Conjunction Weighted Average Method with Fuzzy Expert System for Weather Event Forecasting – A Monthly Outlook

Fuzzy logic as a limiting case of approximate reasoning is viewed in exact reasoning, consider everything in a matter of degree. A collection of elastic or equivalently interpreted to knowledge, a collection of variables in fuzzy constraint. Inference is process as a propagation of elastic constraints. Every logical system is fuzzified in fuzzy logic. Fuzzy logic is fascinating area of research, it trading off between significance and precision. It is convenient way to map space of input to a space of output. Fuzzy logic as so far as the laws of Mathematics refers to reality, they are not certain and so far, as they are certain as complexity rises, precise statements lose meaning and meaningful statements lose precision. Most meteorological infrastructure is surprisingly versatile. For example, the same radar system that can detect oncoming storms will also be useful for gathering general rainfall data for the farming sector. Being able to predict and forecast the weather also allows for data to be gathered to build up a more detailed picture of a nation’s climate, and trends within i

Algorithmic Approaches to Game-theoretical Modeling and Simulation

This paper deals with a methodology of computer modeling and simulation of market competitive situations using game theory. The situations are thematically focused mostly to models of commodity markets but the applications of the methodology can be wider. This methodology covers the whole modeling work, including a primary specification of a problem, making an abstract model, making a simulation model, design of a state space of the problem and the simulator itself. As a whole, the methodology represents a complete framework for implementation of computer models of commodity markets suitable for their further analysis and prediction of their future evolution. The main contribution of the paper consists in the algorithmic implementation of computer processing of large strategic game.Market models, non-cooperative game theory, modeling and simulation, artificial intelligence

Smart Grid Technologies in Europe: An Overview

The old electricity network infrastructure has proven to be inadequate, with respect to modern challenges such as alternative energy sources, electricity demand and energy saving policies. Moreover, Information and Communication Technologies (ICT) seem to have reached an adequate level of reliability and flexibility in order to support a new concept of electricity network—the smart grid. In this work, we will analyse the state-of-the-art of smart grids, in their technical, management, security, and optimization aspects. We will also provide a brief overview of the regulatory aspects involved in the development of a smart grid, mainly from the viewpoint of the European Unio

Computational intelligence for measuring macro-knowledge competitiveness

The aim of this research is to investigate the utilisation of Computational Intelligence methods for constructing Synthetic Composite Indicators (SCI). In particular for delivering a Unified Macro-Knowledge Competitiveness Indicator (UKCI) to enable consistent and transparent assessments and forecasting of the progress and competitiveness of Knowledge Based Economy (KBE). SCI are assessment tools usually constructed to evaluate and contrast entities performance by aggregating intangible measures in many areas such as economy, education, technology and innovation. SCI key value is inhibited in its capacity to aggregate complex and multi-dimensional variables into a single meaningful value. As a result, SCIs have been considered as one of the most important tools for macro-level and strategic decision making. Considering the shortcomings of the existing SCI, this study is proposing an alternative approach to develop Intelligent Synthetic Composite Indicators (iSCI). The suggested approach utilizes Fuzzy Proximity Knowledge Mining technique to build the qualitative taxonomy initially, and Fuzzy c-mean is employed to form the new composite indicators

Wind speed and global radiation forecasting based on differential, deep and stochastic machine learning of patterns in 2-level historical meteo-quantity sets

Accurate forecasting of wind speed and solar radiation can help operators of wind farms and Photo-Voltaic (PV) plants prepare efficient and practicable production plans to balance the supply with demand in the generation and consumption of Renewable Energy (RE). Reliable Artificial Intelligence (AI) forecast models can minimize the effect of wind and solar power fluctuations, eliminating their intermittent character in system dispatch planning and utilization. Intelligent wind and solar energy management is essential in load scheduling and decision-making processes to meet user requirements. The proposed 24-h prediction schemes involve the beginning detection and secondary similarity re-evaluation of optimal day-data sequences, which is a notable incremental improvement against state-of-the-art in the consequent application of statistical AI learning. 2-level altitude measurements allow the identification of data relationships between two surface layers (hill and lowland) and adequate interpretation of various meteorological situations, whose differentiate information is used by AI models to recognize upcoming changes in the mid-term day horizon. Observations at two professional meteorological stations comprise specific quantities, of which the most valuable are automatically selected as input for the day model. Differential learning is a novel designed unconventional neurocomputing approach that combines derivative components produced in selected network nodes in the weighted modular output. The complexity of the node-stepwise composed model corresponds to the patterns included in the training data. It allows for representation of high uncertain and nonlinear dynamic systems, dependent on local RE production, not substantially reducing the input vector dimensionality leading to model over simplifications as standard AI does. Available angular and frequency time data (e.g., wind direction, humidity, and irradiation cycles) are combined with the amplitudes to solve reduced Partial Differential Equations (PDEs), defined in network nodes, in the periodical complex form. This is a substantial improvement over the previous publication design. The comparative results show better efficiency and reliability of differential learning in representing the modular uncertainty and PDE dynamics of patterns on a day horizon, taking into account recent deep and stochastic learning. A free available C++ parametric software together with the processed meteo-data sets allow additional comparisons with the presented model results.Web of Scienc

Intellectual technologies and decision support systems for the control of the economic and financial processes

Запропоновано комп'ютерна система підтримки прийняття рішень, основними завданнями якої є побудова адаптивної моделі і прогнозування різних типів процесів, які розвиваються в соціально-економічних системах під впливом фундаментальних структурних змін. Складність і актуальність розв'язуваної проблеми полягає в необхідності забезпечити прийнятні якісні прогнози фінансових і економічних показників для коротких вибірок даних, коли використання ретроспективних даних неможливо або суттєво обмежена. Розробка СППР заснована на принципах системного аналізу, тобто можливості обліку деяких стохастичних та інформаційних невизначеностей, формування альтернатив для моделей і прогнозів і відстеження правильності обчислювальних процедур на всіх етапах обробки даних. Реалізована модульна архітектура, яка забезпечує можливість подальшого розширення і модифікації функціональних можливостей системи за допомогою нових методів прогнозування та оцінки параметрів. Крім того, пропонована система, завдяки модульній архітектурі, може бути поліпшена за рахунок використання програмного забезпечення різних виробників без будь-яких додаткових структурних змін. Висока якість кінцевого результату досягається завдяки належному відстеження обчислювальних процедур на всіх етапах обробки даних в ході обчислювальних експериментів: попередня обробка даних, побудова моделі і оцінка прогнозів. Відстеження виконується з відповідними наборами статистичних параметрів якості. Наведено приклад оцінки фінансового ризику в сфері страхування та споживання електроенергії з точки зору енергозбереження. Наведені приклади показують, що розроблена система має хороші перспективи для практичного використання. Передбачається, що система буде універсальною і знайде своє застосування в якості додаткового інструменту для підтримки прийняття рішень при розробці стратегій для компаній і підприємств різних типів.A computer-based decision support system is proposed the basic tasks of which are adaptive model constructing and forecasting of various types of processes that are developing in socio-economic systems under the influence of fundamental structural changes. The complexity and urgency of the solvable problem is the need to provide acceptable quality forecasts of financial and economic indicators for short data samples, when the usage of retrospective data is impossible or significantly limited. The DSS development is based on the system analysis principles, i.e. the possibility for taking into consideration of some stochastic and information uncertainties, forming alternatives for models and forecasts, and tracking of the computing procedures correctness during all stages of data processing. A modular architecture is implemented that provides a possibility for the further enhancement and modification of the system functional possibilities with new forecasting and parameter estimation techniques. In addition, the proposed system, thanks to the modular architecture, can be improved by using the software of different vendors without any additional structural changes. A high quality of the final result is achieved thanks to appropriate tracking of the computing procedures at all stages of data processing during computational experiments: preliminary data processing, model constructing, and forecasts estimation. The tracking is performed with appropriate sets of statistical quality parameters. The example is given for estimation of financial risk in the insurance sphere and the electricity consumption in terms of energy saving. The examples solved show that the system developed has good perspectives for practical use. It is supposed that the system will be universal and find its applications as an extra tool for support of decision making when developing the strategies for companies and enterprises of various types.Предложена компьютерная система поддержки принятия решений, основными задачами которой являются построение адаптивной модели и прогнозирование различных типов процессов, которые развиваются в социально-экономических системах под воздействием фундаментальных структурных изменений. Сложность и актуальность решаемой проблемы заключается в необходимости обеспечить приемлемые качественные прогнозы финансовых и экономических показателей для коротких выборок данных, когда использование ретроспективных данных невозможно или существенно ограничено. Разработка СППР основана на принципах системного анализа, то есть возможности учета некоторых стохастических и информационных неопределенностей, формирования альтернатив для моделей и прогнозов и отслеживания правильности вычислительных процедур на всех этапах обработки данных. Реализована модульная архитектура, которая обеспечивает возможность дальнейшего расширения и модификации функциональных возможностей системы с помощью новых методов прогнозирования и оценки параметров. Кроме того, предлагаемая система, благодаря модульной архитектуре, может быть улучшена за счет использования программного обеспечения разных производителей без каких-либо дополнительных структурных изменений. Высокое качество конечного результата достигается благодаря надлежащему отслеживанию вычислительных процедур на всех этапах обработки данных в ходе вычислительных экспериментов: предварительная обработка данных, построение модели и оценка прогнозов. Отслеживание выполняется с соответствующими наборами статистических параметров качества. Приведен пример оценки финансового риска в сфере страхования и потребления электроэнергии с точки зрения энергосбережения. Решенные примеры показывают, что разработанная система имеет хорошие перспективы для практического использования. Предполагается, что система будет универсальной и найдет свое применение в качестве дополнительного инструмента для поддержки принятия решений при разработке стратегий для компаний и предприятий различных типов

Forecasting of power quality parameters based on meteorological data in small-scale household off-grid systems

Off-grid power systems are often used to supply electricity to remote households, cottages, or small industries, comprising small renewable energy systems, typically a photovoltaic plant whose energy supply is stochastic in nature, without electricity distributions. This approach is economically viable and conforms to the requirements of the European Green Deal and the Fit for 55 package. Furthermore, these systems are associated with a lower short circuit power as compared with distribution grid traditional power plants. The power quality parameters (PQPs) of such small-scale off-grid systems are largely determined by the inverter's ability to handle the impact of a device; however, this makes it difficult to accurately forecast the PQPs. To address this issue, this work compared prediction models for the PQPs as a function of the meteorological conditions regarding the off-grid systems for small-scale households in Central Europe. To this end, seven models-the artificial neural network (ANN), linear regression (LR), interaction linear regression (ILR), quadratic linear regression (QLR), pure quadratic linear regression (PQLR), the bagging decision tree (DT), and the boosting DT-were considered for forecasting four PQPs: frequency, the amplitude of the voltage, total harmonic distortion of the voltage (THDu), and current (THDi). The computation times of these forecasting models and their accuracies were also compared. Each forecasting model was used to forecast the PQPs for three sunny days in August. As a result of the study, the most accurate methods for forecasting are DTs. The ANN requires the longest computational time, and conversely, the LR takes the shortest computational time. Notably, this work aimed to predict poor PQPs that could cause all the equipment in off-grid systems to respond in advance to disturbances. This study is expected to be beneficial for the off-grid systems of small households and the substations included in existing smart grids.Web of Science1514art. no. 525

Autonomic Computing

Autonomic computing (AC) has as its vision the creation of self-managing systems to address today’s con-cerns of complexity and total cost of ownership while meeting tomorrow’s needs for pervasive and ubiquitous computation and communication. This paper reports on the latest auto-nomic systems research and technologies to influence the industry; it looks behind AC, summarising what it is, the current state-of-the-art research, related work and initiatives, highlights research and technology transfer issues and concludes with further and recommended reading