Deep Probabilistic Time Series Forecasting using Augmented Recurrent Input for Dynamic Systems

The demand of probabilistic time series forecasting has been recently raised in various dynamic system scenarios, for example, system identification and prognostic and health management of machines. To this end, we combine the advances in both deep generative models and state space model (SSM) to come up with a novel, data-driven deep probabilistic sequence model. Specially, we follow the popular encoder-decoder generative structure to build the recurrent neural networks (RNN) assisted variational sequence model on an augmented recurrent input space, which could induce rich stochastic sequence dependency. Besides, in order to alleviate the issue of inconsistency between training and predicting as well as improving the mining of dynamic patterns, we (i) propose using a hybrid output as input at next time step, which brings training and predicting into alignment; and (ii) further devise a generalized auto-regressive strategy that encodes all the historical dependencies at current time step. Thereafter, we first investigate the methodological characteristics of the proposed deep probabilistic sequence model on toy cases, and then comprehensively demonstrate the superiority of our model against existing deep probabilistic SSM models through extensive numerical experiments on eight system identification benchmarks from various dynamic systems. Finally, we apply our sequence model to a real-world centrifugal compressor sensor data forecasting problem, and again verify its outstanding performance by quantifying the time series predictive distribution.Comment: 25 pages, 7 figures, 4 tables, preprint under revie

Energy load forecast in smart buildings with deep learning techniques

Predicting energy load is a growing problem these days. The need to study in advance how electricity consumption will behave is key to resource management. Especially interesting is the case of the so-called Smart Buildings, buildings born from the trend towards sustainable development and consumption which is increasingly in vogue, becoming mandatory by law in many countries. One type of model that constitutes an important part of the state of the art are the models based on Deep Learning. These models represented great advances in Artificial Intelligence recently, since although they were born in the 20th century, it has not been until 10 years ago that they have re-emerged thanks to the computational advances that allow them to be trained by the general public. In this Final Degree Project, advanced Deep Learning techniques applied to the problem of load prediction in Smart Buildings are presented, mainly basing the development on the data from the Alice Perry building of the National University of Ireland Galway, in collaboration with the Informatics Research Unit for Sustainable Engineering of the same university. The datasets used were obtained from the time series of aggregated electricity consumption of the air handling units (AHUs) in the Alice Perry building. Along with this information, historical weather data were also collected from the weather station in the same building in order to study if these climatic variables help to a better prediction in the models. Time series prediction on this energy load data will be made in two different ways with hourly granularity: one-step prediction in which studying the previous observations an estimate of the value of the load in the next hour is obtained and sequence prediction, in which we will try to predict the behaviour of the series in the next hours from the previous values.La predicción de carga energética es un problema al alza actualmente. La necesidad de estudiar con antelación cómo se va a comportar el consumo eléctrico es clave para la gestión de recursos. Especialmente interesante es el caso de los llamados Smart Buildings, edificios nacidos por la tendencia hacia un desarrollo y consumo sostenible el cual cada vez está más en boga, llegando a ser obligatorio por ley en muchos países. Un tipo de modelos que constituyen una parte importante del estado del arte son los modelos basados en Deep Learning. Estos modelos supusieron grandes avances en la Inteligencia Artificial recientemente, ya que aunque nacidos en el Siglo XX, no ha sido hasta escasos 10 años cuando han resurgido gracias a los avances computacionales que permiten entrenarlos por el público general. En este trabajo de fin de grado se presentan técnicas avanzadas de Deep Learning aplicadas al problema de la predicción de carga en Smart Buildings, principalmente basando el desarrollo en los datos del edificio Alice Perry de la National University of Ireland Galway, en colaboración con el grupo Informatics Research Unit for Sustainable Engineering de la misma universidad. Los conjuntos de datos utilizados se obtuvieron datos sobre la serie temporal de consumo eléctrico agregado de los aires acondicionados en el edificio Alice Perry. Junto a esta información, se recopilaron también datos meteorológicos históricos de la estación meteorológica en el mismo edificio con el objetivo de estudiar si estas variables climáticas ayudan a una mejor predicción en los modelos. La predicción de series temporales sobre estos datos de carga energética se realizará en dos modos con granularidad horaria: La predicción a un paso en la que estudiando las observaciones anteriores se obtiene una estimación del valor de la carga en la próxima hora y predicción de secuencias, en la que se intentará predecir el comportamiento de la serie en las próximas horas a partir de los valores anteriores.Grado en Ingeniería Informátic

LSTM-enabled Level Curve Tracking in Scalar Fields Using Multiple Mobile Robots

Autonomous mobile sensor networks are ideal candidates for exploring large-scaleunknown fields with tasks ranging from source seeking, level curve tracking, mapping an unknown field, and many more. In this work, we investigate the problem of level curve tracking in unknown scalar fields using a limited number of mobile sensors. The level curve tracking problem has been studied in many applications such as monitoring the propagation of fire boundaries and the algae blooms. We design and implement a long short term memory (LSTM) enabled control strategy for a mobile sensor network to detect and track the desired level curve. We develop on top of existing research which uses cooperative Kalman Filter as part of its motion control strategy. This existing method is theoretically proven to converge. The LSTM enabled strategy has some benefits such as it can be trained offline on a collection of level curves in known fields prior to deployment, where the trained model will enable the mobile sensor network to track level curves in unknown fields for various applications. So we can train using larger resources to get a more accurate model, while we can utilize a limited number of resources when the mobile sensor network is deployed in the production. We design and implement an LSTM-enhanced cooperative Kalman Filter that utilizes the sensor measurements and a sequence of past fields and gradients to estimates the current field value and gradient. We also design an LSTM model to estimate the Hessian of the field. We utilize these estimates of the field characteristics with motion controllers to track the desired level curve in an unknown field with the center of the sensor network. Simulation results show that this LSTM enabled control strategy successfully tracks the level curve using a mobile multi-robot sensor network