Use of Optimised LSTM Neural Networks Pre-Trained With Synthetic Data to Estimate PV Generation

Optimising the use of the photovoltaic (PV) energy is essential to reduce fossil fuel emissions by increasing the use of solar power generation. In recent years, research has focused on physical simulations or artifical intelligence models attempting to increase the accuracy of PV generation predictions. The use of simulated data as pre-training for deep learning models has increased in different fields. The reasons are the higher efficiency in the subsequent training with real data and the possibility of not having real data available. This work presents a methodology, based on an deep learning model optimised with specific techniques and pre-trained with synthetic data, to estimate the generation of a PV system. A case study of a photovoltaic installation with 296 PV panels located in northwest Spain is presented. The results show that the model with proper pre-training trains six to seven times faster than a model without pre-training and three to four times faster than a model pre-trained with non-accurate simulated data. In terms of accuracy and considering a homogeneous training process, all models obtained average relative errors around 12%, except the model with incorrect pre-training which performs worse

Estimating mobility of tourists. New Twitter-based procedure

Twitter has been actively researched as a human mobility proxy. Tweets can contain two classes of geographical metadata: the location from which a tweet was published, and the place where the tweet is estimated to have been published. Nevertheless, Twitter also presents tweets without any geographical metadata when querying for tweets on a specific location. This study presents a methodology which includes an algorithm for estimating the geographical coordinates to tweets for which Twitter doesn't assign any. Our objective is to determine the origin and the route that a tourist followed, even if Twitter doesn't return geographically identified data. This is carried out through geographical searches of tweets inside a defined area. Once a tweet is found inside an area, but its metadata contains no explicit geographical coordinates, its coordinates are estimated by iteratively performing geographical searches, with a decreasing geographical searching radius. This algorithm was tested in two touristic villages of Madrid (Spain) and a major city in Canada. A set of tweets without geographical coordinates in these areas were found and processed. The coordinates of a subset of them were successfully estimated.Agencia Estatal de Investigación | Ref. PID2020-116040RB-I00Universidade de Vigo/CISU

A Functional Data Analysis for Assessing the Impact of a Retrofitting in the Energy Performance of a Building

There is an increasing interest in reducing the energy consumption in buildings and in improving their energy efficiency. Building retrofitting is the employed solution for enhancing the energy efficiency in existing buildings. However, the actual performance after retrofitting should be analysed to check the effectiveness of the energy conservation measures. The aim of this work was to detect and to quantify the impact that a retrofitting had in the electrical consumption, heating demands, lighting and temperatures of a building located in the north of Spain. The methodology employed is the application of Functional Data Analyses (FDA) in comparison with classic mathematical techniques such as the Analysis of Variance (ANOVA). The methods that are commonly used for assessing building refurbishment are based on vectorial approaches. The novelty of this work is the application of FDA for assessing the energy performance of renovated buildings. The study proves that more accurate and realistic results are obtained working with correlated datasets than with independently distributed observations of classical methods. Moreover, the electrical savings reached values of more than 70% and the heating demands were reduced more than 15% for all floors in the building.This paper was funded by the Spanish Government (Science, Innovation and Universities Ministry) under the project RTI2018-096296-B-C21

A functional data analysis for assessing the impact of a retrofitting in the energy performance of a building

There is an increasing interest in reducing the energy consumption in buildings and in improving their energy efficiency. Building retrofitting is the employed solution for enhancing the energy efficiency in existing buildings. However, the actual performance after retrofitting should be analysed to check the effectiveness of the energy conservation measures. The aim of this work was to detect and to quantify the impact that a retrofitting had in the electrical consumption, heating demands, lighting and temperatures of a building located in the north of Spain. The methodology employed is the application of Functional Data Analyses (FDA) in comparison with classic mathematical techniques such as the Analysis of Variance (ANOVA). The methods that are commonly used for assessing building refurbishment are based on vectorial approaches. The novelty of this work is the application of FDA for assessing the energy performance of renovated buildings. The study proves that more accurate and realistic results are obtained working with correlated datasets than with independently distributed observations of classical methods. Moreover, the electrical savings reached values of more than 70% and the heating demands were reduced more than 15% for all floors in the building.Ministerio de Ciencia, Innovación y Universidades | Ref. RTI2018-096296-B-C2

A Functional Data Analysis Approach for the Detection of Air Pollution Episodes and Outliers: A Case Study in Dublin, Ireland

Ground level concentrations of nitrogen oxide (NOx) can act as an indicator of air quality in the urban environment. In cities with relatively good air quality, and where NOx concentrations rarely exceed legal limits, adverse health effects on the population may still occur. Therefore, detecting small deviations in air quality and deriving methods of controlling air pollution are challenging. This study presents different data analytical methods which can be used to monitor and effectively evaluate policies or measures to reduce nitrogen oxide (NOx) emissions through the detection of pollution episodes and the removal of outliers. This method helps to identify the sources of pollution more effectively, and enhances the value of monitoring data and exceedances of limit values. It will detect outliers, changes and trend deviations in NO2 concentrations at ground level, and consists of four main steps: classical statistical description techniques, statistical process control techniques, functional analysis and a functional control process. To demonstrate the effectiveness of the outlier detection methodology proposed, it was applied to a complete one-year NO2 dataset for a sub-urban site in Dublin, Ireland in 2013. The findings demonstrate how the functional data approach improves the classical techniques for detecting outliers, and in addition, how this new methodology can facilitate a more thorough approach to defining effect air pollution control measures

Impacto de la inteligencia artificial en los métodos de evaluación en la educación primaria y secundaria: revisión sistemática de la literatura

The educational sector can be enriched by the incorporation of artificial intelligence (AI) in various aspects. The field of artificial intelligence and its applications in the education sector give rise to a multidisciplinary field that brings together computer science, statistics, psychology and, of course, education. Within this context, this review aimed to synthesise existing research focused on provide improvements on primary/secondary student assessment using some AI tool. Thus, nine original research studies (641 participants), published between 2010 and 2023, met the inclusion criteria defined in this systematic literature review. The main contributions of the application of AI in the assessment of students at these lower educational levels focus on predicting their performance, automating and making evaluations more objective by means of neural networks or natural language processing, the use of educational robots to analyse their learning process, and the detection of specific factors that make classes more attractive. This review shows the possibilities and already existing uses that AI can bring to education, specifically in the evaluation of student performance at the primary and secondary levels.El sector educativo puede enriquecerse con la incorporación de la inteligencia artificial (IA) en diversos aspectos. El campo de la inteligencia artificial y sus aplicaciones en el sector educativo dan lugar a un campo multidisciplinar en el que confluyen la informática, la estadística, la psicología y, por supuesto, la educación. Dentro de este contexto, esta revisión pretende sintetizar las investigaciones existentes centradas en proporcionar mejoras en la evaluación del alumnado de primaria/secundaria utilizando alguna herramienta de IA. Así, nueve estudios de investigación originales (641 participantes), publicados entre 2010 y 2023, cumplen los criterios de inclusión definidos en esta revisión bibliográfica sistemática. Las principales aportaciones de la aplicación de la IA en la evaluación del alumnado de estos niveles educativos inferiores se centran en la predicción de su rendimiento, evaluaciones más objetivas y automatizadas mediante redes neuronales o procesamiento del lenguaje natural, el uso de robots educativos para analizar su proceso de aprendizaje y la detección de factores específicos que hacen más atractivas las clases. Esta revisión muestra las posibilidades y los usos ya existentes que la IA puede aportar a la educación, concretamente en la evaluación del rendimiento del alumnado de primaria y secundaria.Universidade de Vigo/CISUGMinisterio de Universidades | Ref. FPU19/0118

Load forecasting with machine learning and deep learning methods

Characterizing the electric energy curve can improve the energy efficiency of existing buildings without any structural change and is the basis for controlling and optimizing building performance. Artificial Intelligence (AI) techniques show much potential due to their accuracy and malleability in the field of pattern recognition, and using these models it is possible to adjust the building services in real time. Thus, the objective of this paper is to determine the AI technique that best forecasts electrical loads. The suggested techniques are random forest (RF), support vector regression (SVR), extreme gradient boosting (XGBoost), multilayer perceptron (MLP), long short-term memory (LSTM), and temporal convolutional network (Conv-1D). The conducted research applies a methodology that considers the bias and variance of the models, enhancing the robustness of the most suitable AI techniques for modeling and forecasting the electricity consumption in buildings. These techniques are evaluated in a single-family dwelling located in the United States. The performance comparison is obtained by analyzing their bias and variance by using a 10-fold cross-validation technique. By means of the evaluation of the models in different sets, i.e., validation and test sets, their capacity to reproduce the results and the ability to properly forecast on future occasions is also evaluated. The results show that the model with less dispersion, both in the validation set and test set, is LSTM. It presents errors of −0.02% of nMBE and 2.76% of nRMSE in the validation set and −0.54% of nMBE and 4.74% of nRMSE in the test set.Universidade de Vigo | Ref. 00VI 131H 6410211European Group for territorial cooperation Galicia-North of Portugal (GNP, AECT) through the IACOBUS program of research staysMinisterio de Ciencia, Innovación y Universidades | Ref. FPU19/01187Ministerio de Ciencia, Innovación y Universidades | Ref. TED2021-130677B-I0

A functional data analysis approach for the detection of air pollution episodes and outliers: a case study in Dublin, Ireland

Ground level concentrations of nitrogen oxide (NOx) can act as an indicator of air quality in the urban environment. In cities with relatively good air quality, and where NOx concentrations rarely exceed legal limits, adverse health effects on the population may still occur. Therefore, detecting small deviations in air quality and deriving methods of controlling air pollution are challenging. This study presents different data analytical methods which can be used to monitor and effectively evaluate policies or measures to reduce nitrogen oxide (NOx) emissions through the detection of pollution episodes and the removal of outliers. This method helps to identify the sources of pollution more effectively, and enhances the value of monitoring data and exceedances of limit values. It will detect outliers, changes and trend deviations in NO2 concentrations at ground level, and consists of four main steps: classical statistical description techniques, statistical process control techniques, functional analysis and a functional control process. To demonstrate the effectiveness of the outlier detection methodology proposed, it was applied to a complete one-year NO2 dataset for a sub-urban site in Dublin, Ireland in 2013. The findings demonstrate how the functional data approach improves the classical techniques for detecting outliers, and in addition, how this new methodology can facilitate a more thorough approach to defining effect air pollution control measures.Ministerio de Industria y Competitividad | Ref. RTI2018-096296-B-C2

IoT-based platform for automated IEQ spatio-temporal analysis in buildings using machine learning techniques

Financiaciado para publicación en acceso aberto: Universidade de Vigo/CISUGProviding accurate information about the indoor environmental quality (IEQ) conditions inside building spaces is essential to assess the comfort levels of their occupants. These values may vary inside the same space, especially for large zones, requiring many sensors to produce a fine-grained representation of the space conditions, which increases hardware installation and maintenance costs. However, sound interpolation techniques may produce accurate values with fewer input points, reducing the number of sensors needed. This work presents a platform to automate this accurate IEQ representation based on a few sensor devices placed across a large building space. A case study is presented in a research centre in Spain using 8 wall-mounted devices and an additional moving device to train a machine learning model. The system yields accurate results for estimations at positions and times never seen before by the trained model, with relative errors between 4% and 10% for the analysed variables.Ministerio de Ciencia, Innovación y Universidades | Ref. RTI2018-096296-B-C2Ministerio de Ciencia, Innovación y Universidades | Ref. FPU17/ 01834Ministerio de Ciencia, Innovación y Universidades | Ref. FPU19/01187Universidad de Vigo | Ref. 00VI 131H 641.0

TFG 2013/2014

Amb aquesta publicació, EINA, Centre universitari de Disseny i Art adscrit a la Universitat Autònoma de Barcelona, dóna a conèixer el recull dels Treballs de Fi de Grau presentats durant el curs 2013-2014. Voldríem que un recull com aquest donés una idea més precisa de la tasca que es realitza a EINA per tal de formar nous dissenyadors amb capacitat de respondre professionalment i intel·lectualment a les necessitats i exigències de la nostra societat. El treball formatiu s’orienta a oferir resultats que responguin tant a paràmetres de rigor acadèmic i capacitat d’anàlisi del context com a l’experimentació i la creació de nous llenguatges, tot fomentant el potencial innovador del disseny.Con esta publicación, EINA, Centro universitario de diseño y arte adscrito a la Universidad Autónoma de Barcelona, da a conocer la recopilación de los Trabajos de Fin de Grado presentados durante el curso 2013-2014. Querríamos que una recopilación como ésta diera una idea más precisa del trabajo que se realiza en EINA para formar nuevos diseñadores con capacidad de responder profesional e intelectualmente a las necesidades y exigencias de nuestra sociedad. El trabajo formativo se orienta a ofrecer resultados que respondan tanto a parámetros de rigor académico y capacidad de análisis, como a la experimentación y la creación de nuevos lenguajes, al tiempo que se fomenta el potencial innovador del diseño.With this publication, EINA, University School of Design and Art, affiliated to the Autonomous University of Barcelona, brings to the public eye the Final Degree Projects presented during the 2013-2014 academic year. Our hope is that this volume might offer a more precise idea of the task performed by EINA in training new designers, able to speak both professionally and intellectually to the needs and demands of our society. The educational task is oriented towards results that might respond to the parameters of academic rigour and the capacity for contextual analysis, as well as to considerations of experimentation and the creation of new languages, all the while reinforcing design’s innovative potential