Persistence in complex systems

Persistence is an important characteristic of many complex systems in nature, related to how long the system remains at a certain state before changing to a different one. The study of complex systems' persistence involves different definitions and uses different techniques, depending on whether short-term or long-term persistence is considered. In this paper we discuss the most important definitions, concepts, methods, literature and latest results on persistence in complex systems. Firstly, the most used definitions of persistence in short-term and long-term cases are presented. The most relevant methods to characterize persistence are then discussed in both cases. A complete literature review is also carried out. We also present and discuss some relevant results on persistence, and give empirical evidence of performance in different detailed case studies, for both short-term and long-term persistence. A perspective on the future of persistence concludes the work.This research has been partially supported by the project PID2020-115454GB-C21 of the Spanish Ministry of Science and Innovation (MICINN). This research has also been partially supported by Comunidad de Madrid, PROMINT-CM project (grant ref: P2018/EMT-4366). J. Del Ser would like to thank the Basque Government for its funding support through the EMAITEK and ELKARTEK programs (3KIA project, KK-2020/00049), as well as the consolidated research group MATHMODE (ref. T1294-19). GCV work is supported by the European Research Council (ERC) under the ERC-CoG-2014 SEDAL Consolidator grant (grant agreement 647423)

Persistence in complex systems

Persistence is an important characteristic of many complex systems in nature, related to how long the system remains at a certain state before changing to a different one. The study of complex systems’ persistence involves different definitions and uses different techniques, depending on whether short-term or long-term persistence is considered. In this paper we discuss the most important definitions, concepts, methods, literature and latest results on persistence in complex systems. Firstly, the most used definitions of persistence in short-term and long-term cases are presented. The most relevant methods to characterize persistence are then discussed in both cases. A complete literature review is also carried out. We also present and discuss some relevant results on persistence, and give empirical evidence of performance in different detailed case studies, for both short-term and long-term persistence. A perspective on the future of persistence concludes the work.This research has been partially supported by the project PID2020-115454GB-C21 of the Spanish Ministry of Science and Innovation (MICINN). This research has also been partially supported by Comunidad de Madrid, PROMINT-CM project (grant ref: P2018/EMT-4366). J. Del Ser would like to thank the Basque Government for its funding support through the EMAITEK and ELKARTEK programs (3KIA project, KK-2020/00049), as well as the consolidated research group MATHMODE (ref. T1294-19). GCV work is supported by the European Research Council (ERC) under the ERC-CoG-2014 SEDAL Consolidator grant (grant agreement 647423)

A wavelet-based approach for large wind power ramp characterisation

A wavelet-based approach for large wind power ramp characterisatio

Sonic anemometer and atmospheric flows over complex terrain

Tese de doutoramento. Engenharia Mecânica. Faculdade de Engenharia. Universidade do Porto. 200

Empirical mode decomposition of wind speed signals

Empirical Mode Decomposition (EMD) is a powerful signal processing technique with diverse applications, particularly in the analysis of non-stationary data. In this study, we assess the capabilities of EMD for wind data analysis, aiming to uncover its effectiveness in capturing intricate temporal patterns and decomposing data into Intrinsic Mode Functions (IMFs) to identify crucial frequency components. Various methods of sifting have been studied as the IMFs and therefore results may vary according to the type. It has been concluded that the Ensemble Empirical Mode Decomposition (EEMD) is the most suitable method for these data. A comparison with Fourier analysis is also conducted to elucidate the strengths and limitations of each method. Furthermore, this investigation examines the Average Diurnal Variation (ADV) and Average Seasonal Variation (ASV) patterns within the wind data. It is found that these patters have a physical significance and interpretation of the IMFs and that it is easier to use EMD than Fourier for wind signals

Forecasting tools and probabilistic scheduling approach incorporatins renewables uncertainty for the insular power systems industry

Nowadays, the paradigm shift in the electricity sector and the advent of the smart grid, along with the growing impositions of a gradual reduction of greenhouse gas emissions, pose numerous challenges related with the sustainable management of power systems. The insular power systems industry is heavily dependent on imported energy, namely fossil fuels, and also on seasonal tourism behavior, which strongly influences the local economy. In comparison with the mainland power system, the behavior of insular power systems is highly influenced by the stochastic nature of the renewable energy sources available. The insular electricity grid is particularly sensitive to power quality parameters, mainly to frequency and voltage deviations, and a greater integration of endogenous renewables potential in the power system may affect the overall reliability and security of energy supply, so singular care should be placed in all forecasting and system operation procedures. The goals of this thesis are focused on the development of new decision support tools, for the reliable forecasting of market prices and wind power, for the optimal economic dispatch and unit commitment considering renewable generation, and for the smart control of energy storage systems. The new methodologies developed are tested in real case studies, demonstrating their computational proficiency comparatively to the current state-of-the-art

A Review of Classification Problems and Algorithms in Renewable Energy Applications

Classification problems and their corresponding solving approaches constitute one of the fields of machine learning. The application of classification schemes in Renewable Energy (RE) has gained significant attention in the last few years, contributing to the deployment, management and optimization of RE systems. The main objective of this paper is to review the most important classification algorithms applied to RE problems, including both classical and novel algorithms. The paper also provides a comprehensive literature review and discussion on different classification techniques in specific RE problems, including wind speed/power prediction, fault diagnosis in RE systems, power quality disturbance classification and other applications in alternative RE systems. In this way, the paper describes classification techniques and metrics applied to RE problems, thus being useful both for researchers dealing with this kind of problem and for practitioners of the field

A REVIEW OF CHALLENGES IN ASSESSMENT AND FORECASTING OF WIND ENERGY RESOURCES

The main issues related to assessment and forecasting of the wind and wind energy have been reviewed. These include the limitations and advantages of wind forecasting and assessment of the wind power density, especially considering trends of increasing penetration of wind-generated power into the utility grid and storage of wind-generated power. Accurate forecasting of the wind power density over a large range of spatial and temporal scales is a critical issue for planning and operations of wind farms. A review of various prediction tools, from simple statistical models to highly complex numerical techniques, was performed for this purpose. The influence of wind variability, atmospheric stability, turbulence, and the low-level jets on wind power density are elaborated on in detail. Furthermore, prediction and assessment of future wind energy resources and their economic implications as well as environmental concerns such as birds’ habitats and routes, viewpoint aesthetics, and noise are also discussed in this study. Some climate projection studies indicate minor changes in the wind resources comparable to differences in global models results while others argue that the wind resources will be reduced due to global warming and they call for harvesting wind energy at the maximum rate as soon as possible

A mobile, scanning eye-safe lidar for the study of atmospheric aerosol particles and transport processes in the lower troposphere

A high-power eye-safe scanning aerosol lidar system in the ultraviolet wavelength region is introduced for the study of the optical properties of aerosol particles and transport processes in the atmosphere, especially in the atmospheric boundary layer (ABL). This system operates with an average power of 9 W in combination with a 40-cm scanner with a speed of up to 10° s-1. A modified version of the lidar inversion algorithm is developed for the retrieval of optical properties of aerosols from scanning lidar measurements. The lidar data can be analyzed with previously unachieved temporal and spatial resolution of 0.03 s and 3 m, respectively.Zur Untersuchung optischer Eigenschaften von Aerosolpartikeln und Transportprozessen in der Atmosphäre, speziell in der atmosphärischen Grenzschicht (atmospheric boundary layer, ABL), wird ein augensicheres Hochleistungs-Scanning-Lidarsystem im ultravioletten Wellenlängenbereich vorgestellt. Das System arbeitet mit einer durchschittlichen Leistung von 9 W in Kombination mit einem 40 cm Scanner mit einer Geschwindigkeit bis zu 10° s-1. Eine modifizierte Version des Lidar-Inversionsalgorithmus zur Rekonstruktion der optischen Eigenschaften von Aerosolpartikeln aus den Scanning-Lidar-Messungen wird entwickelt. Die Lidar-Daten können mit einer bisher nicht erreichten zeitlichen und räumlichen Auflösung von 0,03 s bzw. 3 m analysiert werden