56 research outputs found

    Modeling Energy Demand—A Systematic Literature Review

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    In this article, a systematic literature review of 419 articles on energy demand modeling, published between 2015 and 2020, is presented. This provides researchers with an exhaustive overview of the examined literature and classification of techniques for energy demand modeling. Unlike in existing literature reviews, in this comprehensive study all of the following aspects of energy demand models are analyzed: techniques, prediction accuracy, inputs, energy carrier, sector, temporal horizon, and spatial granularity. Readers benefit from easy access to a broad literature base and find decision support when choosing suitable data-model combinations for their projects. Results have been compiled in comprehensive figures and tables, providing a structured summary of the literature, and containing direct references to the analyzed articles. Drawbacks of techniques are discussed as well as countermeasures. The results show that among the articles, machine learning (ML) techniques are used the most, are mainly applied to short-term electricity forecasting on a regional level and rely on historic load as their main data source. Engineering-based models are less dependent on historic load data and cover appliance consumption on long temporal horizons. Metaheuristic and uncertainty techniques are often used in hybrid models. Statistical techniques are frequently used for energy demand modeling as well and often serve as benchmarks for other techniques. Among the articles, the accuracy measured by mean average percentage error (MAPE) proved to be on similar levels for all techniques. This review eases the reader into the subject matter by presenting the emphases that have been made in the current literature, suggesting future research directions, and providing the basis for quantitative testing of hypotheses regarding applicability and dominance of specific methods for sub-categories of demand modeling.BMBF, 03SFK4T0, Verbundvorhaben ENavi: Energiewende-Navigationssystem zur Erfassung, Analyse und Simulation der systemischen Vernetzungen" - Teilvorhaben T0BMWi, 03ET4040C, Verbundvorhaben: Harmonisierung und Entwicklung von Verfahren zur regional und zeitlich aufgelösten Modellierung von Energienachfragen (DemandRegio) Teilvorhaben: ProfileDFG, 414044773, Open Access Publizieren 2021 - 2022 / Technische Universität Berli

    TĂ©cnicas big data para el procesamiento de flujos de datos masivos en tiempo real

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    Programa de Doctorado en Biotecnología, Ingeniería y Tecnología QuímicaLínea de Investigación: Ingeniería, Ciencia de Datos y BioinformáticaClave Programa: DBICódigo Línea: 111Machine learning techniques have become one of the most demanded resources by companies due to the large volume of data that surrounds us in these days. The main objective of these technologies is to solve complex problems in an automated way using data. One of the current perspectives of machine learning is the analysis of continuous flows of data or data streaming. This approach is increasingly requested by enterprises as a result of the large number of information sources producing time-indexed data at high frequency, such as sensors, Internet of Things devices, social networks, etc. However, nowadays, research is more focused on the study of historical data than on data received in streaming. One of the main reasons for this is the enormous challenge that this type of data presents for the modeling of machine learning algorithms. This Doctoral Thesis is presented in the form of a compendium of publications with a total of 10 scientific contributions in International Conferences and journals with high impact index in the Journal Citation Reports (JCR). The research developed during the PhD Program focuses on the study and analysis of real-time or streaming data through the development of new machine learning algorithms. Machine learning algorithms for real-time data consist of a different type of modeling than the traditional one, where the model is updated online to provide accurate responses in the shortest possible time. The main objective of this Doctoral Thesis is the contribution of research value to the scientific community through three new machine learning algorithms. These algorithms are big data techniques and two of them work with online or streaming data. In this way, contributions are made to the development of one of the current trends in Artificial Intelligence. With this purpose, algorithms are developed for descriptive and predictive tasks, i.e., unsupervised and supervised learning, respectively. Their common idea is the discovery of patterns in the data. The first technique developed during the dissertation is a triclustering algorithm to produce three-dimensional data clusters in offline or batch mode. This big data algorithm is called bigTriGen. In a general way, an evolutionary metaheuristic is used to search for groups of data with similar patterns. The model uses genetic operators such as selection, crossover, mutation or evaluation operators at each iteration. The goal of the bigTriGen is to optimize the evaluation function to achieve triclusters of the highest possible quality. It is used as the basis for the second technique implemented during the Doctoral Thesis. The second algorithm focuses on the creation of groups over three-dimensional data received in real-time or in streaming. It is called STriGen. Streaming modeling is carried out starting from an offline or batch model using historical data. As soon as this model is created, it starts receiving data in real-time. The model is updated in an online or streaming manner to adapt to new streaming patterns. In this way, the STriGen is able to detect concept drifts and incorporate them into the model as quickly as possible, thus producing triclusters in real-time and of good quality. The last algorithm developed in this dissertation follows a supervised learning approach for time series forecasting in real-time. It is called StreamWNN. A model is created with historical data based on the k-nearest neighbor or KNN algorithm. Once the model is created, data starts to be received in real-time. The algorithm provides real-time predictions of future data, keeping the model always updated in an incremental way and incorporating streaming patterns identified as novelties. The StreamWNN also identifies anomalous data in real-time allowing this feature to be used as a security measure during its application. The developed algorithms have been evaluated with real data from devices and sensors. These new techniques have demonstrated to be very useful, providing meaningful triclusters and accurate predictions in real time.Universidad Pablo de Olavide de Sevilla. Departamento de Deporte e informátic

    Asynchronous dual-pipeline deep learning framework for online data stream classification

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    Data streaming classification has become an essential task in many fields where real-time decisions have to be made based on incoming information. Neural networks are a particularly suitable technique for the streaming scenario due to their incremental learning nature. However, the high computation cost of deep architectures limits their applicability to high-velocity streams, hence they have not yet been fully explored in the literature. Therefore, in this work, we aim to evaluate the effectiveness of complex deep neural networks for supervised classification in the streaming context. We propose an asynchronous deep learning framework in which training and testing are performed simultaneously in two different processes. The data stream entering the system is dual fed into both layers in order to concurrently provide quick predictions and update the deep learning model. This separation reduces processing time while obtaining high accuracy on classification. Several time-series datasets from the UCR repository have been simulated as streams to evaluate our proposal, which has been compared to other methods such as Hoeffding trees, drift detectors, and ensemble models. The statistical analysis carried out verifies the improvement in performance achieved with our dual-pipeline deep learning framework, that is also competitive in terms of computation time.Ministerio de EconomĂ­a y Competitividad TIN2017-88209-C2-2-

    Energy Data Analytics for Smart Meter Data

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    The principal advantage of smart electricity meters is their ability to transfer digitized electricity consumption data to remote processing systems. The data collected by these devices make the realization of many novel use cases possible, providing benefits to electricity providers and customers alike. This book includes 14 research articles that explore and exploit the information content of smart meter data, and provides insights into the realization of new digital solutions and services that support the transition towards a sustainable energy system. This volume has been edited by Andreas Reinhardt, head of the Energy Informatics research group at Technische Universität Clausthal, Germany, and Lucas Pereira, research fellow at Técnico Lisboa, Portugal

    Algorithmes de bandits stochastiques pour la gestion de la demande Ă©lectrique

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    As electricity is hard to store, the balance between production and consumption must be strictly maintained. With the integration of intermittent renewable energies into the production mix, the management of the balance becomes complex. At the same time, the deployment of smart meters suggests demand response. More precisely, sending signals - such as changes in the price of electricity - would encourage users to modulate their consumption according to the production of electricity. The algorithms used to choose these signals have to learn consumer reactions and, in the same time, to optimize them (exploration-exploration trade-off). Our approach is based on bandit theory and formalizes this sequential learning problem. We propose a first algorithm to control the electrical demand of a homogeneous population of consumers and offer T⅔ upper bound on its regret. Experiments on a real data set in which price incentives were offered illustrate these theoretical results. As a “full information” dataset is required to test bandit algorithms, a consumption data generator based on variational autoencoders is built. In order to drop the assumption of the population homogeneity, we propose an approach to cluster households according to their consumption profile. These different works are finally combined to propose and test a bandit algorithm for personalized demand side management.L'électricité se stockant difficilement à grande échelle, l'équilibre entre la production et la consommation doit être rigoureusement maintenu. Une gestion par anticipation de la demande se complexifie avec l'intégration au mix de production des énergies renouvelables intermittentes. Parallèlement, le déploiement des compteurs communicants permet d'envisager un pilotage dynamique de la consommation électrique. Plus concrètement, l'envoi de signaux - tels que des changements du prix de l'électricité – permettrait d'inciter les usagers à moduler leur consommation afin qu'elle s'ajuste au mieux à la production d'électricité. Les algorithmes choisissant ces signaux devront apprendre la réaction des consommateurs face aux envois tout en les optimisant (compromis exploration-exploitation). Notre approche, fondée sur la théorie des bandits, a permis de formaliser ce problème d'apprentissage séquentiel et de proposer un premier algorithme pour piloter la demande électrique d'une population homogène de consommateurs. Une borne supérieure d'ordre T⅔ a été obtenue sur le regret de cet algorithme. Des expériences réalisées sur des données de consommation de foyers soumis à des changements dynamiques du prix de l'électricité illustrent ce résultat théorique. Un jeu de données en « information complète » étant nécessaire pour tester un algorithme de bandits, un simulateur de données de consommation fondé sur les auto-encodeurs variationnels a ensuite été construit. Afin de s'affranchir de l'hypothèse d'homogénéité de la population, une approche pour segmenter les foyers en fonction de leurs habitudes de consommation est aussi proposée. Ces différents travaux sont finalement combinés pour proposer et tester des algorithmes de bandits pour un pilotage personnalisé de la consommation électrique

    15th SC@RUG 2018 proceedings 2017-2018

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    15th SC@RUG 2018 proceedings 2017-2018

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    15th SC@RUG 2018 proceedings 2017-2018

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    15th SC@RUG 2018 proceedings 2017-2018

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