- EC| DATASOUND
R. Rueda; M. P. Cuéllar; M. P. Cuéllar
Harvested from ORCID Public Data FileM. P. Cuéllar in OpenAIRE M. Molina-Solana; M. Molina-Solana
Harvested from ORCID Public Data FileM. Molina-Solana in OpenAIRE Y. Guo; +1 Authors R. Rueda; M. P. Cuéllar; M. P. Cuéllar
Harvested from ORCID Public Data FileM. P. Cuéllar in OpenAIRE M. Molina-Solana; M. Molina-Solana
Harvested from ORCID Public Data FileM. Molina-Solana in OpenAIRE Y. Guo; M. C. Pegalajar; M. C. Pegalajar
Harvested from ORCID Public Data FileM. C. Pegalajar in OpenAIREThis work addresses the problem of energy consumption time series forecasting. In our approach, a set of time series containing energy consumption data is used to train a single, parameterised prediction model that can be used to predict future values for all the input time series. As a result, the proposed method is able to learn the common behaviour of all time series in the set (i.e., a fingerprint) and use this knowledge to perform the prediction task, and to explain this common behaviour as an algebraic formula. To that end, we use symbolic regression methods trained with both single- and multi-objective algorithms. Experimental results validate this approach to learn and model shared properties of different time series, which can then be used to obtain a generalised regression model encapsulating the global behaviour of different energy consumption time series.
Access RoutesGreen gold 4 citations 4 popularity Average influence Average impulse Average 
Powered by BIP!add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.<script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/en12061069&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu - EC| DATASOUND R. Rueda; M. P. Cuéllar;
M. P. Cuéllar
Harvested from ORCID Public Data FileM. P. Cuéllar in OpenAIRE M. Molina-Solana; M. Molina-Solana
Harvested from ORCID Public Data FileM. Molina-Solana in OpenAIRE Y. Guo; +1 Authors R. Rueda; M. P. Cuéllar; M. P. Cuéllar
Harvested from ORCID Public Data FileM. P. Cuéllar in OpenAIRE M. Molina-Solana; M. Molina-Solana
Harvested from ORCID Public Data FileM. Molina-Solana in OpenAIRE Y. Guo; M. C. Pegalajar; M. C. Pegalajar
Harvested from ORCID Public Data FileM. C. Pegalajar in OpenAIREThis work addresses the problem of energy consumption time series forecasting. In our approach, a set of time series containing energy consumption data is used to train a single, parameterised prediction model that can be used to predict future values for all the input time series. As a result, the proposed method is able to learn the common behaviour of all time series in the set (i.e., a fingerprint) and use this knowledge to perform the prediction task, and to explain this common behaviour as an algebraic formula. To that end, we use symbolic regression methods trained with both single- and multi-objective algorithms. Experimental results validate this approach to learn and model shared properties of different time series, which can then be used to obtain a generalised regression model encapsulating the global behaviour of different energy consumption time series.
Access RoutesGreen gold 4 citations 4 popularity Average influence Average impulse Average Powered by BIP!add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.<script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/en12061069&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu 
R. Rueda; R. Rueda
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesR. Rueda in OpenAIRE M.P. Cuéllar; M.P. Cuéllar
Harvested from ORCID Public Data FileM.P. Cuéllar in OpenAIRE M.C. Pegalajar; M.C. Pegalajar
Harvested from ORCID Public Data FileM.C. Pegalajar in OpenAIRE M. Delgado; M. Delgado
Harvested from ORCID Public Data FileM. Delgado in OpenAIREAbstract Energy consumption has increased in recent decades at a rate ranging from 1.5% to 10% per year in the developed world. As a consequence, several efforts have been made to model energy consumption in order to achieve a better use of energy and to minimize environmental impact. Open problems in this area range from energy consumption forecasting to user profile mining, energy source planning, to transportation, among others. To address these problems, it is important to have suitable tools to model energy consumption data series, so that the analysts and CEOs can have knowledge about the underlying properties of the power demand in order to make high-level decisions. In this paper, we focus on the problem of energy consumption modelling, and provide a solution from the perspective of symbolic regression. More specifically, we develop hybrid genetic programming algorithms to find the algebraic expression that best models daily energy consumption in public buildings at the University of Granada as a testbed, and compare the benefits of Straight Line Programs with the classic tree representation used in symbolic regression. Regarding algorithm design, the outcomes of our experimentation suggest that Straight Line Programs outperform other representation models in the symbolic regression problems studied, and also that the hybridation with local search methods can improve the quality of the resulting algebraic expression. On the other hand, with regards to energy consumption modelling, our approach empirically demonstrates that symbolic regression can be a powerful tool to find underlying relationships between multivariate energy consumption data series.
9 citations 9 popularity Top 10% influence Average impulse Top 10% Powered by BIP!add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.<script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.asoc.2019.04.001&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu- R. Rueda;
R. Rueda
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesR. Rueda in OpenAIRE M.P. Cuéllar; M.P. Cuéllar
Harvested from ORCID Public Data FileM.P. Cuéllar in OpenAIRE M.C. Pegalajar; M.C. Pegalajar
Harvested from ORCID Public Data FileM.C. Pegalajar in OpenAIRE M. Delgado; M. Delgado
Harvested from ORCID Public Data FileM. Delgado in OpenAIREAbstract Energy consumption has increased in recent decades at a rate ranging from 1.5% to 10% per year in the developed world. As a consequence, several efforts have been made to model energy consumption in order to achieve a better use of energy and to minimize environmental impact. Open problems in this area range from energy consumption forecasting to user profile mining, energy source planning, to transportation, among others. To address these problems, it is important to have suitable tools to model energy consumption data series, so that the analysts and CEOs can have knowledge about the underlying properties of the power demand in order to make high-level decisions. In this paper, we focus on the problem of energy consumption modelling, and provide a solution from the perspective of symbolic regression. More specifically, we develop hybrid genetic programming algorithms to find the algebraic expression that best models daily energy consumption in public buildings at the University of Granada as a testbed, and compare the benefits of Straight Line Programs with the classic tree representation used in symbolic regression. Regarding algorithm design, the outcomes of our experimentation suggest that Straight Line Programs outperform other representation models in the symbolic regression problems studied, and also that the hybridation with local search methods can improve the quality of the resulting algebraic expression. On the other hand, with regards to energy consumption modelling, our approach empirically demonstrates that symbolic regression can be a powerful tool to find underlying relationships between multivariate energy consumption data series.
9 citations 9 popularity Top 10% influence Average impulse Top 10% Powered by BIP!add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.<script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.asoc.2019.04.001&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu - M.C. Pegalajar;
M.C. Pegalajar
Harvested from ORCID Public Data FileM.C. Pegalajar in OpenAIRE L.G.B. Ruiz; L.G.B. Ruiz
Harvested from ORCID Public Data FileL.G.B. Ruiz in OpenAIRE M.P. Cuéllar; R. Rueda;M.P. Cuéllar
Harvested from ORCID Public Data FileM.P. Cuéllar in OpenAIREAbstract Electricity demand is shown to steadily increase in the last few years, and it is one of the key aspects of living standards and quantifying welfare effects. However, the irregularity of electricity demand is one of the main problems in this field. Therefore, it is important to accurately anticipate future expenditures in order to optimize energy generation and to avoid unexpected wastes. As a result, we developed Machine Learning models to predict electricity demand. In particular, our study has been performed using data of the Spanish Electricity Network from 2007 to 2019. To this end, we propose the implementation of a set of Machine Learning techniques using various frameworks. In particular, we implemented six different prediction models: Linear Regression, Regression Trees, Gradient Boosting Regression, Random Forests, Multi-layer Perceptron, and three types of recurrent neural networks. Our experimentation shows promising results in all cases, since our models provides better prediction than the one estimated by the Spanish Electricity Network with an improvement of 12% in the worst case and up to 37% for the best predictor, which turned out to be the Gated Recurrent Unit neural network.
Access Routesbronze 20 citations 20 popularity Top 10% influence Top 10% impulse Top 10% Powered by BIP!add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.<script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.ijar.2021.03.002&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu - M.C. Pegalajar;
M.C. Pegalajar
Harvested from ORCID Public Data FileM.C. Pegalajar in OpenAIRE L.G.B. Ruiz; L.G.B. Ruiz
Harvested from ORCID Public Data FileL.G.B. Ruiz in OpenAIRE M.P. Cuéllar; R. Rueda;M.P. Cuéllar
Harvested from ORCID Public Data FileM.P. Cuéllar in OpenAIREAbstract Electricity demand is shown to steadily increase in the last few years, and it is one of the key aspects of living standards and quantifying welfare effects. However, the irregularity of electricity demand is one of the main problems in this field. Therefore, it is important to accurately anticipate future expenditures in order to optimize energy generation and to avoid unexpected wastes. As a result, we developed Machine Learning models to predict electricity demand. In particular, our study has been performed using data of the Spanish Electricity Network from 2007 to 2019. To this end, we propose the implementation of a set of Machine Learning techniques using various frameworks. In particular, we implemented six different prediction models: Linear Regression, Regression Trees, Gradient Boosting Regression, Random Forests, Multi-layer Perceptron, and three types of recurrent neural networks. Our experimentation shows promising results in all cases, since our models provides better prediction than the one estimated by the Spanish Electricity Network with an improvement of 12% in the worst case and up to 37% for the best predictor, which turned out to be the Gated Recurrent Unit neural network.
Access Routesbronze 20 citations 20 popularity Top 10% influence Top 10% impulse Top 10% Powered by BIP!add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.<script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.ijar.2021.03.002&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu - EC| Athenea3I L.G.B. Ruiz; L.G.B. Ruiz; Rossella Arcucci;
L.G.B. Ruiz
Harvested from ORCID Public Data FileL.G.B. Ruiz in OpenAIRE Miguel Molina-Solana; +2 AuthorsMiguel Molina-Solana
Harvested from ORCID Public Data FileMiguel Molina-Solana in OpenAIRE L.G.B. Ruiz; L.G.B. Ruiz; Rossella Arcucci;L.G.B. Ruiz
Harvested from ORCID Public Data FileL.G.B. Ruiz in OpenAIRE Miguel Molina-Solana; Miguel Molina-Solana;Miguel Molina-Solana
Harvested from ORCID Public Data FileMiguel Molina-Solana in OpenAIRE M.C. Pegalajar; M.C. Pegalajar
Harvested from ORCID Public Data FileM.C. Pegalajar in OpenAIREAbstract In the Energy Efficiency field, the incorporation of intelligent systems in cities and buildings is motivated by the energy savings and pollution reduction that can be attained. To achieve this goal, energy modelling and a better understanding of how energy is consumed are fundamental factors. As a result, this study proposes a methodology for knowledge acquisition in energy-related data through Time-Series Clustering (TSC) techniques. In our experimentation, we utilize data from the buildings at the University of Granada (Spain) and compare several clustering methods to get the optimum model, in particular, we tested k-Means, k-Medoids, Hierarchical clustering and Gaussian Mixtures; as well as several algorithms to obtain the best grouping, such as PAM, CLARA, and two variants of Lloyd’s method, Small and Large. Thus, our methodology can provide non-trivial knowledge from raw energy data. In contrast to previous studies in this field, not only do we propose a clustering methodology to group time series straightforwardly, but we also present an automatic strategy to search and analyse energy periodicity in these series recursively so that we can deepen granularity and extract information at different levels of detail. The results show that k-Medoids with PAM is the best approach in virtually all cases, and the Squared Euclidean distance outperforms the rest of the metrics.
58 citations 58 popularity Top 1% influence Top 10% impulse Top 1% Powered by BIP!add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.<script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.eswa.2020.113731&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu - EC| Athenea3I L.G.B. Ruiz; L.G.B. Ruiz; Rossella Arcucci;
L.G.B. Ruiz
Harvested from ORCID Public Data FileL.G.B. Ruiz in OpenAIRE Miguel Molina-Solana; +2 AuthorsMiguel Molina-Solana
Harvested from ORCID Public Data FileMiguel Molina-Solana in OpenAIRE L.G.B. Ruiz; L.G.B. Ruiz; Rossella Arcucci;L.G.B. Ruiz
Harvested from ORCID Public Data FileL.G.B. Ruiz in OpenAIRE Miguel Molina-Solana; Miguel Molina-Solana;Miguel Molina-Solana
Harvested from ORCID Public Data FileMiguel Molina-Solana in OpenAIRE M.C. Pegalajar; M.C. Pegalajar
Harvested from ORCID Public Data FileM.C. Pegalajar in OpenAIREAbstract In the Energy Efficiency field, the incorporation of intelligent systems in cities and buildings is motivated by the energy savings and pollution reduction that can be attained. To achieve this goal, energy modelling and a better understanding of how energy is consumed are fundamental factors. As a result, this study proposes a methodology for knowledge acquisition in energy-related data through Time-Series Clustering (TSC) techniques. In our experimentation, we utilize data from the buildings at the University of Granada (Spain) and compare several clustering methods to get the optimum model, in particular, we tested k-Means, k-Medoids, Hierarchical clustering and Gaussian Mixtures; as well as several algorithms to obtain the best grouping, such as PAM, CLARA, and two variants of Lloyd’s method, Small and Large. Thus, our methodology can provide non-trivial knowledge from raw energy data. In contrast to previous studies in this field, not only do we propose a clustering methodology to group time series straightforwardly, but we also present an automatic strategy to search and analyse energy periodicity in these series recursively so that we can deepen granularity and extract information at different levels of detail. The results show that k-Medoids with PAM is the best approach in virtually all cases, and the Squared Euclidean distance outperforms the rest of the metrics.
58 citations 58 popularity Top 1% influence Top 10% impulse Top 1% Powered by BIP!add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.<script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.eswa.2020.113731&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu - L. Cabezón;
L. Cabezón
Harvested from ORCID Public Data FileL. Cabezón in OpenAIRE L. G. B. Ruiz; L. G. B. Ruiz
Harvested from ORCID Public Data FileL. G. B. Ruiz in OpenAIRE D. Criado-Ramón; D. Criado-Ramón
Harvested from ORCID Public Data FileD. Criado-Ramón in OpenAIRE E. J. Gago; +1 AuthorsE. J. Gago
Harvested from ORCID Public Data FileE. J. Gago in OpenAIRE L. Cabezón; L. Cabezón
Harvested from ORCID Public Data FileL. Cabezón in OpenAIRE L. G. B. Ruiz; L. G. B. Ruiz
Harvested from ORCID Public Data FileL. G. B. Ruiz in OpenAIRE D. Criado-Ramón; D. Criado-Ramón
Harvested from ORCID Public Data FileD. Criado-Ramón in OpenAIRE E. J. Gago; E. J. Gago
Harvested from ORCID Public Data FileE. J. Gago in OpenAIRE M. C. Pegalajar; M. C. Pegalajar
Harvested from ORCID Public Data FileM. C. Pegalajar in OpenAIREPhotovoltaic solar energy is booming due to the continuous improvement in photovoltaic panel efficiency along with a downward trend in production costs. In addition, the European Union is committed to easing the implementation of renewable energy in many companies in order to obtain funding to install their own panels. Nonetheless, the nature of solar energy is intermittent and uncontrollable. This leads us to an uncertain scenario which may cause instability in photovoltaic systems. This research addresses this problem by implementing intelligent models to predict the production of solar energy. Real data from a solar farm in Scotland was utilized in this study. Finally, the models were able to accurately predict the energy to be produced in the next hour using historical information as predictor variables.
Access Routesgold 12 citations 12 popularity Top 10% influence Top 10% impulse Top 10% Powered by BIP!add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.<script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/en15228732&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu - L. Cabezón;
L. Cabezón
Harvested from ORCID Public Data FileL. Cabezón in OpenAIRE L. G. B. Ruiz; L. G. B. Ruiz
Harvested from ORCID Public Data FileL. G. B. Ruiz in OpenAIRE D. Criado-Ramón; D. Criado-Ramón
Harvested from ORCID Public Data FileD. Criado-Ramón in OpenAIRE E. J. Gago; +1 AuthorsE. J. Gago
Harvested from ORCID Public Data FileE. J. Gago in OpenAIRE L. Cabezón; L. Cabezón
Harvested from ORCID Public Data FileL. Cabezón in OpenAIRE L. G. B. Ruiz; L. G. B. Ruiz
Harvested from ORCID Public Data FileL. G. B. Ruiz in OpenAIRE D. Criado-Ramón; D. Criado-Ramón
Harvested from ORCID Public Data FileD. Criado-Ramón in OpenAIRE E. J. Gago; E. J. Gago
Harvested from ORCID Public Data FileE. J. Gago in OpenAIRE M. C. Pegalajar; M. C. Pegalajar
Harvested from ORCID Public Data FileM. C. Pegalajar in OpenAIREPhotovoltaic solar energy is booming due to the continuous improvement in photovoltaic panel efficiency along with a downward trend in production costs. In addition, the European Union is committed to easing the implementation of renewable energy in many companies in order to obtain funding to install their own panels. Nonetheless, the nature of solar energy is intermittent and uncontrollable. This leads us to an uncertain scenario which may cause instability in photovoltaic systems. This research addresses this problem by implementing intelligent models to predict the production of solar energy. Real data from a solar farm in Scotland was utilized in this study. Finally, the models were able to accurately predict the energy to be produced in the next hour using historical information as predictor variables.
Access Routesgold 12 citations 12 popularity Top 10% influence Top 10% impulse Top 10% Powered by BIP!add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.<script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/en15228732&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu - D. Criado-Ramón;
D. Criado-Ramón
Harvested from ORCID Public Data FileD. Criado-Ramón in OpenAIRE L. G. B. Ruiz; J. R. S. Iruela;L. G. B. Ruiz
Harvested from ORCID Public Data FileL. G. B. Ruiz in OpenAIRE M. C. Pegalajar; M. C. Pegalajar
Harvested from ORCID Public Data FileM. C. Pegalajar in OpenAIREThis paper introduces the first completely unsupervised methodology for non-intrusive load monitoring that does not rely on any additional data, making it suitable for real-life applications. The methodology includes an algorithm to efficiently decompose the aggregated energy load from households in events and algorithms based on expert knowledge to assign each of these events to four types of appliances: fridge, dishwasher, microwave, and washer/dryer. The methodology was developed to work with smart meters that have a granularity of 1 min and was evaluated using the Reference Energy Disaggregation Dataset. The results show that the algorithm can disaggregate the refrigerator with high accuracy and the usefulness of the proposed methodology to extract relevant features from other appliances, such as the power use and duration from the heating cycles of a dishwasher.
Access Routesgold 2 citations 2 popularity Average influence Average impulse Average Powered by BIP!add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.<script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/info15020087&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu - D. Criado-Ramón;
D. Criado-Ramón
Harvested from ORCID Public Data FileD. Criado-Ramón in OpenAIRE L. G. B. Ruiz; J. R. S. Iruela;L. G. B. Ruiz
Harvested from ORCID Public Data FileL. G. B. Ruiz in OpenAIRE M. C. Pegalajar; M. C. Pegalajar
Harvested from ORCID Public Data FileM. C. Pegalajar in OpenAIREThis paper introduces the first completely unsupervised methodology for non-intrusive load monitoring that does not rely on any additional data, making it suitable for real-life applications. The methodology includes an algorithm to efficiently decompose the aggregated energy load from households in events and algorithms based on expert knowledge to assign each of these events to four types of appliances: fridge, dishwasher, microwave, and washer/dryer. The methodology was developed to work with smart meters that have a granularity of 1 min and was evaluated using the Reference Energy Disaggregation Dataset. The results show that the algorithm can disaggregate the refrigerator with high accuracy and the usefulness of the proposed methodology to extract relevant features from other appliances, such as the power use and duration from the heating cycles of a dishwasher.
Access Routesgold 2 citations 2 popularity Average influence Average impulse Average Powered by BIP!add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.<script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/info15020087&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu - D. Criado-Ramón;
D. Criado-Ramón
Harvested from ORCID Public Data FileD. Criado-Ramón in OpenAIRE L. G. B. Ruiz; L. G. B. Ruiz
Harvested from ORCID Public Data FileL. G. B. Ruiz in OpenAIRE M. C. Pegalajar; M. C. Pegalajar
Harvested from ORCID Public Data FileM. C. Pegalajar in OpenAIREPattern sequence-based models are a type of forecasting algorithm that utilizes clustering and other techniques to produce easily interpretable predictions faster than traditional machine learning models. This research focuses on their application in energy demand forecasting and introduces two significant contributions to the field. Firstly, this study evaluates the use of pattern sequence-based models with large datasets. Unlike previous works that use only one dataset or multiple datasets with less than two years of data, this work evaluates the models in three different public datasets, each containing eleven years of data. Secondly, we propose a new pattern sequence-based algorithm that uses a genetic algorithm to optimize the number of clusters alongside all other hyperparameters of the forecasting method, instead of using the Cluster Validity Indices (CVIs) commonly used in previous proposals. The results indicate that neural networks provide more accurate results than any pattern sequence-based algorithm and that our proposed algorithm outperforms other pattern sequence-based algorithms, albeit with a longer training time.
Access Routesgold 3 citations 3 popularity Average influence Average impulse Average Powered by BIP!add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.<script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/bdcc7020092&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu - D. Criado-Ramón;
D. Criado-Ramón
Harvested from ORCID Public Data FileD. Criado-Ramón in OpenAIRE L. G. B. Ruiz; L. G. B. Ruiz
Harvested from ORCID Public Data FileL. G. B. Ruiz in OpenAIRE M. C. Pegalajar; M. C. Pegalajar
Harvested from ORCID Public Data FileM. C. Pegalajar in OpenAIREPattern sequence-based models are a type of forecasting algorithm that utilizes clustering and other techniques to produce easily interpretable predictions faster than traditional machine learning models. This research focuses on their application in energy demand forecasting and introduces two significant contributions to the field. Firstly, this study evaluates the use of pattern sequence-based models with large datasets. Unlike previous works that use only one dataset or multiple datasets with less than two years of data, this work evaluates the models in three different public datasets, each containing eleven years of data. Secondly, we propose a new pattern sequence-based algorithm that uses a genetic algorithm to optimize the number of clusters alongside all other hyperparameters of the forecasting method, instead of using the Cluster Validity Indices (CVIs) commonly used in previous proposals. The results indicate that neural networks provide more accurate results than any pattern sequence-based algorithm and that our proposed algorithm outperforms other pattern sequence-based algorithms, albeit with a longer training time.
Access Routesgold 3 citations 3 popularity Average influence Average impulse Average Powered by BIP!add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.<script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/bdcc7020092&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu - A. Cabrera;
A. Cabrera
Harvested from ORCID Public Data FileA. Cabrera in OpenAIRE L. G. B. Ruiz; L. G. B. Ruiz
Harvested from ORCID Public Data FileL. G. B. Ruiz in OpenAIRE D. Criado-Ramón; C. D. Barranco; +1 AuthorsD. Criado-Ramón
Harvested from ORCID Public Data FileD. Criado-Ramón in OpenAIRE A. Cabrera; A. Cabrera
Harvested from ORCID Public Data FileA. Cabrera in OpenAIRE L. G. B. Ruiz; L. G. B. Ruiz
Harvested from ORCID Public Data FileL. G. B. Ruiz in OpenAIRE D. Criado-Ramón; C. D. Barranco;D. Criado-Ramón
Harvested from ORCID Public Data FileD. Criado-Ramón in OpenAIRE M. C. Pegalajar; M. C. Pegalajar
Harvested from ORCID Public Data FileM. C. Pegalajar in OpenAIREThis paper presents the implementation and analysis of two approaches (fuzzy and conventional). Using hourly data from buildings at the University of Granada, we have examined their electricity demand and designed a model to predict energy consumption. Our proposal was conducted with the aid of time series techniques as well as the combination of artificial neural networks and clustering algorithms. Both approaches proved to be suitable for energy modelling although nonfuzzy models provided more variability and less robustness than fuzzy ones. Despite the relatively small difference between fuzzy and nonfuzzy estimates, the results reported in this study show that the fuzzy solution may be useful to enhance and enrich energy predictions.
Access Routeshybrid 0 citations 0 popularity Average influence Average impulse Average Powered by BIP!add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.<script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1155/2023/4391555&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu - A. Cabrera;
A. Cabrera
Harvested from ORCID Public Data FileA. Cabrera in OpenAIRE L. G. B. Ruiz; L. G. B. Ruiz
Harvested from ORCID Public Data FileL. G. B. Ruiz in OpenAIRE D. Criado-Ramón; C. D. Barranco; +1 AuthorsD. Criado-Ramón
Harvested from ORCID Public Data FileD. Criado-Ramón in OpenAIRE A. Cabrera; A. Cabrera
Harvested from ORCID Public Data FileA. Cabrera in OpenAIRE L. G. B. Ruiz; L. G. B. Ruiz
Harvested from ORCID Public Data FileL. G. B. Ruiz in OpenAIRE D. Criado-Ramón; C. D. Barranco;D. Criado-Ramón
Harvested from ORCID Public Data FileD. Criado-Ramón in OpenAIRE M. C. Pegalajar; M. C. Pegalajar
Harvested from ORCID Public Data FileM. C. Pegalajar in OpenAIREThis paper presents the implementation and analysis of two approaches (fuzzy and conventional). Using hourly data from buildings at the University of Granada, we have examined their electricity demand and designed a model to predict energy consumption. Our proposal was conducted with the aid of time series techniques as well as the combination of artificial neural networks and clustering algorithms. Both approaches proved to be suitable for energy modelling although nonfuzzy models provided more variability and less robustness than fuzzy ones. Despite the relatively small difference between fuzzy and nonfuzzy estimates, the results reported in this study show that the fuzzy solution may be useful to enhance and enrich energy predictions.
Access Routeshybrid 0 citations 0 popularity Average influence Average impulse Average Powered by BIP!add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.<script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1155/2023/4391555&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu - L. G. B. Ruiz;
L. G. B. Ruiz
Harvested from ORCID Public Data FileL. G. B. Ruiz in OpenAIRE M. C. Pegalajar; M. C. Pegalajar
Harvested from ORCID Public Data FileM. C. Pegalajar in OpenAIRECurrently, new technologies and approaches are continuously and rapidly being introduced and implemented in energy systems [...]
Access Routesgold 0 citations 0 popularity Average influence Average impulse Average Powered by BIP!add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.<script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/en16052258&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu - L. G. B. Ruiz;
L. G. B. Ruiz
Harvested from ORCID Public Data FileL. G. B. Ruiz in OpenAIRE M. C. Pegalajar; M. C. Pegalajar
Harvested from ORCID Public Data FileM. C. Pegalajar in OpenAIRECurrently, new technologies and approaches are continuously and rapidly being introduced and implemented in energy systems [...]
Access Routesgold 0 citations 0 popularity Average influence Average impulse Average Powered by BIP!add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.<script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/en16052258&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu - L.G.B. Ruiz;
L.G.B. Ruiz
Harvested from ORCID Public Data FileL.G.B. Ruiz in OpenAIRE M.I. Capel; M.I. Capel
Harvested from ORCID Public Data FileM.I. Capel in OpenAIRE M.C. Pegalajar; M.C. Pegalajar
Harvested from ORCID Public Data FileM.C. Pegalajar in OpenAIREAbstract In our state-of-the-art study, we improve neural network-based models for predicting energy consumption in buildings by parallelizing the CHC adaptive search algorithm. We compared the sequential implementation of the evolutionary algorithm with the new parallel version to obtain predictors and found that this new version of our software tool halved the execution time of the sequential version. New predictors based on various classes of neural networks have been developed and the obtained results support the validity of the proposed approaches with an average improvement of 75% of the average execution time in relation to previous sequential implementations.
30 citations 30 popularity Top 10% influence Top 10% impulse Top 10% Powered by BIP!add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.<script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.asoc.2018.12.028&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu - L.G.B. Ruiz;
L.G.B. Ruiz
Harvested from ORCID Public Data FileL.G.B. Ruiz in OpenAIRE M.I. Capel; M.I. Capel
Harvested from ORCID Public Data FileM.I. Capel in OpenAIRE M.C. Pegalajar; M.C. Pegalajar
Harvested from ORCID Public Data FileM.C. Pegalajar in OpenAIREAbstract In our state-of-the-art study, we improve neural network-based models for predicting energy consumption in buildings by parallelizing the CHC adaptive search algorithm. We compared the sequential implementation of the evolutionary algorithm with the new parallel version to obtain predictors and found that this new version of our software tool halved the execution time of the sequential version. New predictors based on various classes of neural networks have been developed and the obtained results support the validity of the proposed approaches with an average improvement of 75% of the average execution time in relation to previous sequential implementations.
30 citations 30 popularity Top 10% influence Top 10% impulse Top 10% Powered by BIP!add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.<script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.asoc.2018.12.028&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu
- 1
- 2
