Thresholding methods in non-intrusive load monitoring

Non-intrusive load monitoring (NILM) is the problem of predicting the status or consumption of individual domestic appliances only from the knowledge of the aggregated power load. NILM is often formulated as a classifcation (ON/OFF) problem for each device. However, the training datasets gathered by smart meters do not contain these labels, but only the electric consumption at every time interval. This paper addresses a fundamental methodological problem in how a NILM problem is posed, namely how the diferent possible thresholding methods lead to diferent classifcation problems. Standard datasets and NILM deep learning models are used to illustrate how the choice of thresholding method afects the output results. Some criteria that should be considered for the choice of such methods are also proposed. Finally, we propose a slight modifcation to current deep learning models for multi-tasking, i.e. tackling the classifcation and regression problems simultaneously. Transfer learning between both problems might improve performance on each of them.Funding for open access publishing: Universidad de Cádiz/CBUA. This research has been financed in part by the Spanish Agencia Estatal de Investigación under grants PID2021-122154NB-I00 and TED2021-129455B-I00, and by a 2021 BBVA Foundation project for research in Mathematics. He also acknowledges support from the EU under the 2014-2020 ERDF Operational Programme and the Department of Economy, Knowledge, Business and University of the Regional Government of Andalusia (FEDER-UCA18-108393)

TUN-AI: Tuna biomass estimation with Machine Learning models trained on oceanography and echosounder FAD data

The use of dFADs by tuna purse-seine fisheries is widespread across oceans, and the echo-sounder buoys attached to these dFADs provide fishermen with estimates of tuna biomass aggregated to them. This information has potential for gaining insight into tuna behaviour and abundance, but has traditionally been difficult to process and use. The current study combines FAD logbook data, oceanographic data and echo-sounder buoy data to evaluate different Machine Learning models and establish a pipeline, named TUN-AI, for processing echo-sounder buoy data and estimating tuna biomass (in metric tons, t) at various levels of complexity: binary classification, ternary classification and regression. Models were trained and tested on over 5000 sets and over 6000 deployments. Of all the models evaluated, the best performing one uses a 3-day window of echo-sounder data, oceanographic data and position/time derived features. This model is able to estimate if tuna biomass was higher than 10 t or lower than 10 t with an F1-score of 0.925. When directly estimating tuna biomass, the best model (Gradient Boosting) has an error (MAE) of 21.6 t and a relative error (SMAPE) of 29.5%, when evaluated over sets. All models tested improved when enriched with oceanographic and position-derived features, highlighting the importance of these features when using echo-sounder buoy data. Potential applications of this methodology, and future improvements, are discussed.12 página

Applications of machine learning and data science to the blue economy sustainable fishing and weather routing

The Blue Economy encompasses an interdisciplinary field of study aimed at achieving sustainable utilization of ocean resources while preserving the environment’s health. The importance of this concept lies in its role in achieving the Sustainable Development Goals defined by the United Nations. Nevertheless, the pursuit of economic development can often conflict with the principles of sustainability, underscoring the necessity of leveraging adequate tools to address these challenges. Data science, and particularly Machine Learning, has become a valuable tool for addressing the challenges of the Blue Economy. For example, in the field of sustainable fishing, monitoring fish populations is highly relevant and can be achieved through Machine Learning models. In another area, such as maritime transport, the implementation of weather routing tools can optimize sea routes, improving fuel efficiency and ensuring a reduction in greenhouse gas emissions. This thesis will delve into the study of sustainable fishing and weather routing in the context of the Blue Economy, applying data science techniques to improve efficiency and sustainability in both fieldsLa Economía Azul ha surgido como un campo de estudio interdisciplinario que busca aprovechar los recursos del océano de manera sostenible y preservar su salud ambiental. Este concepto se ha vuelto cada vez más importante para alcanzar los Objetivos de Desarrollo Sostenible de las Naciones Unidas. Sin embargo, el desarrollo económico y la sostenibilidad pueden entrar en conflicto, lo que destaca la necesidad de abordar estos desafíos con herramientas adecuadas. La ciencia de datos, y en particular el aprendizaje automático (Machine Learning), se ha convertido en una herramienta valiosa para abordar los desafíos de la Economía Azul. Por ejemplo, en el ámbito de la pesca sostenible, es muy relevante la monitorización de poblaciones de peces, que se puede realizar mediante modelos de Machine Learning. En otro ámbito, como es el transporte marítimo, la implementación de herramientas de “weather routing” puede optimizar las rutas por mar, mejorando la eficiencia en el consumo de combustible y garantizando una reducción en las emisiones de gases de efecto invernadero. En esta tesis se profundizará en el estudio de la pesca sostenible y el weather routing en el contexto de la Economía Azul, aplicando técnicas de ciencia de datos para mejorar la eficiencia y sostenibilidad en ambos campos.146 página