Dynamic artificial neural network for electricity market prices forecast

This paper presents an artificial neural network applied to the forecasting of electricity market prices, with the special feature of being dynamic. The dynamism is verified at two different levels. The first level is characterized as a re-training of the network in every iteration, so that the artificial neural network can able to consider the most recent data at all times, and constantly adapt itself to the most recent happenings. The second level considers the adaptation of the neural network’s execution time depending on the circumstances of its use. The execution time adaptation is performed through the automatic adjustment of the amount of data considered for training the network. This is an advantageous and indispensable feature for this neural network’s integration in ALBidS (Adaptive Learning strategic Bidding System), a multi-agent system that has the purpose of providing decision support to the market negotiating players of MASCEM (Multi-Agent Simulator of Competitive Electricity Markets).

Adapting Meeting Tools to Agent Decision

Electricity markets are complex environments comprising several negotiation mechanisms. MASCEM (Multi- Agent System for Competitive Electricity Markets) is a simulator developed to allow deep studies of the interactions between the players that take part in the electricity market negotiations. ALBidS (Adaptive Learning Strategic Bidding System) is a multiagent system created to provide decision support to market negotiating players. Fully integrated with MASCEM it considers several different methodologies based on very distinct approaches. The Six Thinking Hats is a powerful technique used to look at decisions from different perspectives. This paper aims to complement ALBidS strategies usage by MASCEM players, providing, through the Six Thinking Hats group decision technique, a means to combine them and take advantages from their different perspectives. The combination of the different proposals resulting from ALBidS’ strategies is performed through the application of a Genetic Algorithm, resulting in an evolutionary learning approach.

Reinforcement Learning Based on the Bayesian Theorem for Electricity Markets Decision Support

This paper presents the applicability of a reinforcement learning algorithm based on the application of the Bayesian theorem of probability. The proposed reinforcement learning algorithm is an advantageous and indispensable tool for ALBidS (Adaptive Learning strategic Bidding System), a multi-agent system that has the purpose of providing decision support to electricity market negotiating players. ALBidS uses a set of different strategies for providing decision support to market players. These strategies are used accordingly to their probability of success for each different context. The approach proposed in this paper uses a Bayesian network for deciding the most probably successful action at each time, depending on past events. The performance of the proposed methodology is tested using electricity market simulations in MASCEM (Multi-Agent Simulator of Competitive Electricity Markets). MASCEM provides the means for simulating a real electricity market environment, based on real data from real electricity market operators.

Strategic Bidding for Electricity Markets Negotiation Using Support Vector Machines

Energy systems worldwide are complex and challenging environments. Multi-agent based simulation platforms are increasing at a high rate, as they show to be a good option to study many issues related to these systems, as well as the involved players at act in this domain. In this scope the authors’ research group has developed a multi-agent system: MASCEM (Multi- Agent System for Competitive Electricity Markets), which simulates the electricity markets environment. MASCEM is integrated with ALBidS (Adaptive Learning Strategic Bidding System) that works as a decision support system for market players. The ALBidS system allows MASCEM market negotiating players to take the best possible advantages from the market context. This paper presents the application of a Support Vector Machines (SVM) based approach to provide decision support to electricity market players. This strategy is tested and validated by being included in ALBidS and then compared with the application of an Artificial Neural Network, originating promising results. The proposed approach is tested and validated using real electricity markets data from MIBEL - Iberian market operator.

Particle Swarm Optimization of Electricity Market Negotiating Players Portfolio

Energy systems worldwide are complex and challenging environments. Multi-agent based simulation platforms are increasing at a high rate, as they show to be a good option to study many issues related to these systems, as well as the involved players at act in this domain. In this scope the authors’ research group has developed a multi-agent system: MASCEM (Multi- Agent System for Competitive Electricity Markets), which performs realistic simulations of the electricity markets. MASCEM is integrated with ALBidS (Adaptive Learning Strategic Bidding System) that works as a decision support system for market players. The ALBidS system allows MASCEM market negotiating players to take the best possible advantages from each market context. However, it is still necessary to adequately optimize the players’ portfolio investment. For this purpose, this paper proposes a market portfolio optimization method, based on particle swarm optimization, which provides the best investment profile for a market player, considering different market opportunities (bilateral negotiation, market sessions, and operation in different markets) and the negotiation context such as the peak and off-peak periods of the day, the type of day (business day, weekend, holiday, etc.) and most important, the renewable based distributed generation forecast. The proposed approach is tested and validated using real electricity markets data from the Iberian operator – MIBEL.

MASCEM Restructuring: Ontologies For Scenarios Generation in Power Systems Simulators

The electricity market restructuring, along with the increasing necessity for an adequate integration of renewable energy sources, is resulting in an rising complexity in power systems operation. Various power system simulators have been introduced in recent years with the purpose of helping operators, regulators, and involved players to understand and deal with this complex environment. This paper focuses on the development of an upper ontology which integrates the essential concepts necessary to interpret all the available information. The restructuring of MASCEM (Multi-Agent System for Competitive Electricity Markets), and this system’s integration with MASGriP (Multi-Agent Smart Grid Platform), and ALBidS (Adaptive Learning Strategic Bidding System) provide the means for the exemplification of the usefulness of this ontology. A practical example is presented, showing how common simulation scenarios for different simulators, directed to very distinct environments, can be created departing from the proposed ontology.

Electricity Markets Portfolio Optimization using a Particle Swarm Approach

Energy systems worldwide are complex and challenging environments. Multi-agent based simulation platforms are increasing at a high rate, as they show to be a good option to study many issues related to these systems, as well as the involved players at act in this domain. In this scope the authors’ research group has developed a multi-agent system: MASCEM (Multi-Agent System for Competitive Electricity Markets), which simulates the electricity markets. MASCEM is integrated with ALBidS (Adaptive Learning Strategic Bidding System) that works as a decision support system for market players. The ALBidS system allows MASCEM market negotiating players to take the best possible advantages from the market context. However, it is still necessary to adequately optimize the player’s portfolio investment. For this purpose, this paper proposes a market portfolio optimization method, based on particle swarm optimization, which provides the best investment profile for a market player, considering the different markets the player is acting on in each moment, and depending on different contexts of negotiation, such as the peak and offpeak periods of the day, and the type of day (business day, weekend, holiday, etc.). The proposed approach is tested and validated using real electricity markets data from the Iberian operator – OMIE.

Negotiation context analysis in electricity markets

Contextualization is critical in every decision making process. Adequate responses to problems depend not only on the variables with direct influence on the outcomes, but also on a correct contextualization of the problem regarding the surrounding environment. Electricity markets are dynamic environments with increasing complexity, potentiated by the last decades' restructuring process. Dealing with the growing complexity and competitiveness in this sector brought the need for using decision support tools. A solid example is MASCEM (Multi-Agent Simulator of Competitive Electricity Markets), whose players' decisions are supported by another multiagent system – ALBidS (Adaptive Learning strategic Bidding System). ALBidS uses artificial intelligence techniques to endow market players with adaptive learning capabilities that allow them to achieve the best possible results in market negotiations. This paper studies the influence of context awareness in the decision making process of agents acting in electricity markets. A context analysis mechanism is proposed, considering important characteristics of each negotiation period, so that negotiating agents can adapt their acting strategies to different contexts. The main conclusion is that context-dependant responses improve the decision making process. Suiting actions to different contexts allows adapting the behaviour of negotiating entities to different circumstances, resulting in profitable outcomes.

Six thinking hats: A novel metalearner for intelligent decision support in electricity markets

The energy sector has suffered a significant restructuring that has increased the complexity in electricity market players' interactions. The complexity that these changes brought requires the creation of decision support tools to facilitate the study and understanding of these markets. The Multiagent Simulator of Competitive Electricity Markets (MASCEM) arose in this context, providing a simulation framework for deregulated electricity markets. The Adaptive Learning strategic Bidding System (ALBidS) is a multiagent system created to provide decision support to market negotiating players. Fully integrated with MASCEM, ALBidS considers several different strategic methodologies based on highly distinct approaches. Six Thinking Hats (STH) is a powerful technique used to look at decisions from different perspectives, forcing the thinker to move outside its usual way of thinking. This paper aims to complement the ALBidS strategies by combining them and taking advantage of their different perspectives through the use of the STH group decision technique. The combination of ALBidS' strategies is performed through the application of a genetic algorithm, resulting in an evolutionary learning approach.

Optimização do despacho e reserva girante em sistemas eléctricos híbridos. Estudo de caso: sistema eléctrico da Ilha de Santiago em Cabo Verde

Dissertação para obtenção do grau de Mestre em Energias Renováveis – Conversão Eléctrica e Utilização Sustentáveis

Predicting chick body mass by artificial intelligence-based models

The objective of this work was to develop, validate, and compare 190 artificial intelligence-based models for predicting the body mass of chicks from 2 to 21 days of age subjected to different duration and intensities of thermal challenge. The experiment was conducted inside four climate-controlled wind tunnels using 210 chicks. A database containing 840 datasets (from 2 to 21-day-old chicks) - with the variables dry-bulb air temperature, duration of thermal stress (days), chick age (days), and the daily body mass of chicks - was used for network training, validation, and tests of models based on artificial neural networks (ANNs) and neuro-fuzzy networks (NFNs). The ANNs were most accurate in predicting the body mass of chicks from 2 to 21 days of age after they were subjected to the input variables, and they showed an R² of 0.9993 and a standard error of 4.62 g. The ANNs enable the simulation of different scenarios, which can assist in managerial decision-making, and they can be embedded in the heating control systems.

Soft Computing Methods in Metallurgy

In metallurgic plants a high quality metal production is always required. Nowadays soft computing applications are more often used for automation of manufacturing process and quality control instead of mechanical techniques. In this thesis an overview of soft computing methods presents. As an example of soft computing application, an effective model of fuzzy expert system for the automotive quality control of steel degassing process was developed. The purpose of this work is to describe the fuzzy relations as quality hypersurfaces by varying number of linguistic variables and fuzzy sets.

Environmental factors in multiple sclerosis [Environmental factors in multiple sclerosis]

Although multiple sclerosis (MS) is recognized as a disorder involving the immune system, the interplay of environmental factors and individual genetic susceptibility seems to influence MS onset and clinical expression, as well as therapeutic responsiveness. Multiple human epidemiological and animal model studies have evaluated the effect of different environmental factors, such as viral infections, vitamin intake, sun exposure, or still dietary and life habits on MS prevalence. Previous Epstein-Barr virus infection, especially if this infection occurs in late childhood, and lack of vitamin D (VitD) currently appear to be the most robust environmental factors for the risk of MS, at least from an epidemiological standpoint. Ultraviolet radiation (UVR) activates VitD production but there are also some elements supporting the fact that insufficient UVR exposure during childhood may represent a VitD-independent risk factor of MS development, as well as negative effect on the clinical and radiological course of MS. Recently, there has been a growing interest in the gut-brain axis, a bidirectional neuro-hormonal communication system between the intestinal microbiota and the central nervous system (CNS). Indeed, components of the intestinal microbiota may be pro-inflammatory, promote the migration of immune cells into the CNS, and thus be a key parameter for the development of autoimmune disorders such as MS. Interestingly most environmental factors seem to play a role during childhood. Thus, if childhood is the most fragile period to develop MS later in life, preventive measures should be applied early in life. For example, adopting a diet enriched in VitD, playing outdoor and avoiding passive smoking would be extremely simple measures of primary prevention for public health strategies. However, these hypotheses need to be confirmed by prospective evaluations, which are obviously difficult to conduct. In addition, it remains to be determined whether and how VitD supplementation in adult life would be useful in alleviating the course of MS, once this disease has already started. A better knowledge of the influence of various environmental stimuli on MS risk and course would certainly allow the development of add-on therapies or measures in parallel to the immunotherapies currently used in MS.

Advanced orbital pipe welding

Since the introduction of automatic orbital welding in pipeline application in 1961, significant improvements have been obtained in orbital pipe welding systems. Requirement of more productive welding systems for pipeline application forces manufacturers to innovate new advanced systems and welding processes for orbital welding method. Various methods have been used to make welding process adaptive, such as visual sensing, passive visual sensing, real-time intelligent control, scan welding technique, multi laser vision sensor, thermal scanning, adaptive image processing, neural network model, machine vision, and optical sensing. Numerous studies are reviewed and discussed in this Master’s thesis and based on a wide range of experiments which already have been accomplished by different researches the vision sensor are reported to be the best choice for adaptive orbital pipe welding system. Also, in this study the most welding processes as well as the most pipe variations welded by orbital welding systems mainly for oil and gas pipeline applications are explained. The welding results show that Gas Metal Arc Welding (GMAW) and its variants like Surface Tension Transfer (STT) and modified short circuit are the most preferred processes in the welding of root pass and can be replaced to the Gas Tungsten Arc Welding (GTAW) in many applications. Furthermore, dual-tandem gas metal arc welding technique is currently considered the most efficient method in the welding of fill pass. Orbital GTAW process mostly is applied for applications ranging from single run welding of thin walled stainless tubes to multi run welding of thick walled pipes. Flux cored arc welding process is faster process with higher deposition rate and recently this process is getting more popular in pipe welding applications. Also, combination of gas metal arc welding and Nd:YAG laser has shown acceptable results in girth welding of land pipelines for oil and gas industry. This Master’s thesis can be implemented as a guideline in welding of pipes and tubes to achieve higher quality and efficiency. Also, this research can be used as a base material for future investigations to supplement present finding.

Eri MAG-hitsausprosessien ja -parametrien vaikutus suurlujuusteräksestä valmistetun otsapienahitsin äärikestävyyteen, muodonmuutoskykyyn ja vauriomuotoon

Työssä tutkittiin Ruukki Oyj:n suorasammutetusta S960QC-teräksestä valmistetun kuormaakantavan levyjen ristiliitoksen pienahitsien lujuutta, muodonmuutoskykyä ja vaurioitumismekanismia laajaan kokeelliseen aineistoon perustuen. Tärkeimpänä muuttujana koematriisissa olivat eri MAG-hitsausprosessit. Perinteisen kuumakaarihitsauksen vertailukohteena oli Kemppi Oy:n uusi adaptiivinen valokaaren pituutta säätävä WiseFusion-hitsaustoiminto kuuma- ja pulssikaarihitsauksessa. Näiden kolmen hitsausprosessin rinnalla varioitiin erisuuruisia a-mittoja, eri hitsauslisäaineita ja hitsin erikylkisyyttä. Lisäksi juuritunkeuman estämisen vaikutusta hitsien käyttäytymiseen tutkittiin täydentävällä koesarjalla, jossa tunkeuman muodostuminen estettiin levyjen väliin asetetulla volframilevyllä. Työn perimmäisenä tavoitteena oli selvittää syy aiemmassa tutkimuksessa havaitulle pienahitsien vaurioitumiselle leikkautumalla sularajaa pitkin. Sularajan suhteellista pituutta saadaan kasvatettua estämällä hitsin juuritunkeuma ja absoluuttista pituutta saadaan lisää kateettipoikkeaman avulla. Lisäksi tutkimuksessa oli tarkoitus tuoda esille suurlujuusteräksisen liitoksen eri mitoituslähtökohdat (mm. lämmöntuonnin kontrollointi). Tämän vuoksi hitsausparametrit mitattiin jännitteen ja virran hetkellisiin arvoihin perustuen, jolloin kuuma- ja pulssikaarihitsauksen laskennalliset hitsaustehot ovat vertailukelpoisia. Koehitseistä valmistettiin hieet hitsien tarkan geometrian määrittämiseksi ja liitoksen mekaaniset ominaisuudet tutkittiin vetokokeella. Tulosten perusteella sularajavaurio aktivoituu pulssikaarella hitsatuissa koekappaleissa. Tämä aiheutunee pulssi- ja kuumakaarihitsauksen sularajan mikrorakenteiden eroavaisuudesta. Sularajavaurio näyttää huonontavan hitsien muodonmuutoskykyä, mutta jatkokokeita tuloksen verifioimiseksi on tehtävä. S960QC-teräkselle ominaisen pehmenneen vyöhykkeen vaurio ei aktivoitunut, vaikka Ruukin antamat jäähtymisaikasuositukset ylitettiin reilusti.