed in the context of the research project, in the interconnected river basin system of Acheloos and Peneios. The study area is modelled as a hypothetically closed and autonomous (in terms of energy balance) system, in order to investigate the perspectives of sustainable development at the peripheral scale, merely based on renewable energy.

    Related project: Combined REnewable Systems for Sustainable ENergy DevelOpment (CRESSENDO)

    Full text: http://www.itia.ntua.gr/en/getfile/1613/1/documents/Report_EE4a.pdf (8010 KB)

  1. A. Efstratiadis, N. Mamassis, Y. Markonis, P. Kossieris, and H. Tyralis, Methodological framework for optimal planning and management of water and renewable energy resources, Combined REnewable Systems for Sustainable ENergy DevelOpment (CRESSENDO), 154 pages, Department of Water Resources and Environmental Engineering – National Technical University of Athens, April 2015.

    We describe a stochastic simulation and optimization framework for hybrid renewable energy systems, based on effective coupling of different models. Initially, we explain the problem of combined management of water and energy resources, we introduce the main concepts and highlight the peculiarities of the problem, by means of methodology and computational implementation. Next is presented the general context, which is based on the combined use of an hourly simulation model for the renewables of a specific study area (wind and solar units), and a daily simulation model for the water resource system and the associated energy components. The models are fed by synthetic time series of hydrological inflows, wind velocity, solar radiation and electricity demand over the study area, for the generation of which are used appropriate stochastic schemes. The theoretical background of all models and related software systems is based on original methodologies or existing approaches that have been improved or generalized in the context of the research project.

    Related project: Combined REnewable Systems for Sustainable ENergy DevelOpment (CRESSENDO)

    Full text: http://www.itia.ntua.gr/en/getfile/1599/1/documents/Report_EE2.pdf (3766 KB)

Miscellaneous works

  1. H. Tyralis, and A. Efstratiadis, "National Programme for the Management and Protection of Water Resources" and "Impacts of climate change to surface and groundwater resources of Greece": Comparative presentation, September 2012.

    Two documents are compared: (1) the report of the National Programme for the Management and Protection of Water Resources, elaborated by NTUA within a research project, and (2) the report entitled "Impacts of climate change to surface and groundwater resources of Greece", elaborated by a research team of Athens University (EKPA) in June 2011, for the Bank of Greece. A large part (~40%) of the two documents are identical.

    Remarks:

    The report of the National Programme for the Management and Protection of Water Resources: http://itia.ntua.gr/el/docinfo/782/

    Web site of the Bank of Greece which contains, among other things, the report of the Stournaras team: http://www.bankofgreece.gr/Pages/el/klima/relevant.aspx (accessed 2012/09/07)

    Full text: http://www.itia.ntua.gr/en/getfile/1285/1/documents/MasterPlanComparison_3.pdf (8176 KB)

    Additional material:

  1. H. Tyralis, A brief introduction to Bayesian statistics, 24 pages, Department of Water Resources and Environmental Engineering – National Technical University of Athens, April 2011.

    Full text: http://www.itia.ntua.gr/en/getfile/1148/1/documents/2011Tyralis_IntroBayesianStats.pdf (314 KB)