The impact of poor power quality in solar photovoltaic microgeneration: technical problems and losses of revenue

This paper presents a critical and quantitative analysis of the influence of the Power Quality in grid connected solar photovoltaic microgeneration installations. First are introduced the main regulations and legislation related with the solar photovoltaic microgeneration, in Portugal and Europe. Next are presented Power Quality monitoring results obtained from two residential solar photovoltaic installations located in the north of Portugal, and is explained how the Power Quality events affect the operation of these installations. Afterwards, it is described a methodology to estimate the energy production losses and the impact in the revenue caused by the abnormal operation of the electrical installation. This is done by comparing the amount of energy that was injected into the power grid with the theoretical value of energy that could be injected in normal conditions. The performed analysis shows that Power Quality severally affects the solar photovoltaic installations operation. The losses of revenue in the two monitored installations M1 and M2 are estimated in about 27% and 22%, respectively.

Using customer lifetime value to improve the prediction of bank deposit subscription in telemarketing campaigns

Customer lifetime value (LTV) enables using client characteristics, such as recency, frequency and monetary (RFM) value, to describe the value of a client through time in terms of profitability. We present the concept of LTV applied to telemarketing for improving the return-on-investment, using a recent (from 2008 to 2013) and real case study of bank campaigns to sell long- term deposits. The goal was to benefit from past contacts history to extract additional knowledge. A total of twelve LTV input variables were tested, un- der a forward selection method and using a realistic rolling windows scheme, highlighting the validity of five new LTV features. The results achieved by our LTV data-driven approach using neural networks allowed an improvement up to 4 pp in the Lift cumulative curve for targeting the deposit subscribers when compared with a baseline model (with no history data). Explanatory knowledge was also extracted from the proposed model, revealing two highly relevant LTV features, the last result of the previous campaign to sell the same product and the frequency of past client successes. The obtained results are particularly valuable for contact center companies, which can improve pre- dictive performance without even having to ask for more information to the companies they serve.

Jet energy measurement and its systematic uncertainty in proton-proton collisions at s√=7 TeV with the ATLAS detector

The jet energy scale (JES) and its systematic uncertainty are determined for jets measured with the ATLAS detector using proton–proton collision data with a centre-of-mass energy of s√=7 TeV corresponding to an integrated luminosity of 4.7 fb −1 . Jets are reconstructed from energy deposits forming topological clusters of calorimeter cells using the anti- kt algorithm with distance parameters R=0.4 or R=0.6 , and are calibrated using MC simulations. A residual JES correction is applied to account for differences between data and MC simulations. This correction and its systematic uncertainty are estimated using a combination of in situ techniques exploiting the transverse momentum balance between a jet and a reference object such as a photon or a Z boson, for 20≤pjetT<1000 GeV and pseudorapidities |η|<4.5 . The effect of multiple proton–proton interactions is corrected for, and an uncertainty is evaluated using in situ techniques. The smallest JES uncertainty of less than 1 % is found in the central calorimeter region ( |η|<1.2 ) for jets with 55≤pjetT<500 GeV . For central jets at lower pT , the uncertainty is about 3 %. A consistent JES estimate is found using measurements of the calorimeter response of single hadrons in proton–proton collisions and test-beam data, which also provide the estimate for pjetT>1 TeV. The calibration of forward jets is derived from dijet pT balance measurements. The resulting uncertainty reaches its largest value of 6 % for low- pT jets at |η|=4.5 . Additional JES uncertainties due to specific event topologies, such as close-by jets or selections of event samples with an enhanced content of jets originating from light quarks or gluons, are also discussed. The magnitude of these uncertainties depends on the event sample used in a given physics analysis, but typically amounts to 0.5–3 %.

Extreme value Birnbaum-Saunders regression models applied to environmental data

Extreme value models are widely used in different areas. The Birnbaum–Saunders distribution is receiving considerable attention due to its physical arguments and its good properties. We propose a methodology based on extreme value Birnbaum–Saunders regression models, which includes model formulation, estimation, inference and checking. We further conduct a simulation study for evaluating its performance. A statistical analysis with real-world extreme value environmental data using the methodology is provided as illustration.

“Learning-by-producing” in the fashion value chain

Proceedings da AUTEX 2015, Bucareste, Roménia.

Nutritional value, bioactive compounds and antioxidant properties of three edible mushrooms from Poland

Mushrooms contain a multitude of biomolecules with nutritional and/or biological activity. Among the bioactive molecules, phenolic compounds and tocopherols are the most responsible for their antioxidant activity. In the present work, Boletus edulis, Lentinus edodes and Xerocomus badius, three edible mushroom species originated from Poland, were analyzed for their chemical composition and antioxidant activity. Carbohydrates were the most abundant macronutrients, followed by proteins and ash. Fructose, mannitol and trehalose were the prevalent sugars, but glucose was only found in B. edulis. Polyunsaturated fatty acids predominated over mono and saturated fatty acids. Palmitic, oleic and linoleic acids were abundant in the three samples. α- and β- Tocopherols were quantified in all the samples, but γ-tocopherol was only identified in X. badius. Oxalic and fumaric acids were quantified in the three samples; quinic acid was only present in L. edodes, and malic and citric acids were only found in X. badius. p-Hydroxybenzoic, protocatechuic and cinnamic acids were quantified in all the species, while p-coumaric acid was only found in B. edulis. This species and X. badius revealed the highest antioxidant properties, being B. edulis more effective in radicals scavenging activity and reducing power, and X. badius in lipid peroxidation inhibition, which is related with the highest amounts in phenolic compounds and tocopherols, respectively.

Automatically estimating iSAX parameters

The Symbolic Aggregate Approximation (iSAX) is widely used in time series data mining. Its popularity arises from the fact that it largely reduces time series size, it is symbolic, allows lower bounding and is space efficient. However, it requires setting two parameters: the symbolic length and alphabet size, which limits the applicability of the technique. The optimal parameter values are highly application dependent. Typically, they are either set to a fixed value or experimentally probed for the best configuration. In this work we propose an approach to automatically estimate iSAX’s parameters. The approach – AutoiSAX – not only discovers the best parameter setting for each time series in the database, but also finds the alphabet size for each iSAX symbol within the same word. It is based on simple and intuitive ideas from time series complexity and statistics. The technique can be smoothly embedded in existing data mining tasks as an efficient sub-routine. We analyze its impact in visualization interpretability, classification accuracy and motif mining. Our contribution aims to make iSAX a more general approach as it evolves towards a parameter-free method.

Lewis Score - Prognostic value in patients with isolated small bowel Crohn's disease

Background and aims: Small bowel capsule endoscopy (SBCE) allows mapping of small bowel inflammation in Crohn’s disease (CD). We aimed to assess the prognostic value of the severity of inflammatory lesions, quantified by the Lewis score (LS), in patients with isolated small bowel CD. Methods: A retrospective study was performed in which 53 patients with isolated small bowel CD were submitted to SBCE at the time of diagnosis. The Lewis score was calculated and patients had at least 12 months of follow-up after diagnosis. As adverse events we defined disease flare requiring systemic corticosteroid therapy, hospitalization and/or surgery during follow-up. We compared the incidence of adverse events in 2 patient subgroups, i.e. those with moderate or severe inflammatory activity (LS =790) and those with mild inflammatory activity (135 = LS < 790). Results: The LS was =790 in 22 patients (41.5%), while 58.5% presented with LS between 135 and 790. Patients with a higher LS were more frequently smokers (p = 0.01), males (p = 0017) and under immunosuppressive therapy (p = 0.004). In multivariate analysis, moderate to severe disease at SBCE was independently associated with corticosteroid therapy during follow-up, with a relative risk (RR) of 5 (p = 0.011; 95% confidence interval [CI] 1.5–17.8), and for hospitalization, with an RR of 13.7 (p = 0 .028; 95% CI 1.3–141.9). Conclusion: In patients with moderate to severe inflammatory activity there were higher prevalences of corticosteroid therapy demand and hospitalization during follow-up. Thus, stratifying the degree of small bowel inflammatory activity with SBCE and LS calculation at the time of diagnosis provided relevant prognostic value in patients with isolated small bowel CD.

Exploitation of Ashbya gossypii for the production of high-value products from glycerol feedstocks

Dissertação de mestrado em Bioengenharia