Hugo T.C. Pedro

A Smart Image-Based Cloud Detection System for Intrahour Solar Irradiance Forecasts

AbstractThis study proposes an automatic smart adaptive cloud identification (SACI) system for sky imagery and solar irradiance forecast. The system is deployed using off-the-shelf fish-eye cameras that offer substantial advantages in terms of cost when compared to industry-standard sky imagers. SACI uses a smart image categorization (SIC) algorithm that combines the sky images and solar irradiance measurements to classify sky conditions into three categories: clear, overcast, and partly cloudy. A cloud detection scheme, optimized for each image category, is used to quantify cloud cover from the sky images. SACI is optimized and validated against manually annotated images. Results show that SACI achieves overall classification accuracy higher than 90% and outperforms reference cloud detection methods. Cloud cover retrieved by SACI is used as an input for an artificial neural network (ANN) model that predicts 1-min average global horizontal irradiance (GHI), 5-, 10-, and 15-min ahead of time. The performan...

Chapter 15 – Stochastic-Learning Methods

In this chapter, we discuss nonlinear regression and stochastic-learning methods for solar forecasting. A detailed comparison of nonstationary regression methods and different stochastic-learning methods based on kNN, ANN, and GA is presented. A hybrid GA/ANN method emerges as the most robust stochastic candidate to be used as the basis for development of high-fidelity forecast engines. We illustrate different applications of stochastic-learning by considering univariate and multivariate inputs, and we highlight some of the robust qualities of stochastic-learning for a wide range of time horizons.

Mathematical methods for optimized solar forecasting

Abstract The higher penetration of renewable resources in the energy portfolios accentuates the need for accurate forecasting of variable resources (solar, wind, tidal) at several different temporal scales to achieve power grid balance. Solar generation technologies have experienced strong energy market growth in the past few years, with corresponding increase in local grid penetration rates. As is the case with wind, the solar resource at the ground level is highly variable mostly due to cloud cover variability, atmospheric aerosol levels, and indirectly, and to a lesser extent, participating gases in the atmosphere. The inherent variability of solar generation at higher grid penetration levels poses problems associated with the cost of reserves, dispatchable and ancillary generation, and grid reliability in general. As a result, high accuracy forecast systems are required for multiple time horizons that are associated with regulation, dispatching, scheduling, and unit commitment. Here we review the theory behind mathematical methods for optimized solar forecasting and a number of successful applications of solar forecasting methods for both the solar resource and the power output of solar plants at the utility scale level.

On Biomimetic Engineering Design

We realized long ago that across the natural world it is possible to find structures, materials, and processes that are of great interest for human applications. In fact, attempts to copy nature to obtain new technologies or improve existing ones go back thousands of years, from the Chinese trying to make artificial silk to Leonardo da Vincis designs of flying machines inspired by the flight of birds. As our ability to explore nature increased, so has the perception that we are surrounded by natural designs that surpass most of our own and that great benefits can be achieved by mimicking nature.

Optimization of Cellular Structures Using Map L-Systems

The present work concerns the formal evolutionary development of complex structural engineering systems using a biologically inspired evolutionary method. The bioinspired method presented in this study uses Map Lindenmayer systems, or more specifically mBPMOL-systems, which create single layered cellular structures, to mimic the biological process of cellular division. The problem here reported is a very simple one, which allows for the proof of concept in a very clear manner. It consists in finding the optimal cellular structure for a plate loaded transversely. The structures created with the mBPMOL-systems are then analyzed using the finite element method (FEM) to determine its structural performance. An evolutionary algorithm, such as the genetic algorithm is used to evolve the topology of the cellular structures.

History Forces in Oscillating Convective Flow Past a Fixed Particle

The determination of a consistent equation of motion for spherical particles in finite and infinitesimal Reynolds numbers is a classical problem in fluid mechanics. A number of keystone steps to develop and manipulate such Lagrangian equations of motion have been discussed in a steady stream of papers, starting at the end of the 19th century. The problem is of great complexity even for the limit of infinitesimal Reynolds numbers when both low and high frequency flows are considered. The complexity of the problem is evident when one considers that the surrounding fluid is continuously being deformed by the presence of the particle and therefore is continuously responding to particle accelerations according to its own inertialviscous balance. This intricate interaction translates into a striking feature: the so called history or Basset force. This force, which accounts for the effects of the local acceleration of the flow, is represented by an integro-differential operator. Coimbra and Rangel were the first to realize that the Basset force can ∗Graduate student, Department of Mechanical Engineering, 2540 Dole Street – Holmes Hall 302. †Assistant Professor, Department of Mechanical Engineering/SMA, Av. Rovisco Pais,1. ‡Associate Professor, Department of Mechanical Engineering, 2540 Dole Street – Holmes Hall 302. §Assistant Professor, Department of Mechanical Engineering, 2540 Dole Street – Holmes Hall 302. Copyright c