Publications

Publications from our Researchers

Several of our current PhD candidates and fellow researchers at the Data Science Institute have published, or in the proccess of publishing, papers to present their research.  

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  • JOURNAL ARTICLE
    Gomez-Romero J, Fernandez-Basso CJ, Cambronero MV, Molina-Solana M, Campana JR, Ruiz MD, Martin-Bautista MJet al., 2019,

    A probabilistic algorithm for predictive control with full-complexity models in non-residential buildings

    , IEEE Access, Vol: 7, Pages: 38748-38765, ISSN: 2169-3536

    Despite the increasing capabilities of information technologies for data acquisition and processing, building energy management systems still require manual configuration and supervision to achieve optimal performance. Model predictive control (MPC) aims to leverage equipment control – particularly heating, ventilation and air conditioning (HVAC)– by using a model of the building to capture its dynamic characteristics and to predict its response to alternative control scenarios. Usually, MPC approaches are based on simplified linear models, which support faster computation but also present some limitations regarding interpretability, solution diversification and longer-term optimization. In this work, we propose a novel MPC algorithm that uses a full-complexity grey-box simulation model to optimize HVAC operation in non-residential buildings. Our system generates hundreds of candidate operation plans, typically for the next day, and evaluates them in terms of consumption and comfort by means of a parallel simulator configured according to the expected building conditions (weather, occupancy, etc.) The system has been implemented and tested in an office building in Helsinki, both in a simulated environment and in the real building, yielding energy savings around 35% during the intermediate winter season and 20% in the whole winter season with respect to the current operation of the heating equipment.
  • JOURNAL ARTICLE
    Rueda R, Cuellar MP, Molina-Solana M, Guo Y, Pegalajar MCet al., 2019,

    Generalised Regression Hypothesis Induction for Energy Consumption Forecasting

    , ENERGIES, Vol: 12, ISSN: 1996-1073
  • JOURNAL ARTICLE
    Gomez-Romero J, Molina-Solana M, Oehmichen A, Guo Yet al., 2018,

    Visualizing large knowledge graphs: A performance analysis

    , FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, Vol: 89, Pages: 224-238, ISSN: 0167-739X
  • JOURNAL ARTICLE
    Amador Diaz Lopez J, Molina-Solana M, Kennedy MT, 2018,

    foo.castr: visualising the future AI workforce

    , Big Data Analytics, Vol: 3
  • JOURNAL ARTICLE
    Dolan D, Jensen HJ, Mediano PAM, Molina-Solana M, Rajpal H, Rosas F, Sloboda JAet al., 2018,

    The Improvisational State of Mind: A Multidisciplinary Study of an Improvisatory Approach to Classical Music Repertoire Performance

    , FRONTIERS IN PSYCHOLOGY, Vol: 9, ISSN: 1664-1078
  • JOURNAL ARTICLE
    Gómez-Romero J, Molina-Solana M, Ros M, Ruiz MD, Martin-Bautista MJet al., 2018,

    Comfort as a service: a new paradigm for residential environmental quality control

    , Sustainability, Vol: 10, ISSN: 1937-0709

    This paper introduces the concept of Comfort as a Service (CaaS), a new energy supply paradigm for providing comfort to residential customers. CaaS takes into account the available passive and active elements, the external factors that affect energy consumption and associated costs, and occupants' behaviors to generate optimal control strategies for the domestic equipment automatically. As a consequence, it releases building occupants from operating the equipment, which gives rise to a disruption of the traditional model of paying per consumed energy in favor of a model of paying per provided comfort. In the paper, we envision a realization of CaaS based on several technologies such as ambient intelligence, big data, cloud computing and predictive computing. We discuss the opportunities and the barriers of CaaS-centered business and exemplify the potential of CaaS deployments by quantifying the expected energy savings achieved after limiting occupants' control over the air conditioning system in a test scenario.
  • JOURNAL ARTICLE
    Creswell A, Bharath AA, 2018,

    Denoising adversarial autoencoders

    , IEEE Transactions on Neural Networks and Learning Systems, ISSN: 2162-2388

    Unsupervised learning is of growing interest becauseit unlocks the potential held in vast amounts of unlabelled data tolearn useful representations for inference. Autoencoders, a formof generative model, may be trained by learning to reconstructunlabelled input data from a latent representation space. Morerobust representations may be produced by an autoencoderif it learns to recover clean input samples from corruptedones. Representations may be further improved by introducingregularisation during training to shape the distribution of theencoded data in the latent space. We suggestdenoising adversarialautoencoders, which combine denoising and regularisation, shap-ing the distribution of latent space using adversarial training.We introduce a novel analysis that shows how denoising maybe incorporated into the training and sampling of adversarialautoencoders. Experiments are performed to assess the contri-butions that denoising makes to the learning of representationsfor classification and sample synthesis. Our results suggest thatautoencoders trained using a denoising criterion achieve higherclassification performance, and can synthesise samples that aremore consistent with the input data than those trained withouta corruption process.
  • JOURNAL ARTICLE
    Song J, Fan S, Lin W, Mottet L, Woodward H, Wykes MD, Arcucci R, Xiao D, Debay J-E, ApSimon H, Aristodemou E, Birch D, Carpentieri M, Fang F, Herzog M, Hunt GR, Jones RL, Pain C, Pavlidis D, Robins AG, Short CA, Linden PFet al., 2018,

    Natural ventilation in cities: the implications of fluid mechanics

    , BUILDING RESEARCH AND INFORMATION, Vol: 46, Pages: 809-828, ISSN: 0961-3218
  • JOURNAL ARTICLE
    Jahani E, Sundsøy P, Bjelland J, Bengtsson L, Pentland AS, de Montjoye Y-Aet al., 2017,

    Improving official statistics in emerging markets using machine learning and mobile phone data

    , EPJ Data Science, Vol: 6
  • JOURNAL ARTICLE
    Molina-Solana M, Birch D, Guo Y-K, 2017,

    Improving data exploration in graphs with fuzzy logic and large-scale visualisation

    , APPLIED SOFT COMPUTING, Vol: 53, Pages: 227-235, ISSN: 1568-4946
  • JOURNAL ARTICLE
    Molina-Solana M, Ros M, Dolores Ruiz M, Gomez-Romero J, Martin-Bautista MJet al., 2017,

    Data science for building energy management: A review

    , RENEWABLE & SUSTAINABLE ENERGY REVIEWS, Vol: 70, Pages: 598-609, ISSN: 1364-0321
  • JOURNAL ARTICLE
    Steele JE, Sundsoy PR, Pezzulo C, Alegana VA, Bird TJ, Blumenstock J, Bjelland J, Engo-Monsen K, de Montjoye Y-A, Iqbal AM, Hadiuzzaman KN, Lu X, Wetter E, Tatem AJ, Bengtsson Let al., 2017,

    Mapping poverty using mobile phone and satellite data

    , JOURNAL OF THE ROYAL SOCIETY INTERFACE, Vol: 14, ISSN: 1742-5689
  • JOURNAL ARTICLE
    de Montjoye YKJV, Rocher L, Pentland AS, 2016,

    bandicoot: an open-source Python toolbox to analyze mobile phone metadata

    , Journal of Machine Learning Research, Vol: 17, ISSN: 1532-4435

    bandicoot is an open-source Python toolbox to extract more than 1442 features from standard mobile phone metadata. bandicoot makes it easy for machine learning researchers and practitioners to load mobile phone data, to analyze and visualize them, and to extract robust features which can be used for various classification and clustering tasks. Emphasis is put on ease of use, consistency, and documentation. bandicoot has no dependencies and is distributed under MIT license
  • JOURNAL ARTICLE
    Ma Z-B, Yang Y, Liu Y-X, Bharath AAet al., 2016,

    Recurrently Decomposable 2-D Convolvers for FPGA-Based Digital Image Processing

    , IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS, Vol: 63, Pages: 979-983, ISSN: 1549-7747
  • JOURNAL ARTICLE
    Taquet M, Quoidbach J, de Montjoye Y-A, Desseilles M, Gross JJet al., 2016,

    Hedonism and the choice of everyday activities

    , Proceedings of the National Academy of Sciences, Vol: 113, Pages: 9769-9773, ISSN: 0027-8424

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