Photovoltaic power forecast modeling with artificial neural networks

Dissertação de Mestrado, Engenharia Eletrónica e Telecomunicações, Faculdade de Ciências e Tecnologia, Universidade do Algarve, 2016

VERIFYING SUCCESS OF ARTIFICIAL REEFS IN THE HURON-ERIE CORRIDOR FOR LAKE STURGEON

Lake sturgeon (Acipenser fulvescens) were historically abundant in the Huron-Erie Corridor (HEC), a 160 km river/channel network composed of the St. Clair River, Lake St. Clair, and the Detroit River that connects Lake Huron to Lake Erie. In the HEC, most natural lake sturgeon spawning substrates have been eliminated or degraded as a result of channelization and dredging. To address significant habitat loss in HEC, multi-agency restoration efforts are underway to restore spawning substrate by constructing artificial spawning reefs. The main objective of this study was to conduct post-construction monitoring of lake sturgeon egg deposition and larval emergence near two of these artificial reef projects; Fighting Island Reef in the Detroit River, and Middle Channel Spawning Reef in the lower St. Clair River. We also investigated seasonal and nightly timing of larval emergence, growth, and vertical distribution in the water column at these sites, and an additional site in the St. Clair River where lake sturgeon are known to spawn on a bed of ~100 year old coal clinkers. From 2010-12, we collected viable eggs and larvae at all three sites indicating that these artificial reefs are creating conditions suitable for egg deposition, fertilization, incubation, and larval emergence. The construction methods and materials, and physical site conditions present in HEC artificial reef projects can be used to inform future spawning habitat restoration or enhancement efforts. The results from this study have also identified the likelihood of additional uncharacterized natural spawning sites in the St. Clair River. In addition to the field study, we conducted a laboratory experiment involving actual substrate materials that have been used in artificial reef construction in this system. Although coal clinkers are chemically inert, some trace elements can be reincorporated with the clinker material during the combustion process. Since lake sturgeon eggs and larvae are developing in close proximity to this material, it is important to measure the concentration of potentially toxic trace elements. This study focused on arsenic, which occurs naturally in coal and can be toxic to fishes. Total arsenic concentration was measured in samples taken from four substrate treatments submerged in distilled water; limestone cobble, rinsed limestone cobble, coal clinker, and rinsed coal clinker. Samples were taken at three time intervals: 24 hours, 11 days, and 21 days. ICP-MS analysis showed that concentrations of total arsenic were below the EPA drinking water standard (10 ppb) for all samples. However, at the 24 hour sampling interval, a two way repeated measures ANOVA with a Holm-Sidak post hoc analysis (α= 0.05) showed that the mean arsenic concentration was significantly higher in the coal clinker substrate treatment then in the rinsed coal clinker treatment (p=0.006), the limestone cobble treatment (p

Optimal control of non-linear systems through hybrid cell-mapping/artificial-neural-networks techniques

In this paper, a real-time optimal control technique for non-linear plants is proposed. The control system makes use of the cell-mapping (CM) techniques, widely used for the global analysis of highly non-linear systems. The CM framework is employed for designing approximate optimal controllers via a control variable discretization. Furthermore, CM-based designs can be improved by the use of supervised feedforward artificial neural networks (ANNs), which have proved to be universal and efficient tools for function approximation, providing also very fast responses. The quantitative nature of the approximate CM solutions fits very well with ANNs characteristics. Here, we propose several control architectures which combine, in a different manner, supervised neural networks and CM control algorithms. On the one hand, different CM control laws computed for various target objectives can be employed for training a neural network, explicitly including the target information in the input vectors. This way, tracking problems, in addition to regulation ones, can be addressed in a fast and unified manner, obtaining smooth, averaged and global feedback control laws. On the other hand, adjoining CM and ANNs are also combined into a hybrid architecture to address problems where accuracy and real-time response are critical. Finally, some optimal control problems are solved with the proposed CM, neural and hybrid techniques, illustrating their good performance.

Digital soil mapping using reference area and artificial neural networks.

Digital soil mapping is an alternative for the recognition of soil classes in areas where pedological surveys are not available. The main aim of this study was to obtain a digital soil map using artificial neural networks (ANN) and environmental variables that express soillandscape relationships. This study was carried out in an area of 11,072 ha located in the Barra Bonita municipality, state of São Paulo, Brazil. A soil survey was obtained from a reference area of approximately 500 ha located in the center of the area studied. With the mapping units identified together with the environmental variables elevation, slope, slope plan, slope profile, convergence index, geology and geomorphic surfaces, a supervised classification by ANN was implemented. The neural network simulator used was the Java NNS with the learning algorithm "back propagation." Reference points were collected for evaluating the performance of the digital map produced. The occurrence of soils in the landscape obtained in the reference area was observed in the following digital classification: medium-textured soils at the highest positions of the landscape, originating from sandstone, and clayey loam soils in the end thirds of the hillsides due to the greater presence of basalt. The variables elevation and slope were the most important factors for discriminating soil class through the ANN. An accuracy level of 82% between the reference points and the digital classification was observed. The methodology proposed allowed for a preliminary soil classification of an area not previously mapped using mapping units obtained in a reference area

Time-Dependent Density Functional Molecular Orbital and Excited State Calculations on Bis(porphyrinyl)butadiynes in the Monocationic, Neutral, Monoanionic, and Dianionic Oxidation States

We report a theoretical study of the multiple oxidation states (1+, 0, 1−, and 2−) of a meso,meso-linked diporphyrin, namely bis[10,15,20-triphenylporphyrinatozinc(II)-5-yl]butadiyne (4), using Time-Dependent Density Functional Theory (TDDFT). The origin of electronic transitions of singlet excited states is discussed in comparison to experimental spectra for the corresponding oxidation states of the close analogue bis{10,15,20-tris[3‘,5‘-di-tert-butylphenyl]porphyrinatozinc(II)-5-yl}butadiyne (3). The latter were measured in previous work under in situ spectroelectrochemical conditions. Excitation energies and orbital compositions of the excited states were obtained for these large delocalized aromatic radicals, which are unique examples of organic mixed-valence systems. The radical cations and anions of butadiyne-bridged diporphyrins such as 3 display characteristic electronic absorption bands in the near-IR region, which have been successfully predicted with use of these computational methods. The radicals are clearly of the “fully delocalized” or Class III type. The key spectral features of the neutral and dianionic states were also reproduced, although due to the large size of these molecules, quantitative agreement of energies with observations is not as good in the blue end of the visible region. The TDDFT calculations are largely in accord with a previous empirical model for the spectra, which was based simplistically on one-electron transitions among the eight key frontier orbitals of the C4 (1,4-butadiyne) linked diporphyrins.