Semi-supervised transfer learning methodology for fault detection and diagnosis in air-handling units

Heating, ventilation and air-conditioning (HVAC) systems are the major energy consumers among buildings’ equipment. Reliable fault detection and diagnosis schemes can effectively reduce their energy consumption and maintenance costs. In this respect, data-driven approaches have shown impressive results, but their accuracy depends on the availability of representative data to train the models, which is not common in real applications. For this reason, transfer learning is attracting growing attention since it tackles the problem by leveraging the knowledge between datasets, increasing the representativeness of fault scenarios. However, to date, research on transfer learning for heating, ventilation and air-conditioning has mostly been focused on learning algorithmic, overlooking the importance of a proper domain similarity analysis over the available data. Thus, this study proposes the design of a transfer learning approach based on a specific data selection methodology to tackle dissimilarity issues. The procedure is supported by neural network models and the analysis of eventual prediction uncertainties resulting from the assessment of the target application samples. To verify the proposed methodology, it is applied to a semi-supervised transfer learning case study composed of two publicly available air-handling unit datasets containing some fault scenarios. Results emphasize the potential of the proposed domain dissimilarity analysis reaching a classification accuracy of 92% under a transfer learning framework, an increase of 37% in comparison to classical approaches.Objectius de Desenvolupament Sostenible::11 - Ciutats i Comunitats SosteniblesObjectius de Desenvolupament Sostenible::12 - Producció i Consum ResponsablesPostprint (published version

Data fusion strategies for energy efficiency in buildings: Overview, challenges and novel orientations

Recently, tremendous interest has been devoted to develop data fusion strategies for energy efficiency in buildings, where various kinds of information can be processed. However, applying the appropriate data fusion strategy to design an efficient energy efficiency system is not straightforward; it requires a priori knowledge of existing fusion strategies, their applications and their properties. To this regard, seeking to provide the energy research community with a better understanding of data fusion strategies in building energy saving systems, their principles, advantages, and potential applications, this paper proposes an extensive survey of existing data fusion mechanisms deployed to reduce excessive consumption and promote sustainability. We investigate their conceptualizations, advantages, challenges and drawbacks, as well as performing a taxonomy of existing data fusion strategies and other contributing factors. Following, a comprehensive comparison of the state-of-the-art data fusion based energy efficiency frameworks is conducted using various parameters, including data fusion level, data fusion techniques, behavioral change influencer, behavioral change incentive, recorded data, platform architecture, IoT technology and application scenario. Moreover, a novel method for electrical appliance identification is proposed based on the fusion of 2D local texture descriptors, where 1D power signals are transformed into 2D space and treated as images. The empirical evaluation, conducted on three real datasets, shows promising performance, in which up to 99.68% accuracy and 99.52% F1 score have been attained. In addition, various open research challenges and future orientations to improve data fusion based energy efficiency ecosystems are explored

Improving Domain Adaptation By Source Selection

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