Prognostics in switching systems: Evidential markovian classification of real-time neuro-fuzzy predictions.

International audienceCondition-based maintenance is nowadays considered as a key-process in maintenance strategies and prognostics appears to be a very promising activity as it should permit to not engage inopportune spending. Various approaches have been developed and data-driven methods are increasingly applied. The training step of these methods generally requires huge datasets since a lot of methods rely on probability theory and/or on artificial neural networks. This step is thus time-consuming and generally made in batch mode which can be restrictive in practical application when few data are available. A method for prognostics is proposed to face up this problem of lack of information and missing prior knowledge. The approach is based on the integration of three complementary modules and aims at predicting the failure mode early while the system can switch between several functioning modes. The three modules are: 1) observation selection based on information theory and Choquet Integral, 2) prediction relying on an evolving real-time neuro-fuzzy system and 3) classification into one of the possible functioning modes using an evidential Markovian classifier based on Dempster-Shafer theory. Experiments concern the prediction of an engine health based on more than twenty observations

Review of Low Voltage Load Forecasting: Methods, Applications, and Recommendations

The increased digitalisation and monitoring of the energy system opens up numerous opportunities to decarbonise the energy system. Applications on low voltage, local networks, such as community energy markets and smart storage will facilitate decarbonisation, but they will require advanced control and management. Reliable forecasting will be a necessary component of many of these systems to anticipate key features and uncertainties. Despite this urgent need, there has not yet been an extensive investigation into the current state-of-the-art of low voltage level forecasts, other than at the smart meter level. This paper aims to provide a comprehensive overview of the landscape, current approaches, core applications, challenges and recommendations. Another aim of this paper is to facilitate the continued improvement and advancement in this area. To this end, the paper also surveys some of the most relevant and promising trends. It establishes an open, community-driven list of the known low voltage level open datasets to encourage further research and development.Comment: 37 pages, 6 figures, 2 tables, review pape

Enhancing statistical wind speed forecasting models : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Engineering at Massey University, Manawatū Campus, New Zealand

In recent years, wind speed forecasting models have seen significant development and growth. In particular, hybrid models have been emerging since the last decade. Hybrid models combine two or more techniques from several categories, with each model utilizing its distinct strengths. Mainly, data-driven models that include statistical and Artificial Intelligence/Machine Learning (AI/ML) models are deployed in hybrid models for shorter forecasting time horizons (< 6hrs). Literature studies show that machine learning models have gained enormous potential owing to their accuracy and robustness. On the other hand, only a handful of studies are available on the performance enhancement of statistical models, despite the fact that hybrid models are incomplete without statistical models. To address the knowledge gap, this thesis identified the shortcomings of traditional statistical models while enhancing prediction accuracy. Three statistical models are considered for analyses: Grey Model [GM(1,1)], Markov Chain, and Holt’s Double Exponential Smoothing models. Initially, the problems that limit the forecasting models' applicability are highlighted. Such issues include negative wind speed predictions, failure of predetermined accuracy levels, non-optimal estimates, and additional computational cost with limited performance. To address these concerns, improved forecasting models are proposed considering wind speed data of Palmerston North, New Zealand. Several methodologies have been developed to improve the model performance and fulfill the necessary and sufficient conditions. These approaches include adjusting dynamic moving window, self-adaptive state categorization algorithm, a similar approach to the leave-one-out method, and mixed initialization method. Keeping in view the application of the hybrid methods, novel MODWT-ARIMA-Markov and AGO-HDES models are further proposed as secondary objectives. Also, a comprehensive analysis is presented by comparing sixteen models from three categories, each for four case studies, three rolling windows, and three forecasting horizons. Overall, the improved models showed higher accuracy than their counter traditional models. Finally, the future directions are highlighted that need subsequent research to improve forecasting performance further

An Interpretable Probabilistic Autoregressive Neural Network Model for Time Series Forecasting

Forecasting time series data presents an emerging field of data science that has its application ranging from stock price and exchange rate prediction to the early prediction of epidemics. Numerous statistical and machine learning methods have been proposed in the last five decades with the demand for generating high-quality and reliable forecasts. However, in real-life prediction problems, situations exist in which a model based on one of the above paradigms is preferable, and therefore, hybrid solutions are needed to bridge the gap between classical forecasting methods and scalable neural network models. We introduce an interpretable probabilistic autoregressive neural network model for an explainable, scalable, and "white box-like" framework that can handle a wide variety of irregular time series data (e.g., nonlinearity and nonstationarity). Sufficient conditions for asymptotic stationarity and geometric ergodicity are obtained by considering the asymptotic behavior of the associated Markov chain. During computational experiments, PARNN outperforms standard statistical, machine learning, and deep learning models on a diverse collection of real-world datasets coming from economics, finance, and epidemiology, to mention a few. Furthermore, the proposed PARNN model improves forecast accuracy significantly for 10 out of 12 datasets compared to state-of-the-art models for short to long-term forecasts

Data Mining in Smart Grids

Effective smart grid operation requires rapid decisions in a data-rich, but information-limited, environment. In this context, grid sensor data-streaming cannot provide the system operators with the necessary information to act on in the time frames necessary to minimize the impact of the disturbances. Even if there are fast models that can convert the data into information, the smart grid operator must deal with the challenge of not having a full understanding of the context of the information, and, therefore, the information content cannot be used with any high degree of confidence. To address this issue, data mining has been recognized as the most promising enabling technology for improving decision-making processes, providing the right information at the right moment to the right decision-maker. This Special Issue is focused on emerging methodologies for data mining in smart grids. In this area, it addresses many relevant topics, ranging from methods for uncertainty management, to advanced dispatching. This Special Issue not only focuses on methodological breakthroughs and roadmaps in implementing the methodology, but also presents the much-needed sharing of the best practices. Topics include, but are not limited to, the following: Fuzziness in smart grids computing Emerging techniques for renewable energy forecasting Robust and proactive solution of optimal smart grids operation Fuzzy-based smart grids monitoring and control frameworks Granular computing for uncertainty management in smart grids Self-organizing and decentralized paradigms for information processin

Системный подход к прогнозированию

Проблематика. Для подальшого підвищення якості прогнозування динаміки розвитку фінансово-економічних процесів (ФЕП) необхідно розробляти нові методи та підходи в межах сучасних концепцій створення інформаційних систем підтримки прийняття рішень (СППР). Мета дослідження. Головна мета дослідження: розглянути принципи системного аналізу, що можуть бути використані для розв’язання задачі короткострокового прогнозування; розробити ефективну систему обробки даних, яка ґрунтується на принципах системного аналізу, реалізованих у СППР; проаналізувати можливі типи невизначеностей, що трапляються при побудові математичних моделей та оцінюванні прогнозів, а також запропонувати методи їх опису і врахування у процесі обробки даних. Методика реалізації. При створенні СППР для прогнозування ФЕП та оцінювання фінансових ризиків використано такі принципи системного аналізу: ієрархічність архітектури системи; ідентифікація та обробка можливих невизначеностей; обчислення альтернативних рішень і супроводження обчислювальних процедур на всіх етапах виконання обчислень. СППР має модульну архітектуру, яку можна розширювати новими функціями, що стосуються попередньої обробки даних, оцінювання параметрів моделей, обчислення оцінок прогнозів та можливих ризиків фінансових втрат. Результати дослідження. Основним результатом дослідження є системна методологія моделювання ФЕП, яка реалізована в межах запропонованої СППР. Висока якість остаточних результатів досягається завдяки супроводженню обчислювальних процесів за допомогою належних множин статистичних критеріїв. Наведено приклад математичного моделювання й оцінювання фінансового ризику. Отримані результати свідчать, що запропонований системний підхід має хороші перспективи для практичного використання. Висновки. Запропоновано системний підхід до математичного моделювання і прогнозування ФЕП та оцінювання фінансового ризику. Застосування цього підходу дає можливість отримувати високоякісні оцінки прогнозів на основі статистичних даних.Background. Further enhancement of forecasts quality for dynamics of financial and economic processes requires development of new techniques and approaches in the frames of modern concepts for constructing informational decision support systems (DSS). Objective. The main purpose of the study is as follows: to consider system analysis principles that are suitable for solving the problem of short-term forecasting; to develop effective data processing system that implements the system analysis principles selected in the frames of DSS; to analyze possible types of uncertainties that are encountered in model constructing and forecasts estimating, and to propose the methods for their description and taking into consideration. Methods. To develop DSS for forecasting financial and economic processes and estimation of financial risks the following system analysis principles were hired: hierarchical architecture, the possibilities for identification and processing possible uncertainties, alternatives computing, and tracking the computational procedures for all stages of data processing. The system developed provides possibilities for taking into consideration statistical and parametric uncertainties. The DSS proposed has a modular architecture that could be easily expanded with new functions like preliminary data processing, model parameters estimation, and procedures for computing forecasts and financial risks. Results. The main result of the study is systemic methodology of mathematical modeling financial and economic processes, that has been implemented in the frames of the DSS proposed. High quality of final results is achieved thanks to appropriate tracking of all computations using several sets of statistical quality criteria. An example is given for mathematical modeling, estimation and forecasting of financial risk. The results of estimation show that the systemic approach proposed has good perspectives for its practical use. Conclusions. Thus, we proposed a systemic approach to mathematical modeling and forecasting financial and economic processes as well as estimation of financial risk. The use of the approach provides possibilities for computing estimate forecasts of high quality using statistical data.Проблематика. Для дальнейшего повышения качества прогнозирования динамики развития финансово-экономических процессов (ФЭП) необходимо разрабатывать новые методы и подходы в рамках современных концепций создания информационных систем поддержки принятия решений (СППР). Цель исследования. Главная цель исследования: рассмотреть принципы системного анализа, которые могут быть использованы для решения задачи краткосрочного прогнозирования; разработать эффективную систему обработки данных, которая базируется на принципах системного анализа, реализованных в СППР; проанализировать возможные типы неопределенностей, встречающихся при построении математических моделей и оценивании прогнозов, а также предложить методы их описания и учета в процессе обработки данных. Методика реализации. При создании СППР для прогнозирования ФЭП и оценивания финансовых рисков использованы такие принципы системного анализа: иерархичность архитектуры системы, идентификация и обработка возможных неопределенностей, получение альтернативных решений и сопровождение вычислительных процедур на всех этапах выполнения вычислений. СППР имеет модульную архитектуру, которая легко расширяется новыми функциями предварительной обработки данных, оценивания параметров, а также процедурами вычисления оценок прогнозов и возможных рисков финансовых потерь. Результаты исследования. Основным результатом исследования является системная методология моделирования ФЭП, которая реализована в рамках предложенной СППР. Высокая точность окончательных результатов достигается благодаря сопровождению вычислительных процессов с помощью соответствующих множеств статистических критериев. Приведен пример математического моделирования и оценивания финансового риска. Полученные результаты свидетельствуют о том, что предложенный системный поход имеет хорошие перспективы для практического использования. Выводы. Предложен системный подход к математическому моделированию и прогнозированию ФЭП, а также к оцениванию финансовых рисков. Применение этого подхода дает возможность получать высококачественные оценки прогнозов на основе статистических данных

A Survey on Data Mining Techniques Applied to Energy Time Series Forecasting

Data mining has become an essential tool during the last decade to analyze large sets of data. The variety of techniques it includes and the successful results obtained in many application fields, make this family of approaches powerful and widely used. In particular, this work explores the application of these techniques to time series forecasting. Although classical statistical-based methods provides reasonably good results, the result of the application of data mining outperforms those of classical ones. Hence, this work faces two main challenges: (i) to provide a compact mathematical formulation of the mainly used techniques; (ii) to review the latest works of time series forecasting and, as case study, those related to electricity price and demand markets.Ministerio de Economía y Competitividad TIN2014-55894-C2-RJunta de Andalucía P12- TIC-1728Universidad Pablo de Olavide APPB81309