Predicting the performance of hybrid ventilation in buildings using a multivariate attention-based biLSTM Encoder - Decoder

Hybrid ventilation is an energy-efficient solution to provide fresh air for most climates, given that it has a reliable control system. To operate such systems optimally, a high-fidelity control-oriented modesl is required. It should enable near-real time forecast of the indoor air temperature based on operational conditions such as window opening and HVAC operating schedules. However, physics-based control-oriented models (i.e., white-box models) are labour-intensive and computationally expensive. Alternatively, black-box models based on artificial neural networks can be trained to be good estimators for building dynamics. This paper investigates the capabilities of a deep neural network (DNN), which is a multivariate multi-head attention-based long short-term memory (LSTM) encoder-decoder neural network, to predict indoor air temperature when windows are opened or closed. Training and test data are generated from a detailed multi-zone office building model (EnergyPlus). Pseudo-random signals are used for the indoor air temperature setpoints and window opening instances. The results indicate that the DNN is able to accurately predict the indoor air temperature of five zones whenever windows are opened or closed. The prediction error plateaus after the 24th step ahead prediction (6 hr ahead prediction).</p

Measurement Time Reduction by Means of Mathematical Modeling of Enzyme Mediated RedOx Reaction in Food Samples Biosensors

The possibility of measuring in real time the different types of analytes present in food is becoming a requirement in food industry. In this context, biosensors are presented as an alternative to traditional analytical methodologies due to their specificity, high sensitivity and ability to work in real time. It has been observed that the behavior of the analysis curves of the biosensors follow a trend that is reproducible among all the measurements and that is specific to the reaction that occurs in the electrochemical cell and the analyte being analyzed. Kinetic reaction modeling is a widely used method to model processes that occur within the sensors, and this leads to the idea that a mathematical approximation can mimic the electrochemical reaction that takes place while the analysis of the sample is ongoing. For this purpose, a novel mathematical model is proposed to approximate the enzymatic reaction within the biosensor in real time, so the output of the measurement can be estimated in advance. The proposed model is based on adjusting an exponential decay model to the response of the biosensors using a nonlinear least-square method to minimize the error. The obtained results show that our proposed approach is capable of reducing about 40% the required measurement time in the sample analysis phase, while keeping the error rate low enough to meet the accuracy standards of the food industry

다 변수 시계열 예측을 위한 주의 기반 LSTM 모델 연구

학위논문(석사)--서울대학교 대학원 :공과대학 컴퓨터공학부,2019. 8. Kang, U.Given previous observations of a multivariate time-series, how can we accurately predict the future value of several steps ahead? With the continuous development of sensor systems and computer systems, time-series prediction techniques are playing more and more important roles in various fields, such as finance, energy, and traffics. Many models have been proposed for time-series prediction tasks, such as Autoregressive model, Vector Autoregressive model, and Recurrent Neural Networks (RNNs). However, these models still have limitations like failure in modeling non-linearity and long-term dependencies in time-series. Among all the proposed approaches, the Temporal Pattern Attention (TPA), which is an attention-based LSTM model, achieves state-of-the-art performance on several real-world multivariate time-series datasets. In this thesis, we study three factors that effect the prediction performance of TPA model, which are the Recurrent Neural Network RNN layer, the attention mechanism, and the Convolutional Neural Network for temporal patter detection. For recurrent layer, we implement bi-directional LSTMs that can extract information from the input sequence in both forward and backward directions. In addition, we design two attention mechanisms, each of which assigns attention weights in different directions. We study the effect of both attention mechanisms on TPA model. Finally, to validate the Convolutional Neural Network (CNN) for temporal pattern detection, we implement a TPA model without CNN. We test all of these factors using several real-world time-series datasets from different fields. The experimental results indicate the validity of these factors.I. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 II. Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2.1 Long Short-term Memory . . . . . . . . . . . . . . . . . . . . . . . . . 4 2.2 Typical Attention Mechanism . . . . . . . . . . . . . . . . . . . . . . 5 2.3 Temporal Pattern Attention Model . . . . . . . . . . . . . . . . . . . . 6 III. Study on Temporal Pattern Attention Model . . . . . . . . . . . . . . 9 3.1 Problem Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 3.2 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 3.3 Recurrent Neural Network Layer . . . . . . . . . . . . . . . . . . . . . 10 3.4 Vertical v.s. Horizontal Attention Mechanism . . . . . . . . . . . . . . 12 3.5 Temporal Pattern Attention Model without CNN . . . . . . . . . . . . 14 IV. Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 4.1 Experimental Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 4.2 Performance Comparison (Q1) . . . . . . . . . . . . . . . . . . . . . . 18 4.3 Effects of Bi-directional LSTM (Q2) . . . . . . . . . . . . . . . . . . . 20 4.4 Effects of CNN for Temporal Pattern Detection (Q3) . . . . . . . . . . 22 4.5 Which Attention Direction Is Better (Q4) . . . . . . . . . . . . . . . . 23 V. RelatedWorks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 VI. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30Maste