Browsing by Subject "LSTM"

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    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.
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    Publication
    Open Access
    Imagery time series cloud removal and classification using long short term memory neural networks
    (MDPI, 2024-06-13) Alonso Sarría, Francisco; Valdivieso Ros, Carmen; Gomariz Castillo, Francisco; Geografía
    The availability of high spatial and temporal resolution imagery, such as that provided by the Sentinel satellites, allows the use of image time series to classify land cover. Recurrent neural networks (RNNs) are a clear candidate for such an approach; however, the presence of clouds poses a difficulty. In this paper, random forest (RF) and RNNs are used to reconstruct cloud-covered pixels using data from other next in time images instead of pixels in the same image. Additionally, two RNN architectures are tested to classify land cover from the series, treating reflectivities as time series and also treating spectral signatures as time series. The results are compared with an RF classification. The results for cloud removal show a high accuracy with a maximum RMSE of 0.057 for RNN and 0.038 for RF over all images and bands analysed. In terms of classification, the RNN model obtained higher accuracy (over 0.92 in the test data for the best hyperparameter combinations) than the RF model (0.905). However, the temporal–spectral model accuracies did not reach 0.9 in any case.