Towards Sustainable Deep Learning for Multi-Label Classification on NILM

Non-intrusive load monitoring (NILM) is the process of obtaining appliance-level data from a single metering point, measuring total electricity consumption of a household or a business. Appliance-level data can be directly used for demand response applications and energy management systems as well as for awareness raising and motivation for improvements in energy efficiency and reduction in the carbon footprint. Recently, classical machine learning and deep learning (DL) techniques became very popular and proved as highly effective for NILM classification, but with the growing complexity these methods are faced with significant computational and energy demands during both their training and operation. In this paper, we introduce a novel DL model aimed at enhanced multi-label classification of NILM with improved computation and energy efficiency. We also propose a testing methodology for comparison of different models using data synthesized from the measurement datasets so as to better represent real-world scenarios. Compared to the state-of-the-art, the proposed model has its carbon footprint reduced by more than 23% while providing on average approximately 8 percentage points in performance improvement when testing on data derived from REFIT and UK-DALE datasets

Contextualized Question Answering

The paper describes a system which enables accurate and easy-to-use contextualized question answering and it provides document overview functionalities. The possibility of asking natural language questions enables a friendly interaction for the user.The contextualization is achieved by using an ontology. The answers are provided based on a domain specific document collection of choice. The approach consists of several phases as follows: data preparation, data enhancement, data indexing and handling questions. Every module uses state of the art technologies that are shown to work in a complex pipeline to make available question answering on top of a given document repository with the context of ontologies, such as Cyc, ASFA and WordNet. The functioning of the proposed approach is demonstrated on English document collections on Aquatic Sciences and Fisheries — ASFA, using Cyc ontology, ASFA thesaurus as domain specific ontology and WordNet as general ontology. Experimental evaluation has shown that the usage of ontologies increases the number of answers retrieved by about 60%. However, the number of answers that are actually correct increases by only 40% when using ontologies

System for semi-automatic ontology construction

In this paper, we review two techniques for topic discovery in collections of text documents (Latent Semantic Indexing and K-Means clustering) and present how we integrated them into a system for semiautomatic topic ontology construction. The system offers supports to the user during the construction process by suggesting topics and analyzing them in real time