Flood Forecasting Using Machine Learning Methods

This book is a printed edition of the Special Issue Flood Forecasting Using Machine Learning Methods that was published in Wate

Advanced Mathematics and Computational Applications in Control Systems Engineering

Control system engineering is a multidisciplinary discipline that applies automatic control theory to design systems with desired behaviors in control environments. Automatic control theory has played a vital role in the advancement of engineering and science. It has become an essential and integral part of modern industrial and manufacturing processes. Today, the requirements for control precision have increased, and real systems have become more complex. In control engineering and all other engineering disciplines, the impact of advanced mathematical and computational methods is rapidly increasing. Advanced mathematical methods are needed because real-world control systems need to comply with several conditions related to product quality and safety constraints that have to be taken into account in the problem formulation. Conversely, the increment in mathematical complexity has an impact on the computational aspects related to numerical simulation and practical implementation of the algorithms, where a balance must also be maintained between implementation costs and the performance of the control system. This book is a comprehensive set of articles reflecting recent advances in developing and applying advanced mathematics and computational applications in control system engineering

Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

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

Model Identification and Robust Nonlinear Model Predictive Control of a Twin Rotor MIMO System

PhDThis thesis presents an investigation into a number of model predictive control (MPC) paradigms for a nonlinear aerodynamics test rig, a twin rotor multi-input multi-output system (TRMS). To this end, the nonlinear dynamic model of the system is developed using various modelling techniques. A comprehensive study is made to compare these models and to select the best one to be used for control design purpose. On the basis of the selected model, a state-feedback multistep Newton-type MPC is developed and its stability is addressed using a terminal equality constraint approach. Moreover, the state-feedback control approach is combined with a nonlinear state observer to form an output-feedback MPC. Finally, a robust MPC technique is employed to address the uncertainties of the system. In the modelling stage, analytical models are developed by extracting the physical equations of the system using the Newtonian and Lagrangian approaches. In the case of the black-box modelling, artificial neural networks (ANNs) are utilised to model the TRMS. Finally, the grey-box model is used to enhance the performance of the white-box model developed earlier through the optimisation of parameters using a genetic algorithm (GA) based approach. Stability analysis of the autonomous TRMS is carried out before designing any control paradigms for the system. In the control design stage, an MPC method is proposed for constrained nonlinear systems, which is the improvement of the multistep Newton-type control strategy. The stability of the proposed state-feedback MPC is guaranteed using terminal equality constraints. Moreover, the formerly proposed MPC algorithm is combined with an unscented Kalman filter (UKF) to formulate an output-feedback MPC. An extended Kalman filter (EKF) based on a state-dependent model is also introduced, whose performance is found to be better compared to that of the UKF. Finally, a robust MPC is introduced and implemented on the TRMS based on a polytopic uncertainty that is cast into linear matrix inequalities (LMI)

Mind out of matter: topics in the physical foundations of consciousness and cognition

This dissertation begins with an exploration of a brand of dual aspect monism and some problems deriving from the distinction between a first person and third person point of view. I continue with an outline of one way in which the conscious experience of the subject might arise from organisational properties of a material substrate. With this picture to hand, I first examine theoretical features at the level of brain organisation which may be required to support conscious experience and then discuss what bearing some actual attributes of biological brains might have on such experience. I conclude the first half of the dissertation with comments on information processing and with artificial neural networks meant to display simple varieties of the organisational features initially described abstractly.While the first half begins with a view of conscious experience and infers downwards in the organisational hierarchy to explore neural features suggested by the view, attention in the second half shifts towards analysing low level dynamical features of material substrates and inferring upwards to possible effects on experience. There is particular emphasis on clarifying the role of chaotic dynamics, and I discuss relationships between levels of description of a cognitive system and comment on issues of complexity, computability, and predictability before returning to the topic of representation which earlier played a central part in isolating features of brain organisation which may underlie conscious experience.Some themes run throughout the dissertation, including an emphasis on understanding experience from both the first person and the third person points of view and on analysing the latter at different levels of description. Other themes include a sustained effort to integrate the picture offered here with existing empirical data and to situate current problems in the philosophy of mind within the new framework, as well as an appeal to tools from mathematics, computer science, and cognitive science to complement the more standard philosophical repertoire

Reinforcement Learning based Adaptive Model Predictive Power Pinch Analysis Systems Level Energy Management Approach to Uncertainty in Isolated Hybrid Energy Storage Systems

Ph. D. ThesisHybrid energy storage systems (HESS) involves the integration of multiple energy storage technologies with different complementary characteristics which are significantly advantageous compared to a single energy storage system, and can greatly improve the reliability of intermittent renewable energy sources (RES). Aside from the advantages HESS offer, the control and coordination of the multiple energy storages and the vital elements of the system via an optimised energy management strategy (EMS) involves increased computational time. Nevertheless, a systems-level graphical EMS based on Power Pinch Analysis (PoPA) which is a low burden computational tool was recently proposed for HESS. In this respect, the EMS which effectively resolved deficit and excess energy objectives was effected via the graphical PoPA tool, the power grand composite curve (PGCC). PGCC is basically a plot of integrated energy demands and sources in the system as a function of time. Although of proven success, accounting for uncertainty with PoPA is a cogent research question due to the assumption of an ideal day ahead (DA) generation and load profiles forecast. Therefore, the proposition of several graphical and reinforcement learning based ‘adaptive’ PoPA EMSs in order to address the issue of uncertainty with PoPA, has been the major contribution of this thesis. Firstly, to counteract the combined effect of uncertainty with PoPA, an Adaptive PoPA EMS for a standalone HESS has been proposed. In the Adaptive PoPA, the PGCC was implemented within a receding horizon model predictive framework with the current output state of the energy storage (in this case the battery) used as control feedback to derive an updated sequence of EMS, inferred via PGCC shaping. Additionally, during the control and operation of the HESS, re-computation of the PGCC only occurs if a forecast uncertainty occurs such that the error between the real and estimated battery’s state of charge becomes greater than an arbitrarily chosen threshold value of 5%. Secondly a Kalman filter for the optimal estimation of uncertainty distributed as a normal Gaussian is integrated into the Adaptive PoPA in order to recursively predict the State of Charge of the battery based on the likelihood of uncertainty. Thus, the Kalman filter Adaptive PoPA by anticipating the effect of uncertainty offers an improved approach to the Adaptive PoPA particularly when the uncertainty is of a Gaussian distribution. The algorithm is therefore more sophisticated than the Adaptive PoPA but nevertheless computationally efficient and offers a preventive measure as an improvement. Furthermore, Tabular Dyna Q-learning algorithm, a subset of reinforcement learning which employs a learning agent to solve a discrete Markov Decision Process by maximising an expected reward in accordance with the Bellman optimality, is integrated within the Power Pinch Analysis. Thereafter, a deep neural network is used to approximate the Q-Learning Table. These aforementioned methods which have been highlighted in order of computational time can be deployed with only a minimal level of historical data requirements such as the average load profile or base load data and solar irradiance forecast to produce a deterministic solution. Nevertheless, this thesis proposed a probabilistic adaptive PoPA strategy based on a (recursive least square) Monte Carlo simulation chance constrained framework, in the event where there is sufficient amount of historical data such as the probability distribution of the uncertain model parameters. The probabilistic approach is no doubt more computationally intensive than the deterministic methods presented though it proffers a much more realistic solution to the problem of uncertainty. In order to enhance the probabilistic adaptive PoPA, an actor-critic deep neural network reinforcement learning agent is incorporated. The six methods are evaluated against the DA PoPA on an actual isolated HESS microgrid built in Greece with respect to the violation of the energy storage operating constraints and plummeting carbon emission footprint.Petroleum Technology Development Funds (PTDF