Local-Global Methods for Generalised Solar Irradiance Forecasting

As the use of solar power increases, having accurate and timely forecasts will be essential for smooth grid operators. There are many proposed methods for forecasting solar irradiance / solar power production. However, many of these methods formulate the problem as a time-series, relying on near real-time access to observations at the location of interest to generate forecasts. This requires both access to a real-time stream of data and enough historical observations for these methods to be deployed. In this paper, we propose the use of Global methods to train our models in a generalised way, enabling them to generate forecasts for unseen locations. We apply this approach to both classical ML and state of the art methods. Using data from 20 locations distributed throughout the UK and widely available weather data, we show that it is possible to build systems that do not require access to this data. We utilise and compare both satellite and ground observations (e.g. temperature, pressure) of weather data. Leveraging weather observations and measurements from other locations we show it is possible to create models capable of accurately forecasting solar irradiance at new locations. This could facilitate use planning and optimisation for both newly deployed solar farms and domestic installations from the moment they come online. Additionally, we show that training a single global model for multiple locations can produce a more robust model with more consistent and accurate results across locations.Comment: 40 pages, 11 figure

A genetic algorithm with composite chromosome for shift assignment of part-time employees

Personnel scheduling problems involve multiple tasks, including assigning shifts to workers. The purpose is usually to satisfy objectives and constraints arising from management, labour unions and employee preferences. The shift assignment problem is usually highly constrained and difficult to solve. The problem can be further complicated (i) if workers have mixed skills; (ii) if the start/end times of shifts are flexible; and (iii) if multiple criteria are considered when evaluating the quality of the assignment. This paper proposes a genetic algorithm using composite chromosome encoding to tackle the shift assignment problem that typically arises in retail stores, where most employees work part-time, have mixed-skills and require flexible shifts. Experiments on a number of problem instances extracted from a real-world retail store, show the effectiveness of the proposed approach in finding good-quality solutions. The computational results presented here also include a comparison with results obtained by formulating the problem as a mixed-integer linear programming model and then solving it with a commercial solver. Results show that the proposed genetic algorithm exhibits an effective and efficient performance in solving this difficult optimisation problem

Aging and Autism Spectrum Disorder: Evidence from the Broad Autism Phenotype

This study investigated for the first time the Broad Autism Phenotype (BAP) in the context of older adulthood and its associations with real world executive function, social support, and both depression and anxiety symptomatology. Based on self-ratings of autistic traits, 66 older adults (60+ years old, range=61-88) were split into BAP (n=20) and control (n=46) groups. Individuals in the BAP group, even after controlling for age, education level, sex, and health problems, exhibited more real world executive function problems in multiple domains, reported lower levels of social support, and self-rated increased depression and anxiety symptomatology compared to the control group. Regression analysis revealed that level of social support was the strongest predictor of BAP traits across both groups, although real world executive function problems and depression symptomatology were also significant predictors. Moreover, when predicting anxiety and depression symptomatology, BAP traits were the strongest predictors above and beyond the effects of demographic factors, real world executive function problems, and social support levels. These findings suggest that the BAP in older adulthood imparts additional risks to areas of functioning that are known to be crucial to aging-related outcomes in the context of typical development. These results might in turn inform aging in autism spectrum disorder, which has been largely unexplored to date

Local-Global Methods for Generalised Solar Irradiance Forecasting

For efficient operation, solar power operators often require generation forecasts for multiple sites with varying data availability. Many proposed methods for forecasting solar irradiance / solar power production formulate the problem as a time-series, using current observations to generate forecasts. This necessitates a real-time data stream and enough historical observations at every location for these methods to be deployed. In this paper, we propose the use of Global methods to train gen-eralised models. Using data from 20 locations distributed throughout the UK, we show that it is possible to learn models without access to data for all locations, enabling them to generate forecasts for unseen locations. We show a single Global model trained on multiple locations can produce more consistent and accurate results across locations. Furthermore, by leveraging weather observations and measurements from other locations we show it is possible to create models capable of accurately forecasting irradiance at locations without any real-time data. We apply our approaches to both classical and 1 Springer Nature 2021 L A T E X template 2 Local-Global Methods for Generalised Solar Irradiance Forecasting state-of-the-art Machine Learning methods, including a Transformer architecture. We compare models using satellite imagery or point observations (temperature, pressure, etc.) as weather data. These methods could facilitate planning and optimisation for both newly deployed solar farms and domestic installations from the moment they come online

Estado y política . Crítica e interpretación. Homenaje a Bolívar Echeverría

Mesa redonda /// Ziranda. Crítica e interpretación. Homenaje a Bolívar Echeverría. Participan: Marco Aurelio García, Isaac García Venegas , Julio Echeverría. Modera: Josu Landa. 30 de septiembre de 2010, Aula Magna. /// Semestre 2011-1. Cátedra Extraordinaria. Maestros del Exilio Español. Secretaría Académica, Secretaría de Extensión Académica, Comisión Organizadora: Raquel Segur, Isaac García Venegas, Carlos Oliva y Diana Fuentes. Facultad de Filosofía y Letras, Universidad Nacional Autónoma de México. //// Audio. Duración: 1 hora 28 minutos. //// Descargar: Pulse botón derecho del ratón sobre el archivo de audio "mp3" y seleccione "Guardar Destino Como...

Anticonvulsant Effect of Turmeric and Resveratrol in Lithium/Pilocarpine-Induced Status Epilepticus in Wistar Rats

Epilepsy is a chronic neurological disorder that lacks a cure. The use of plant-derived antioxidant molecules such as those contained in turmeric powder and resveratrol may produce short-term anticonvulsant effects. A total of 42 three-month-old male Wistar rats were divided into six groups (n = 7 in each group): Vehicle (purified water), turmeric (150 and 300 mg/kg, respectively), and resveratrol (30 and 60 mg/kg, respectively), administered per os (p.o.) every 24 h for 35 days. Carbamazepine (300 mg/kg/5 days) was used as a pharmacological control for anticonvulsant activity. At the end of the treatment, status epilepticus was induced using the lithium–pilocarpine model [3 mEq/kg, intraperitoneally (i.p.) and 30 mg/kg subcutaneously (s.c.), respectively]. Seizures were evaluated using the Racine scale. The 300 mg/kg of turmeric and 60 mg/kg of resveratrol groups had an increased latency to the first generalized seizure. The groups treated with 150 and 300 mg/kg of turmeric and 60 mg/kg of resveratrol also had an increased latency to status epilepticus and a decreased number of generalized seizures compared to the vehicle group. The chronic administration of turmeric and resveratrol exerts anticonvulsant effects without producing kidney or liver damage. This suggests that both of these natural products of plant origin could work as adjuvants in the treatment of epilepsy

The Effect of Rapid Heating and Fast Cooling on the Transformation Behavior and Mechanical Properties of an Advanced High Strength Steel (AHSS)

The major goal of this work was to study the effect of rapid heating and fast cooling on the transformation behavior of 22MnB5 steel. The effect of the initial microstructure (ferrite + pearlite or fully spheroidized) on the transformation behavior of austenite (during intercritical and supercritical annealing) in terms of heating rates (2.5, 30 & 200 °C/s) and fast cooling, i.e., 300 °C/s rate, were studied. As expected, the kinetics of austenite nucleation and growth were strongly related to the heating rates. Similarly, the carbon content of the austenite was higher at lower intercritical annealing temperatures, particularly when slower heating rates were used. The supercritical temperatures used in this study were similar to those used during commercial hot stamping operations, i.e., 845 and 895 °C, respectively, followed by a fast cooling rate. The prior austenite grain size (PAGS) was not strongly influenced by the nature of the initial microstructure, heating rate, reheating temperatures (845 or 895 °C), at 30 s holding time. The decomposition of austenite using fast cooling rates was examined. The results showed that 100% martensite was not obtained. The observed low temperature transformation products consisted of mixtures of martensite-bainite plus undissolved Fe3C carbides and small amounts of martensite-austenite (M-A). At higher supercritical temperatures, i.e., 1000 °C and 1050 °C, the final microstructure showed an increase in the volume fraction of martensite and a decrease in the volume fraction of bainite. The Fe3C and the M-A microconstituent were not observed. The best combination of tensile properties was obtained on samples reheated in the lower temperature range (845 to 895 °C). Interestingly, when the samples where reheated at the higher temperature range (1000 to 1050 °C) and fast cooled, the results of the mechanical properties did not exhibit significantly higher strength levels independent of heating rate or initial microstructural condition. This can be attributed to the change in the microstructural balance %martensite+%bainite as the reheating temperature increases. The results of this study are presented and discussed

EUSC: a clustering-based surrogate model to accelerate evolutionary undersampling in imbalanced classification

Learning from imbalanced datasets is highly demanded in real-world applications and a challenge for standard classifiers that tend to be biased towards the classes with the majority of the examples. Undersampling approaches reduce the size of the majority class to balance the class distributions. Evolutionary-based approaches are prominent, treating undersampling as a binary optimisation problem that determines which examples are removed. However, their utilisation is limited to small datasets due to fitness evaluation costs. This work proposes a two-stage clustering-based surrogate model that enables evolutionary undersampling to compute fitness values faster. The main novelty lies in the development of a surrogate model for binary optimisation which is based on the meaning (phenotype) rather than their binary representation (genotype). We conduct an evaluation on 44 imbalanced datasets, showing that in comparison with the original evolutionary undersampling, we can save up to 83% of the runtime without significantly deteriorating the classification performance.The work of H. Lam Le was funded by a Ph.D. scholarship from the School of Computer Science of the University of Nottingham, United Kingdom