Bayesian Optimization Algorithm-Based Statistical and Machine Learning Approaches for Forecasting Short-Term Electricity Demand

This article focuses on developing both statistical and machine learning approaches for forecasting hourly electricity demand in Ontario. The novelties of this study include (i) identifying essential factors that have a significant effect on electricity consumption, (ii) the execution of a Bayesian optimization algorithm (BOA) to optimize the model hyperparameters, (iii) hybridizing the BOA with the seasonal autoregressive integrated moving average with exogenous inputs (SARIMAX) and nonlinear autoregressive networks with exogenous input (NARX) for modeling separately short-term electricity demand for the first time, (iv) comparing the model’s performance using several performance indicators and computing efficiency, and (v) validation of the model performance using unseen data. Six features (viz., snow depth, cloud cover, precipitation, temperature, irradiance toa, and irradiance surface) were found to be significant. The Mean Absolute Percentage Error (MAPE) of five consecutive weekdays for all seasons in the hybrid BOA-NARX is obtained at about 3%, while a remarkable variation is observed in the hybrid BOA-SARIMAX. BOA-NARX provides an overall steady Relative Error (RE) in all seasons (1~6.56%), while BOA-SARIMAX provides unstable results (Fall: 0.73~2.98%; Summer: 8.41~14.44%). The coefficient of determination (R2) values for both models are >0.96. Overall results indicate that both models perform well; however, the hybrid BOA-NARX reveals a stable ability to handle the day-ahead electricity load forecasts

Machine Learning Tool for Transmission Capacity Forecasting of Overhead Lines based on Distributed Weather Data

Die Erhöhung des Anteils intermittierender erneuerbarer Energiequellen im elektrischen Energiesystem ist eine Herausforderung für die Netzbetreiber. Ein Beispiel ist die Zunahme der Nord-Süd Übertragung von Windenergie in Deutschland, die zu einer Erhöhung der Engpässe in den Freileitungen führt und sich direkt in den Stromkosten der Endverbraucher niederschlägt. Neben dem Ausbau neuer Freileitungen ist ein witterungsabhängiger Freileitungsbetrieb eine Lösung, um die aktuelle Auslastung des Systems zu verbessern. Aus der Analyse in einer Probeleitung in Deutschland wurde gezeigt, dass einen Zuwachs von ca. 28% der Stromtragfähigkeit eine Reduzierung der Kosten für Engpassmaßnahmen um ca. 55% bedeuten kann. Dieser Vorteil kann nur vom Netzbetreiber wahrgenommen werden, wenn eine Belastbarkeitsprognose für die Stromerzeugunsgplanung der konventionellen Kraftwerke zur Verfügung steht. Das in dieser Dissertation vorgestellte System prognostiziert die Belastbarkeit von Freileitungen für 48 Stunden, mit einer Verbesserung der Prognosegenauigkeit im Vergleich zum Stand-der-Technik von 6,13% in Durchschnitt. Der Ansatz passt die meteorologischen Vorhersagen an die lokale Wettersituation entlang der Leitung an. Diese Anpassungen sind aufgrund von Veränderungen der Topographie entlang der Leitungstrasse und Windschatten der umliegenden Bäume notwendig, da durch die meteorologischen Modelle diese nicht beschrieben werden können. Außerdem ist das in dieser Dissertation entwickelte Modell in der Lage die Genauigkeitsabweichungen der Wettervorhersage zwischen Tag und Nacht abzugleichen, was vorteilhaft für die Strombelastbarkeitsprognose ist. Die Zuverlässigkeit und deswegen auch die Effizienz des Stromerzeugungsplans für den nächsten 48 Stunden wurde um 10% gegenüber dem Stand der Technik erhöht. Außerdem wurde in Rahmen dieser Arbeit ein Verfahren für die Positionierung der Wetterstationen entwickelt, um die wichtigsten Stellen entlang der Leitung abzudecken und gleichzeitig die Anzahl der Wetterstationen zu minimieren. Wird ein verteiltes Sensornetzwerk in ganz Deutschland umgesetzt, wird die Einsparung von Redispatchingkosten eine Kapitalrendite von ungefähr drei Jahren bedeuten. Die Durchführung einer transienten Analyse ist im entwickelten System ebenfalls möglich, um Engpassfälle für einige Minuten zu lösen, ohne die maximale Leitertemperatur zu erreichen. Dieses Dokument versucht, die Vorteile der Freileitungsmonitoringssysteme zu verdeutlichen und stellt eine Lösung zur Unterstützung eines flexiblen elektrischen Netzes vor, die für eine erfolgreiche Energiewende erforderlich ist

Statistical Data Modeling and Machine Learning with Applications

The modeling and processing of empirical data is one of the main subjects and goals of statistics. Nowadays, with the development of computer science, the extraction of useful and often hidden information and patterns from data sets of different volumes and complex data sets in warehouses has been added to these goals. New and powerful statistical techniques with machine learning (ML) and data mining paradigms have been developed. To one degree or another, all of these techniques and algorithms originate from a rigorous mathematical basis, including probability theory and mathematical statistics, operational research, mathematical analysis, numerical methods, etc. Popular ML methods, such as artificial neural networks (ANN), support vector machines (SVM), decision trees, random forest (RF), among others, have generated models that can be considered as straightforward applications of optimization theory and statistical estimation. The wide arsenal of classical statistical approaches combined with powerful ML techniques allows many challenging and practical problems to be solved. This Special Issue belongs to the section “Mathematics and Computer Science”. Its aim is to establish a brief collection of carefully selected papers presenting new and original methods, data analyses, case studies, comparative studies, and other research on the topic of statistical data modeling and ML as well as their applications. Particular attention is given, but is not limited, to theories and applications in diverse areas such as computer science, medicine, engineering, banking, education, sociology, economics, among others. The resulting palette of methods, algorithms, and applications for statistical modeling and ML presented in this Special Issue is expected to contribute to the further development of research in this area. We also believe that the new knowledge acquired here as well as the applied results are attractive and useful for young scientists, doctoral students, and researchers from various scientific specialties

When less is more: How increasing the complexity of machine learning strategies for geothermal energy assessments may not lead toward better estimates

Previous moderate- and high-temperature geothermal resource assessments of the western United States utilized data-driven methods and expert decisions to estimate resource favorability. Although expert decisions can add confidence to the modeling process by ensuring reasonable models are employed, expert decisions also introduce human and, thereby, model bias. This bias can present a source of error that reduces the predictive performance of the models and confidence in the resulting resource estimates. Our study aims to develop robust data-driven methods with the goals of reducing bias and improving predictive ability. We present and compare nine favorability maps for geothermal resources in the western United States using data from the U.S. Geological Survey\u27s 2008 geothermal resource assessment. Two favorability maps are created using the expert decision-dependent methods from the 2008 assessment (i.e., weight-of-evidence and logistic regression). With the same data, we then create six different favorability maps using logistic regression (without underlying expert decisions), XGBoost, and support-vector machines paired with two training strategies. The training strategies are customized to address the inherent challenges of applying machine learning to the geothermal training data, which have no negative examples and severe class imbalance. We also create another favorability map using an artificial neural network. We demonstrate that modern machine learning approaches can improve upon systems built with expert decisions. We also find that XGBoost, a non-linear algorithm, produces greater agreement with the 2008 results than linear logistic regression without expert decisions, because the expert decisions in the 2008 assessment rendered the otherwise linear approaches non-linear despite the fact that the 2008 assessment used only linear methods. The F1 scores for all approaches appear low (F1 score \u3c 0.10), do not improve with increasing model complexity, and, therefore, indicate the fundamental limitations of the input features (i.e., training data). Until improved feature data are incorporated into the assessment process, simple non-linear algorithms (e.g., XGBoost) perform equally well or better than more complex methods (e.g., artificial neural networks) and remain easier to interpret

Symmetric and Asymmetric Data in Solution Models

This book is a Printed Edition of the Special Issue that covers research on symmetric and asymmetric data that occur in real-life problems. We invited authors to submit their theoretical or experimental research to present engineering and economic problem solution models that deal with symmetry or asymmetry of different data types. The Special Issue gained interest in the research community and received many submissions. After rigorous scientific evaluation by editors and reviewers, seventeen papers were accepted and published. The authors proposed different solution models, mainly covering uncertain data in multicriteria decision-making (MCDM) problems as complex tools to balance the symmetry between goals, risks, and constraints to cope with the complicated problems in engineering or management. Therefore, we invite researchers interested in the topics to read the papers provided in the book

Learning to forecast: The probabilistic time series forecasting challenge

We report on a course project in which students submit weekly probabilistic forecasts of two weather variables and one financial variable. This real-time format allows students to engage in practical forecasting, which requires a diverse set of skills in data science and applied statistics. We describe the context and aims of the course, and discuss design parameters like the selection of target variables, the forecast submission process, the evaluation of forecast performance, and the feedback provided to students. Furthermore, we describe empirical properties of students' probabilistic forecasts, as well as some lessons learned on our part