Skarmeta Gómez, Antonio

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Skarmeta Gómez, Antonio

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Skarmeta Gómez, Antonio

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Universidad de Murcia. Departamento de Ingeniería de la Informacióny las Comunicaciones

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Open Access
Transfer and deep learning models for daily reference evapotranspiration estimation and forecasting in Spain from local to national scale
(Elsevier, 2025-03-12) Ye, Yu; Zamora Izquierdo, Miguel Ángel; Skarmeta Gómez, Antonio; González Vidal, Aurora; Ingeniería de la Información y las Comunicaciones; Facultades de la UMU::Facultad de Informática
Accurate estimation and forecasting of Reference Evapotranspiration () is critical for almost all agricultural activities and water resource management. However, the most commonly used Penman-Monteith model (FAO56-PM) requires a large amount of input data and it is difficult to compute for general users. Machine Learning (ML) techniques can be used to address this shortcoming. Nevertheless, most studies are site-specific and lack generalizability. This study compares standard ML and Deep Learning (DL) algorithms for estimating and forecasting daily at different spatial scales in Spain. While Transfer Learning (TL) is a well-established ML technique, its application in computation remains largely unexplored. We applied TL in a novel approach to retrain DL models, enabling adaptation to diverse local climatic conditions, which is particularly important in this domain. All possible combinations of FAO56-PM inputs were evaluated. The results showed that with three or more climatic variables, the TL process can consistently reduce errors by using an appropriate amount of new data to retrain the models. In estimation, with 20% (120 days) of new data, TL models can provide the same performance as if they were trained with local data, both regionally and nationally (improvement of MAE from 26.4% to 99.5%). During forecasting, we used predicted weather data as input, and despite inherent biases in some variables, the TL models successfully adapted using 9-36 days of new data, significantly improving predictive performance (reducing MAE from -1.1% to 134.3%). Thus, the TL process is highly recommended as a promising methodology for increasing the generalization capability of DL models in both daily estimation and forecasting under diverse climatic conditions with limited local data.
Open Access
MultiBEATS: blocks of eigenvalues algorithm for multivariate time series dimensionality reduction
(Elsevier, 2024-04) González Vidal, Aurora; Martínez Ibarra, Antonio; Skarmeta Gómez, Antonio; Ingeniería de la Información y las Comunicaciones; Facultades de la UMU::Facultad de Informática
Multivariate Time Series are sequences of observations taken from multiple sources. The proliferation of environments in which data is collected by means of sensors and the adoption of data-based services reveals the importance of their efficient analysis. In smart environments, the analysis of the raw Multivariate Time Series is cumbersome. The algorithms are heavy, slow, and fail to extract all the knowledge available. In that sense, representation techniques reduce the data points but maintain the inner patterns so that the information present in data persists. There exist several approaches to represent Univariate Time Series, but for the multivariate case they are scarce. We have adapted the existent univariate representation algorithm BEATS, which is based on Discrete Cosine transformation and the extraction of eigenvalues to multivariate scenarios and we have called it MultiBEATS. Our method allows flexibility with regard to data reduction and does not require labelled data. To investigate the efficacy of MultiBEATS, we selected 8 open multivariate time series datasets and compared the running time and accuracy of their classification using raw data and MultiBEATS transformed data. We have also compared our results with the representation algorithm Seq2VAR, based on autoencoders, for having a baseline. The MultiBEATS transformation reduces the execution time of classification algorithms, has improved the results in accuracy in 2 of the datasets and matches a third one. With regards to the baseline, MultiBEATS is faster than Seq2VAR and more accurate in 7 out of the 8 datasets. In conclusion, MultiBEATS is a promising approach for efficiently reducing multivariate time series data, therefore helping decision-making in dynamic smart environments.
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Empirical study of massive set-point behavioral data: towards a cloud-based artificial intelligence that democratizes thermostats
(IEEE Computer Society, 2018-07-30) Ramallo González, Alfonso Pablo; González Vidal, Aurora; Skarmeta Gómez, Antonio; Ingeniería de la Información y las Comunicaciones; Facultad de Informática
The research showed in this document consisted on monitoring 546 air conditioners of individual offices located in two large buildings for the later evaluation of the behaviors of users with respect to their controllers. Data was collected over 14 months and provided important insights about the phenomenon. It was seen that users can be separated in two groups, one that likes to interact with the controllers often and change the temperature at least once a week and another that interact less. It was seen that the variability of users with respect to the thermostat values they prefer is high, and that this should be taken into account when creating a “one fits all” solution. Also, it appears that adaptive thermal comfort theories that suggest users want lower temperatures in cold months are not reflected on the set-points chosen. In addition, we have seen that people interacting more with the controllers tend to waste less energy, this would be interesting if an app to interact with the user for this purpose is design. More communication with the user may imply less energy wasted.
Open Access
Missing Data Imputation with Bayesian Maximum Entropy for Internet of Things Applications
(IEEE Internet of Things Journal, 2021-11-01) González Vidal, Aurora; Rathore, Punit ; Rao, Aravinda S.; Marimuthu Palaniswami; Skarmeta Gómez, Antonio; Mendoza Bernal, José; Ingeniería de la Información y las Comunicaciones; Facultades de la UMU::Facultad de Informática
Internet of Things (IoT) enables the seamless integration of sensors, actuators and communication devices for real-time applications. IoT systems require good quality sensor data in order to make real-time decisions. However, values are often missing from the sensor data collected owing to faulty sensors, a loss of data during communication, interference and measurement errors. Considering the spatiotemporal nature of IoT data and the uncertainty of the data collected by sensors, we propose a new framework with which to impute missing values utilizing Bayesian Maximum Entropy (BME) as a convenient means to estimate the missing data from IoT applications. Missing sensor measurements adversely affect the quality of data, and consequently the performance and outcomes of IoT systems. Our proposed framework incorporates BME in order to impute missing values in diverse IoT scenarios by making use of the combination of low- and high-precision sensors. Our approach can incorporate the measurement errors of low- precision sensors as interval quantities along with the high-precision sensor measurements, making it highly suitable for real-time IoT systems. Our framework is robust to variations in data, requires less execution time, and requires only a single input parameter, thus outperforming existing IoT data imputation methods. The experimental results obtained for three IoT datasets demonstrate the superiority of the BME framework as regards accuracy, running time and robustness. The framework can additionally be extended to distributed IoT nodes for the online imputation of missing values.
Open Access
Providing personalized energy management and awareness services for energy efficiency in smart buildings
(MDPI, 2017-09-07) Fotopoulou, Eleni; Zafeiropoulos, Anastasios; Simsek, Umutcan; González Vidal, Aurora; Tsiolis, George; Gouvas, Panagiotis; Liapis, Paris; Fensel, Anna; Skarmeta Gómez, Antonio; Terroso Sáenz, Fernando; Ingeniería de la Información y las Comunicaciones; Facultades de la UMU::Facultad de Informática
Considering that the largest part of end-use energy consumption worldwide is associated with the buildings sector, there is an inherent need for the conceptualization, specification, implementation, and instantiation of novel solutions in smart buildings, able to achieve significant reductions in energy consumption through the adoption of energy efficient techniques and the active engagement of the occupants. Towards the design of such solutions, the identification of the main energy consuming factors, trends, and patterns, along with the appropriate modeling and understanding of the occupants’ behavior and the potential for the adoption of environmentally-friendly lifestyle changes have to be realized. In the current article, an innovative energy-aware information technology (IT) ecosystem is presented, aiming to support the design and development of novel personalized energy management and awareness services that can lead to occupants’ behavioral change towards actions that can have a positive impact on energy efficiency. Novel information and communication technologies (ICT) are exploited towards this direction, related mainly to the evolution of the Internet of Things (IoT), data modeling, management and fusion, big data analytics, and personalized recommendation mechanisms. The combination of such technologies has resulted in an open and extensible architectural approach able to exploit in a homogeneous, efficient and scalable way the vast amount of energy, environmental, and behavioral data collected in energy efficiency campaigns and lead to the design of energy management and awareness services targeted to the occupants’ lifestyles. The overall layered architectural approach is detailed, including design and instantiation aspects based on the selection of set of available technologies and tools. Initial results from the usage of the proposed energy aware IT ecosystem in a pilot site at the University of Murcia are presented along with a set of identified open issues for future research.
Open Access
A Transfer Learning Framework for predictive energy-related scenarios in Smart Buildings
(IEEE Transactions on Industry Applications, 2023-02-01) González Vidal, Aurora; Niu, Shuteng; Song, Houbing; Skarmeta Gómez, Antonio; Mendoza Bernal, José; Ingeniería de la Información y las Comunicaciones; Facultades de la UMU::Facultad de Informática
Human activities and city routines follow patterns. Transfer learning can help achieve scalable solutions towards the realisation of smart cities accounting for similarities between regions, domains, and activities. In this study, we propose a Transfer Learning-based framework for smart buildings to test this hypothesis in energy-related problems. Our framework has two major components: the network creation and the transferable predictive model. In order to create the network that groups buildings sharing characteristics, we evaluated two strategies: a novel clustering algorithm for mixed data, k-prod, and clustering the image-based representation of time series. Then, a combination of Long Short Term Memory and Convolutional Neural Network was trained on the centroids of the clusters for energy consumption prediction. The Coefficient of Variation of the Root Mean Squared Error (CVRMSE) of the predictions in such clusters vary between 3.85 and 58.85 %. The obtained parameters were transferred to the rest of the buildings for predictive purposes, finding accurate results in buildings with little data. Our framework deals with insufficient training data since parameters from scenarios with more sensors can be received. It also carries out state-of-the-art performance on 3 datasets from different sources having in total 533 rooms/buildings and two energy efficiency domains: consumption prediction reducing the CVRMSE in a 21.6 %, and air conditioning usage prediction moving from a 4.18 % to a 0.28% CVRMSE. Our framework extracts more knowledge from available IoT deployments, so that smartness could be spread between environments at a fewer cost given that less individual effort will be needed.
Open Access
An open IoT platform for the management and analysis of energy data
(Elsevier, 2019-03-01) Fernando Terroso-Sáenz; González Vidal, Aurora; Ramallo González, Alfonso Pablo; Skarmeta Gómez, Antonio; Ingeniería de la Información y las Comunicaciones; Facultades de la UMU::Facultad de Informática
Buildings are key players when looking at end-use energy demand. It is for this reason that during the last few years, the Internet of Things (IoT) has been considered as a tool that could bring great opportunities for energy reduction via the accurate monitoring and control of a large variety of energy-related agents in buildings. However, there is a lack of IoT platforms specifically oriented towards the proper processing, management and analysis of such large and diverse data. In this context, we put forward in this paper the IoT Energy Platform (IoTEP) which attempts to provide the first holistic solution for the management of IoT energy data. The platform we show here (that has been based on FIWARE) is suitable to include several functionalities and features that are key when dealing with energy quality insurance and support for data analytics. As part of this work, we have tested the platform IoTEP with a real use case that includes data and information from three buildings totalizing hundreds of sensors. The platform has exceed expectations proving robust, plastic and versatile for the application at hand.
Open Access
Applicability of Big Data Techniques to Smart Cities Deployments
(2001-09-16) M. Victoria Moreno; Fernando Terroso-Sáenz; González Vidal, Aurora; Valdés Vela, Mercedes; Skarmeta Gómez, Antonio; Zamora Izquierdo, Miguel Ángel; Victor Chang; Ingeniería de la Información y las Comunicaciones; Facultades de la UMU::Facultad de Informática
This paper presents the main foundations of big data applied to smart cities. A general Internet of Things based architecture is proposed to be applied to different smart cities applications. We describe two scenarios of big data analysis. One of them illustrates some services implemented in the smart campus of the University of Murcia. The second one is focused on a tram service scenario, where thousands of transit-card transactions should be processed. Results obtained from both scenarios show the potential of the applicability of this kind of techniques to provide profitable services of smart cities, such as the management of the energy consumption and comfort in smart buildings, and the detection of travel profiles in smart transport.
Open Access
IoT for water management: towards Intelligent anomaly detection
(IEEE, 2019-07-22) Cuenca-Jara, Jesús ; Antonio F. Skarmeta; González Vidal, Aurora; Skarmeta Gómez, Antonio; Ingeniería de la Información y las Comunicaciones; Facultad de Informática
Given that the global water system is deteriorating and the supply and demand are very dynamic, smart ways to improve the water management system are needed so that it becomes more efficient and to extend the services provided to the citizens leading to smart cities. One of many water related problems that can be addressed by the Internet of Things is anomaly detection in water consumption. The analysis of data collected by smart meters will help to personalize the feedback to customers, prevent water waste and detect alarming situations. Water consumption data can be considered as a time series. Time series anomaly detection is an old topic but in this work we attempt to examine which techniques suits better for water consumption. We examine two very well-known methods for time series anomaly detection: an ARIMA-based framework anomaly detection technique which selects as outliers those points no fitting an ARIMA process and also a technique named HOTSAX which represents windows of data in a discrete way and then discriminates them using a heuristic. They are both very different in nature but the true positive analysis is excellent. The challenge remains in removing the false positive from the picture.
Open Access
A methodology for energy multivariate time series forecasting in smart buildings based on feature selection
(Elsevier, 2019-05-10) González Vidal, Aurora; Jiménez Barrionuevo, Fernando; Skarmeta Gómez, Antonio; Ingeniería de la Información y las Comunicaciones; Facultades de la UMU::Facultad de Informática
The massive collection of data via emerging technologies like the Internet of Things (IoT) requires ﬁnding optimal ways to reduce the created features that have a potential impact on the information that can be extracted through the machine learning process. The mining of knowledge related to a concept is done on the basis of the features of data. The process of ﬁnding the best combination of features is called feature selection. In this paper we deal with multivariate time-dependent series of data points for energy forecasting in smart buildings. We propose a methodology to transform the time-dependent database into a structure that standard machine learning algorithms can process, and then, apply different types of feature selection methods for regression tasks. We used Weka for the tasks of database transformation, feature selection, regression, statistical test and forecasting. The proposed methodology improves MAE by 59.97% and RMSE by 40.75%, evaluated on training data, and it improves MAE by 42.28% and RMSE by 36.62%, evaluated on test data, on average for 1-step-ahead, 2-step-ahead and 3-step-ahead when compared to not applying any feature selection methodology.