Prediction of global solar irradiance based on time series analysis: Application to solar thermal power plants energy production planning

Due to strong increase of solar power generation, the predictions of incoming solar energy are acquiring more importance. Photovoltaic and solar thermal are the main sources of electricity generation from solar energy. In the case of solar thermal energy plants with storage energy system, its management and operation need reliable predictions of solar irradiance with the same temporal resolution as the temporal capacity of the back-up system. These plants can work like a conventional power plant and compete in the energy stock market avoiding intermittence in electricity production.This work presents a comparisons of statistical models based on time series applied to predict half daily values of global solar irradiance with a temporal horizon of 3 days. Half daily values consist of accumulated hourly global solar irradiance from solar raise to solar noon and from noon until dawn for each day. The dataset of ground solar radiation used belongs to stations of Spanish National Weather Service (AEMet). The models tested are autoregressive, neural networks and fuzzy logic models. Due to the fact that half daily solar irradiance time series is non-stationary, it has been necessary to transform it to two new stationary variables (clearness index and lost component) which are used as input of the predictive models. Improvement in terms of RMSD of the models essayed is compared against the model based on persistence. The validation process shows that all models essayed improve persistence. The best approach to forecast half daily values of solar irradiance is neural network models with lost component as input, except Lerida station where models based on clearness index have less uncertainty because this magnitude has a linear behaviour and it is easier to simulate by models.     

Can implementation intentions and text messages promote brisk walking? A randomized trial.

Objective: To test the efficacy in promoting brisk walking of two theory-based interventions that incorporate implementation intentions and text message (Short Message Service; SMS) reminders directed at one’s walking-related plans or goals. Design: Participants (N = 149) were randomized to one of three conditions (implementation intention + SMS plan reminder, implementation intention + SMS goal reminder, control) before completing measures at baseline and follow-up 4 weeks later. At follow-up, the experimental groups were given a surprise recall task concerning their plans. All participants completed an equivalent goal recall task. Main Outcome Measures: Validated self-report measures of physical activity and measures of implementation intention and goal recall, weight, and waist-to-hip ratio. Results: Both intervention groups increased their brisk walking relative to the control group, without reducing other physical activity. The goal reminder group lost the most weight. The SMS plan reminder group recalled more of their plans than the SMS goal reminder group, but the latter were more successful in goal recall. Conclusion: Both interventions can promote brisk walking in sedentary populations. Text messages aid the recall of, and could enhance interventions that target, implementation intentions and goals. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

A total torque index for dynamic performance evaluation of a radial symmetric six-legged robot

This article focuses on the dynamic index and performance of a radial symmetric six-legged robot. At first the structure of the robot is described in brief and its inverse kinematics is presented. Then the dynamic model is formulated as based on the Lagrange equations. A novel index of total torque is proposed by considering the posture of the supporting legs. The new index can be used to optimize the leg’s structure and operation for consuming minimum power and avoiding unstable postures of the robot. A characterization of the proposed six-legged robot is obtained by a parametric analysis of robot performance through simulation using the presented dynamic model. Main influences are outlined as well as the usefulness of the proposed performance index.     

Machine learning methods for short‐term bid forecasting in the renewable energy market: A case study in Italy

In liberalized markets, there usually exists a day‐ahead session where energy is sold and acquired for the following production day. Owing to the high uncertainty of its production, renewable energy (wind in particular) can significantly influence the network imbalance of the following day. In this work, we consider the problem of predicting the sum of the bid volumes for wind energy of all the producers inside the day‐ahead energy market. This is a valuable tool to be used by an energy provider in order to determine the imbalance of a market zone and, thus, properly size its bids. In particular, we focus on the estimation of the possible relationship between the meteorological forecasts and the wind power offered on the market by the companies for a market zone. We propose a machine learning model which is used to compute a 1‐day‐ahead forecast. The input‐output mapping is obtained by support vector regression. The input feature vector is defined by a suitable feature extraction technique since the meteorological forecasts are given on a lattice of thousands of geographical points. The computational experiments are performed considering the Italian market as a case study (years 2012‐2016). The results show that the proposed feature extraction technique, selecting only some geographical zones, manages to reduce the error attained using all the features. Moreover, classical statistical methods are shown to be outperformed by machine learning models. The analysis reveals also some weaknesses of the model, which may be due to other nonmeteorological factors at play.     

Oxidation behaviour of Fe–40 at.-% Al intermetallics with Li or Cu additions at high temperature

Fe–40?at.-% Al intermetallics with the addition of Li (1 and 3?at.-%) and Cu (3 and 5?at.-%) were produced using standard casting techniques. The oxidation behaviour was obtained using thermogravimetric analyses in an atmosphere of 99.99% oxygen at 800, 900, 1000, and 1100°C. The oxidation product layers on the top and cross-sectional views were characterised using SEM and energy-dispersive X-ray spectroscopy mapping. The different alumina phases can produce a double layer with different mechanical and chemical properties. The results showed that the addition of 1% Li assisted the activation of the oxide production at 1000 and 1100°C, while the rest of the third element additions slightly modified the oxidation resistance.     

Application of inductively coupled plasma–mass spectrometry for trace element characterisation of equine meats

A validated and accredited analytical method of inductively coupled plasma–mass spectrometry was used to determine the levels of 22 trace elements in 52 equine meat samples collected during 2015. Greater amounts of Zn, Fe, and Ca were found with mean values of over 25 µg g^?1. Levels of non-essential trace elements, that is, Pb, Cd, Hg, and As, were generally low (mean values lower than 11.3 ng g^?1). Equine gender and geographic origin of meats (Italy and Poland) were compared, with no significant differences being found, whereas equine meats could be differentiated from bovine through a multivariate approach. As regards trace element accumulation, evaluated considering slaughter age, Zn, Fe, Ca, and Cr showed greater increase. Finally, a good correlation was obtained between two pairs of trace elements, Zn/Fe (r = 0.82) and Ca/Fe (r = 0.87).     

Prevalence,Distribution, and Diversity of Salmonella spp. in Meat Samples Collected from Italian Slaughterhouses

Recently worldwide food safety authorities indicated the rise of foodborne outbreaks linked to Salmonella: this highlighted the need to intensify monitoring and apply more targeted controls to help manage the spread of the disease. The aim of this study was to assess the prevalence and distribution of Salmonella serotypes in 7 slaughterhouses, located in different areas of Naples province (Regione Campania, Italy). Meat samples collected from the slaughterhouses were submitted for standardized microbiological analysis in 2015. Results of routine testing for Salmonella spp. were analyzed and then compared to biochemical and molecular evaluations. Salmonella spp. were detected in 12% of 320 samples examined (39/320) and the isolation rates ranged from 87% (32 samples) for raw poultry meat to 13% (7 samples) for pork meat. Biochemical serotyping showed that approximately 50% of the isolates belonged to Salmonella enterica serotype Choleraesuis. Rapid detection methods, such as molecular analysis (polymerase chain reaction and gel electrophoresis), able to confirm food matrices contamination, represent a valid support to the fast identification of Salmonella species. A further aspect of the study consisted, indeed, on analyzing isolated strains through molecular evaluations. By amplifying bacterial DNA—using invA primers, selective for Salmonella—it was possible, in less than 3 h, to classify the isolates as Salmonella spp., confirming the results of microbiological outcomes. Results of distribution analysis, supported by rapid molecular approaches, showed the difficulty of reducing Salmonella risk on food chain. This emphasized the importance of periodic surveillance to prevent outbreaks.