Stream Learning in Energy IoT Systems: A Case Study in Combined Cycle Power Plants

The prediction of electrical power produced in combined cycle power plants is a key challenge in the electrical power and energy systems field. This power production can vary depending on environmental variables, such as temperature, pressure, and humidity. Thus, the business problem is how to predict the power production as a function of these environmental conditions, in order to maximize the profit. The research community has solved this problem by applying Machine Learning techniques, and has managed to reduce the computational and time costs in comparison with the traditional thermodynamical analysis. Until now, this challenge has been tackled from a batch learning perspective, in which data is assumed to be at rest, and where models do not continuously integrate new information into already constructed models. We present an approach closer to the Big Data and Internet of Things paradigms, in which data are continuously arriving and where models learn incrementally, achieving significant enhancements in terms of data processing (time, memory and computational costs), and obtaining competitive performances. This work compares and examines the hourly electrical power prediction of several streaming regressors, and discusses about the best technique in terms of time processing and predictive performance to be applied on this streaming scenario.This work has been partially supported by the EU project iDev40. This project has received funding from the ECSEL Joint Undertaking (JU) under grant agreement No 783163. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and Austria, Germany, Belgium, Italy, Spain, Romania. It has also been supported by the Basque Government (Spain) through the project VIRTUAL (KK-2018/00096), and by Ministerio de Economía y Competitividad of Spain (Grant Ref. TIN2017-85887-C2-2-P)

Nuevos algoritmos de soft-computing en física atmosférica

Tesis de la Universidad Complutense de Madrid, Facultad de Ciencias Físicas, leída el 12-03-2019This Ph.D. Thesis elaborates and analyzes several hybrid Soft-Computing algorithms for optimization and prediction problems in Atmospheric Physics. The core of the Thesis is a recently developed optimization meta-heuristic, the Coral Reefs Optimization Algorithm (CRO), an evolutionary-based approach which considers a population of possible solutions to a given optimization problem. It simulates different procedures mimicking real processes occurring in coral reefs in order to evolve the population towards good solutions for the problem. Alternative modifications of this algorithm lead to powerful co-evolution meta-heuristics, such as theCRO-SL, in which Substrates implementing different search procedures are included. Another modification of the algorithm leads to the CRO-SP, which considers Species in the evolutionof the population, and it is able to deal with different encodings within a single population.These approaches are hybridized with other Machine Learning and traditional algorithms such as neural networks or the Analogue Method (AM), to come up with powerful hybrid approaches able to solve hard problems in Atmospheric Physics...En esta Tesis Doctoral se elaboran y analizan en detalle diferentes algoritmos híbridos deSoft-Computing para problemas de optimización y predicción en Física de la Atmósfera. El núcleo central de la Tesis es un algoritmo meta-heurístico de optimización recientemente desarrollado, conocido como Coral Reefs Optimization algorithm (CRO). Este algoritmo pertenece a la familia de la Computación Evolutiva, de forma que considera una población de solucionesa un problema concreto, y simula los diferentes procesos que ocurren en un arrecife de coralpara evolucionar dicha población hacia la solución óptima del problema. Recientemente se han propuesto diferentes versiones del algoritmo CRO básico para obtener mecanismos potentes de optimización co-evolutiva. Una de estas modificaciones es el CRO-SL, en la que se definen un conjunto de Sustratos en el algoritmo, de manera que cada sustrato simula un mecanismo de evolución diferente, que son aplicados a la vez en una única población. Otra modificación hadado lugar al conocido como CRO-SP, un algoritmo donde se definen diferentes Especies, capaz de manejar varias codificaciones para un mismo problema a la vez. Estas versiones del CRO han sido hibridadas con varias técnicas de Aprendizaje Máquina, tales como varios tipos de redes neuronales de entrenamiento rápido, sistemas de aprendizaje tales como Máquinas de Vectores Soporte, o sistemas de predicción vinculados totalmente al área de la Física Atmosférica, tales como el Método de los Análogos (AM). Los algoritmos híbridos obtenidos son muy robustos y capaces de obtener excelentes soluciones en diferentes problemas donde han sido probados...Fac. de Ciencias FísicasTRUEunpu

Optimal Microgrid Topology Design and Siting of Distributed Generation Sources Using a Multi-Objective Substrate Layer Coral Reefs Optimization Algorithm

n this work, a problem of optimal placement of renewable generation and topology design for a Microgrid (MG) is tackled. The problem consists of determining the MG nodes where renewable energy generators must be optimally located and also the optimization of the MG topology design, i.e., deciding which nodes should be connected and deciding the lines’ optimal cross-sectional areas (CSA). For this purpose, a multi-objective optimization with two conflicting objectives has been used, utilizing the cost of the lines, C, higher as the lines’ CSA increases, and the MG energy losses, E, lower as the lines’ CSA increases. To characterize generators and loads connected to the nodes, on-site monitored annual energy generation and consumption profiles have been considered. Optimization has been carried out by using a novel multi-objective algorithm, the Multi-objective Substrate Layers Coral Reefs Optimization algorithm (Mo-SL-CRO). The performance of the proposed approach has been tested in a realistic simulation of a MG with 12 nodes, considering photovoltaic generators and micro-wind turbines as renewable energy generators, as well as the consumption loads from different commercial and industrial sites. We show that the proposed Mo-SL-CRO is able to solve the problem providing good solutions, better than other well-known multi-objective optimization techniques, such as NSGA-II or multi-objective Harmony Search algorithm.This research was partially funded by Ministerio de Economía, Industria y Competitividad, project number TIN2017-85887-C2-1-P and TIN2017-85887-C2-2-P, and by the Comunidad Autónoma de Madrid, project number S2013ICE-2933_02

Characterisation of large changes in wind power for the day-ahead market using a fuzzy logic approach

Wind power has become one of the renewable resources with a major growth in the electricity market. However, due to its inherent variability, forecasting techniques are necessary for the optimum scheduling of the electric grid, specially during ramp events. These large changes in wind power may not be captured by wind power point forecasts even with very high resolution Numerical Weather Prediction (NWP) models. In this paper, a fuzzy approach for wind power ramp characterisation is presented. The main benefit of this technique is that it avoids the binary definition of ramp event, allowing to identify changes in power out- put that can potentially turn into ramp events when the total percentage of change to be considered a ramp event is not met. To study the application of this technique, wind power forecasts were obtained and their corresponding error estimated using Genetic Programming (GP) and Quantile Regression Forests. The error distributions were incorporated into the characterisation process, which according to the results, improve significantly the ramp capture. Results are presented using colour maps, which provide a useful way to interpret the characteristics of the ramp events

Hydnum pallidum Raddi, the Correct Name for H. albidum Peck in the Sense of European Authors and the Recently Described H. reginae Kibby, Liimat. & Niskanen

The systematics of the genus Hydnum have undergone important advances, and many new species have been described with the aid of molecular data. A revision of old names that refer to Hydnum s. str., considering the knowledge now available, might reveal prioritary names of recently described species. This study focuses on the study of names that refer to white Hydnum in Europe, among which earlier synonyms of Hydnum reginae (=Hydnum albidum s. auct. pl. eur.) are potentially found, a species characterized by producing white basidiomata and smaller spores than any other European species. Our revision revealed the existence of three earlier names based on European material, namely H. pallidum Raddi, H. album Fr. and H. heimii Maas Geest. The earliest of those, Hydnum pallidum, is epitypified using material from Tuscany (Italy), from where it was originally described, and hence, it becomes the correct name for H. albidum s. auct. pl. eur. A full description and photographs of H. pallidum are provided, and further comments on other names that refer to white Hydnum based on European material are made.This research was funded by the Spanish Research Agency (Agencia Estatal de Investigación, AEI) through the grant PID-2020116570GB-100

Density, refractive index, and derived properties of binary mixtures of water + ionic liquid 1-(2-hydroxyethyl)-3-methylimidazolium tetrafluoroborate

The density and refractive index were experimentally determined for binary mixtures of water + ionic liquid 1-(2-hydroxyethyl)-3-methylimidazolium tetrafluoroborate throughout the ionic liquid mass frac- tion range, at atmospheric pressure and various temperatures between 293.15 K and 323.15 K. The refractive index was measured at five wavelengths between 589.2 nm and 935 nm. From the experimen- tal data on density, volumetric properties such as the excess molar volume and thermal expansion coef- ficient were calculated. The excess molar volume was negative throughout the ionic liquid mass fraction range and its magnitude decreased with temperature. From the experimental data on the refractive index, the deviation in the refractive index and its coefficients of concentration, temperature and chro- matic dispersion were obtained. The values of the deviation in the refractive index were positive and decreased with temperature. In order to simultaneously investigate the dependence of the refractive index on concentration, temperature and wavelength, we correlated the experimental data with a two-term Cauchy equation. Furthermore, a comparative study of 11 refractive index mixing rules was performed to assess their prediction ability. More advanced mixing rules do not lead to any improvement in comparison with the simple linear mixing rule (Arago-Biot) for estimating refractive index and the concentration contrast factor of the mixture studied. The results are expected to be useful for tuning the properties of an ionic liquid by adding water or selecting the temperature or optical region.Peer ReviewedPostprint (author's final draft

Optimal generation scheduling in hydro-power plants with the Coral Reefs Optimization algorithm

Hydro-power plants are able to produce electrical energy in a sustainable way. A known format for producing energy is through generation scheduling, which is a task usually established as a Unit Commitment problem. The challenge in this process is to define the amount of energy that each turbine-generator needs to deliver to the plant, to fulfill the requested electrical dispatch commitment, while coping with the operational restrictions. An optimal generation scheduling for turbine-generators in hydro-power plants can offer a larger amount of energy to be generated with respect to non-optimized schedules, with significantly less water consumption. This work presents an efficient mathematical modelling for generation scheduling in a real hydro-power plant in Brazil. An optimization method based on different versions of the Coral Reefs Optimization algorithm with Substrate Layers (CRO) is proposed as an effective method to tackle this problem.This approach uses different search operators in a single population to refine the search for an optimal scheduling for this problem. We have shown that the solution obtained with the CRO using Gaussian search in exploration is able to produce competitive solutions in terms of energy production. The results obtained show a huge savings of 13.98 billion (liters of water) monthly projected versus the non-optimized scheduling.European CommissionMinisterio de Economía y CompetitividadComunidad de Madri

Component-Resolved in Vitro Diagnosis in Peach-Allergic Patients

BACKGROUND: The in vitro diagnosis of pollen-related food allergy presents low specifi city and reproducibility with many conventional extracts. This can be improved using natural purifi ed allergens, recombinant purifi ed allergens, or both. OBJECTIVE: We compared specifi c immunoglobulin (Ig) E determination (sIgE), the basophil activation test (BAT), the histamine release test (HRT), and the cellular allergen stimulation test (CAST) using natural and recombinant allergens in the diagnosis of peach allergy. METHODS: Thirty-two peach allergic patients were studied. Skin prick tests were performed with commercial peach and extract with Mal d 1, nPru p 3, and profi lin (nPho d 2). sIgE, BAT, CAST, and HRT were determined using rPru p 3, rMal d 3, rBet v 1, rMal d 1, and rMal d 4. RESULTS: Agreement between the techniques was good with all the allergens, except HRT with rMal d 1 and rMal d 4. With rPru p 3, sIgE, CAST, BAT, and HRT showed sensitivity values of 88%, 81%, 72%, and 69% and specifi city values of 100%, 93%, 97%, and 83%, respectively. In patients with systemic symptoms or contact urticaria, the values were 100%, 85%, 81%, and 81%. In patients with oral allergy syndrome, sensitivity to profi lins or homologues of Bet v 1 was detected in 100% of the cases by all the techniques, except by HRT with rMal d 1, which detected 66% of the cases. CONCLUSIONS: The use of single allergens in the in vitro diagnosis of peach allergy by specifi c IgE determination, BAT, and CAST offers high specifi city and sensitivity, with better results than the HRT