Solar Irradiance Forecasting Using Dynamic Ensemble Selection

Solar irradiance forecasting has been an essential topic in renewable energy generation. Forecasting is an important task because it can improve the planning and operation of photovoltaic systems, resulting in economic advantages. Traditionally, single models are employed in this task. However, issues regarding the selection of an inappropriate model, misspecification, or the presence of random fluctuations in the solar irradiance series can result in this approach underperforming. This paper proposes a heterogeneous ensemble dynamic selection model, named HetDS, to forecast solar irradiance. For each unseen test pattern, HetDS chooses the most suitable forecasting model based on a pool of seven well-known literature methods: ARIMA, support vector regression (SVR), multilayer perceptron neural network (MLP), extreme learning machine (ELM), deep belief network (DBN), random forest (RF), and gradient boosting (GB). The experimental evaluation was performed with four data sets of hourly solar irradiance measurements in Brazil. The proposed model attained an overall accuracy that is superior to the single models in terms of five well-known error metrics

Multistep prediction of dynamic uncertainty under limited data

Engineering systems are growing in complexity, requiring increasingly intelligent and flexible methods to account for and predict uncertainties in service. This paper presents a framework for dynamic uncertainty prediction under limited data (UPLD). Spatial geometry is incorporated with LSTM networks to enable real-time multistep prediction of quantitative and qualitative uncertainty over time. Validation is achieved through two case studies. Results demonstrate robust prediction of trends in limited and dynamic uncertainty data with parallel determination of geometric symmetry at each time unit. Future work is recommended to explore alternative network architectures suited to limited data scenarios.Engineering and Physical Sciences Research Council (EPSRC): 194431

Solar and wind quantity 24 h-series prediction using PDE-modular models gradually developed according to spatial pattern similarity

The design and implementation of efficient photovoltaic (PV) plants and wind farms require a precise analysis and definition of specifics in the region of interest. Reliable Artificial Intelligence (AI) models can recognize long-term spatial and temporal variability, including anomalies in solar and wind patterns, which are necessary to estimate the generation capacity and configuration parameters of PV panels and wind turbines. The proposed 24 h planning of renewable energy (RE) production involves an initial reassessment of the optimal day data records based on the spatial pattern similarity in the latest hours and their follow-up statistical AI learning. Conventional measurements comprise a larger territory to allow the development of robust models representing unsettled meteorological situations and their significant changes from a comprehensive aspect, which becomes essential in middle-term time horizons. Differential learning is a new unconventionally designed neurocomputing strategy that combines differentiated modules composed of selected binomial network nodes as the output sum. This approach, based on solutions of partial differential equations (PDEs) defined in selected nodes, enables us to comprise high uncertainty in nonlinear chaotic patterns, contingent upon RE local potential, without an undesirable reduction in data dimensionality. The form of back-produced modular compounds in PDE models is directly related to the complexity of large-scale data patterns used in training to avoid problem simplification. The preidentified day-sample series are reassessed secondary to the training applicability, one by one, to better characterize pattern progress. Applicable phase or frequency parameters (e.g., azimuth, temperature, radiation, etc.) are related to the amplitudes at each time to determine and solve particular node PDEs in a complex form of the periodic sine/cosine components. The proposed improvements contribute to better performance of the AI modular concept of PDE models, a cable to represent the dynamics of complex systems. The results are compared with the recent deep learning strategy. Both methods show a high approximation ability in radiation ramping events, often in PV power supply; moreover, differential learning provides more stable wind gust predictions without undesirable alterations in day errors, namely in over-break frontal fluctuations. Their day average percentage approximation of similarity correlation on real data is 87.8 and 88.1% in global radiation day-cycles and 46.7 and 36.3% in wind speed 24 h. series. A parametric C++ executable program with complete spatial metadata records for one month is available for free to enable another comparative evaluation of the conducted experiments.Web of Science163art. no. 108

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Significant Accomplishments in Science and Technology at Goddard Space Flight Center, 1969

Aerospace scientific and technological studies in 1969 for satellite systems and spacecraft mission

Mutual information based tracking with mobile sensors

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (p. 125-127).In order to utilize mobile sensor nodes in a sensing and estimation problem, one must carefully consider the optimal placement of those sensor nodes and simultaneously account for the cost incurred in moving the sensor nodes. We present an approximate dynamic programming approach to a tracking problem with mobile sensor nodes. We utilize mutual information as the objective for optimal sensor placement. We show how a constrained dynamic programming approach allows us to balance estimation quality against mobility costs. However this constrained optimization problem is NP-hard. We present a set of approximations that allow this dynamic program to be solved with polynomial complexity in the number of sensors. We present a greedy multiple time step planning algorithm that greedily selects the most informative paths over a fixed planning horizon. These approximation algorithms are verified via simulation to give a comparative analysis of estimate quality and mobility costs.by John A. Russ.S.M

Eine gitterfreie Raum-Zeit-Kollokationsmethode für gekoppelte Probleme auf Gebieten mit komplizierten Rändern

In der vorliegenden Arbeit wird eine neuartige gitterfreie Raum-Zeit-Kollokationsmethode (engl. STMCM) zur Lösung von Systemen partieller und gewöhnlicher Differentialgleichungen durch eine konsistente Diskretisierung in Raum und Zeit als Alternative zu den etablierten netzbasierten Verfahren vorgeschlagen. Die STMCM gehört zur Klasse der tatsächlich gitterfreien Methoden, die nur mit Punktwolken ohne a priori Netzkonnektivität arbeiten und kein Diskretisierungsnetz benötigen. Das Verfahren basiert auf der Interpolating Moving Least Squares Methode, die eine vereinfachte Erfüllung der Randbedingungen durch die von den Kernfunktionen erfüllte Kronecker-Delta-Eigenschaft ermöglicht, was beim größten Teil anderer netzfreier Verfahren nicht der Fall ist. Ein Regularisierungsverfahren zur Bewältigung des beim Aufbau der Kernfunktionen auftretenden Singularitätsproblems, sowie zur Berechnung aller benötigten Ableitungen der Kernfunktionen wird dargelegt. Ziel ist es dabei, eine Methode zu entwickeln, die die Einfachheit der Verfahren zur Lösung partieller Differentialgleichungen in starker Form mit den Vorteilen der gitterfreien Verfahren, insbesondere mit Blick auf gekoppelte Probleme des Ingenieurwesens mit sich bewegenden Grenzflächen, verknüpft. Die vorgeschlagene Methode wird zunächst zur Lösung linearer und nichtlinearer partieller sowie gewöhnlicher Differentialgleichungen angewendet. Dabei werden deren Konvergenz- und Genauigkeitseigenschaften untersucht. Die implizite Rekonstruktion der Gebiete mit komplizierten Rändern als Abbildungsstrategie zur Punktwolken-Streuung wird durch die Interpolation von Punktwolkendaten in zwei und drei Raumdimensionen demonstriert. Anhand der Modelle zur Simulation von Biofilm- und Tumor-Wachstumsprozessen werden Anwendungsbeispiele aus dem Bereich der Umweltwissenschaften und der Medizintechnik dargestellt.In this thesis an innovative Space-Time Meshfree Collocation Method (STMCM) for solving systems of nonlinear ordinary and partial differential equations by a consistent discretization in both space and time is proposed as an alternative to established mesh-based methods. The STMCM belongs to the class of truly meshfree methods, i.e. the methods which do not have any underlying mesh, but work on a set of nodes only without an a priori node-to-node connectivity. The STMCM is constructed using the Interpolating Moving Least Squares technique, allowing a simplified implementation of boundary conditions due to fulfilment of the Kronecker delta property by the kernel functions, which is not the case for the major part of other meshfree methods. A regularization technique to overcome the singularity-by-construction problem and compute all necessary derivatives of the kernel functions is presented. The goal is to design a method that combines the simplicity and straightforwardness of the strong-form computational techniques with the advantages of meshfree methods over the classical ones, especially for coupled engineering problems involving moving interfaces. The proposed STMCM is applied to linear and nonlinear partial and ordinary differential equations of different types and its accuracy and convergence properties are studied. The power of the technique is demonstrated by implicit reconstruction of domains with complex boundaries via interpolation of point cloud data in two and three space dimensions as a `mapping' strategy for distribution of computational points within such domains. Applications from the fields of environmental and medical engineering are presented by means of a mathematical model for simulating a biofilm growth and a nonlinear model of tumour growth processes

Application of machine learning in operational flood forecasting and mapping

Considering the computational effort and expertise required to simulate 2D hydrodynamic models, it is widely understood that it is practically impossible to run these types of models during a real-time flood event. To allow for real-time flood forecasting and mapping, an automated, computationally efficient and robust data driven modelling engine - as an alternative to the traditional 2D hydraulic models - has been proposed. The concept of computationally efficient model relies heavily on replacing time consuming 2D hydrodynamic software packages with a simplified model structure that is fast, reliable and can robustly retains sufficient accuracy for applications in real-time flood forecasting, mapping and sequential updating. This thesis presents a novel data-driven modelling framework that uses rainfall data from meteorological stations to forecast flood inundation maps. The proposed framework takes advantage of the highly efficient machine learning (ML) algorithms and also utilities the state-of-the-art hydraulic models as a system component. The aim of this research has been to develop an integrated system, where a data-driven rainfall-streamflow forecasting model sets up the upstream boundary conditions for the machine learning based classifiers, which then maps out multi-step ahead flood extents during an extreme flood event. To achieve the aim and objectives of this research, firstly, a comprehensive investigation was undertaken to search for a robust ML-based multi-step ahead rainfall-streamflow forecasting model. Three potential models were tested (Support Vector Regression (SVR), Deep Belief Network (DBN) and Wavelet decomposed Artificial Neural Network (WANN)). The analysis revealed that SVR-based models perform most efficiently in forecasting streamflow for shorter lead time. This study also tested the portability of model parameters and performance deterioration rates. Secondly, multiple ML-based models (SVR, Random Forest (RF) and Multi-layer Perceptron (MLP)) were deployed to simulate flood inundation extents. These models were trained and tested for two geomorphologically distinct case study areas. In the first case of study, of the models trained using the outputs from LISFLOOD-FP hydraulic model and upstream flow data for a large rural catchment (Niger Inland Delta, Mali). For the second case of study similar approach was adopted, though 2D Flood Modeller software package was used to generate target data for the machine learning algorithms and to model inundation extent for a semi-urban floodplain (Upton-Upon-Severn, UK). In both cases, machine learning algorithms performed comparatively in simulating seasonal and event based fluvial flooding. Finally, a framework was developed to generate flood extent maps from rainfall data using the knowledge learned from the case studies. The research activity focused on the town of Upton-Upon-Severn and the analysis time frame covers the flooding event of October-November 2000. RF-based models were trained to forecast the upstream boundary conditions, which were systematically fed into MLP-based classifiers. The classifiers detected states (wet/dry) of the randomly selected locations within a floodplain at every time step (e.g. one hour in this study). The forecasted states of the sampled locations were then spatially interpolated using regression kriging method to produce high resolution probabilistic inundation (9m) maps. Results show that the proposed data centric modelling engine can efficiently emulate the outcomes of the hydraulic model with considerably high accuracy, measured in terms of flood arrival time error, and classification accuracy during flood growing, peak, and receding periods. The key feature of the proposed modelling framework is that, it can substantially reduce computational time, i.e. ~14 seconds for generating flood maps for a flood plain of ~4 km2 at 9m spatial resolution (which is significantly low compared to a fully 2D hydrodynamic model run time)