Energy Analytics for Infrastructure: An Application to Institutional Buildings

abstract: Commercial buildings in the United States account for 19% of the total energy consumption annually. Commercial Building Energy Consumption Survey (CBECS), which serves as the benchmark for all the commercial buildings provides critical input for EnergyStar models. Smart energy management technologies, sensors, innovative demand response programs, and updated versions of certification programs elevate the opportunity to mitigate energy-related problems (blackouts and overproduction) and guides energy managers to optimize the consumption characteristics. With increasing advancements in technologies relying on the ‘Big Data,' codes and certification programs such as the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE), and the Leadership in Energy and Environmental Design (LEED) evaluates during the pre-construction phase. It is mostly carried out with the assumed quantitative and qualitative values calculated from energy models such as Energy Plus and E-quest. However, the energy consumption analysis through Knowledge Discovery in Databases (KDD) is not commonly used by energy managers to perform complete implementation, causing the need for better energy analytic framework. The dissertation utilizes Interval Data (ID) and establishes three different frameworks to identify electricity losses, predict electricity consumption and detect anomalies using data mining, deep learning, and mathematical models. The process of energy analytics integrates with the computational science and contributes to several objectives which are to 1. Develop a framework to identify both technical and non-technical losses using clustering and semi-supervised learning techniques. 2. Develop an integrated framework to predict electricity consumption using wavelet based data transformation model and deep learning algorithms. 3. Develop a framework to detect anomalies using ensemble empirical mode decomposition and isolation forest algorithms. With a thorough research background, the first phase details on performing data analytics on the demand-supply database to determine the potential energy loss reduction potentials. Data preprocessing and electricity prediction framework in the second phase integrates mathematical models and deep learning algorithms to accurately predict consumption. The third phase employs data decomposition model and data mining techniques to detect the anomalies of institutional buildings.Dissertation/ThesisDoctoral Dissertation Civil, Environmental and Sustainable Engineering 201

Application of Neural Network and Wavelet Transform Techniques in Structural Health Monitoring

Structural Health Monitoring (SHM) has recently emerged as a useful tool for tracking the performance parameters of a structure such as strain, deflection, and acceleration through a series of sensors installed on them. The signals produced from these sensors are the main performance indicator of the structure. In assessing the condition of the structure, the proper analysis and the evaluation of changes in pattern of signals are the most important tasks in SHM. Another important aspect of SHM is the detection of the defective sensors. And it is very difficult to identify it manually from a series of sensors. Although it is an important task in SHM but no straightforward method exists currently to carry out this task. In this study, the sensor data from a Canadian bridge have been utilized here to develop Artificial Neural Network (ANN) and Wavelet Transform (WT) based methods for tracking the changes in sensor data pattern and detecting the defective sensors in SHM. The ANN structures are constructed with input nodes accepting data from selected strain gauges and a target selected from the remaining strain gauges. The data collected at different time periods are de-noised by WT and tested against the trained network to find the pattern of differences between the input and output data series. The proposed methods have been validated with the available data and are found to be effective in tracking the data patterns and detecting defective sensors

A data analytics approach to gas turbine prognostics and health management

As a consequence of the recent deregulation in the electrical power production industry, there has been a shift in the traditional ownership of power plants and the way they are operated. To hedge their business risks, the many new private entrepreneurs enter into long-term service agreement (LTSA) with third parties for their operation and maintenance activities. As the major LTSA providers, original equipment manufacturers have invested huge amounts of money to develop preventive maintenance strategies to minimize the occurrence of costly unplanned outages resulting from failures of the equipments covered under LTSA contracts. As a matter of fact, a recent study by the Electric Power Research Institute estimates the cost benefit of preventing a failure of a General Electric 7FA or 9FA technology compressor at $10 to$20 million. Therefore, in this dissertation, a two-phase data analytics approach is proposed to use the existing monitoring gas path and vibration sensors data to first develop a proactive strategy that systematically detects and validates catastrophic failure precursors so as to avoid the failure; and secondly to estimate the residual time to failure of the unhealthy items. For the first part of this work, the time-frequency technique of the wavelet packet transforms is used to de-noise the noisy sensor data. Next, the time-series signal of each sensor is decomposed to perform a multi-resolution analysis to extract its features. After that, the probabilistic principal component analysis is applied as a data fusion technique to reduce the number of the potentially correlated multi-sensors measurement into a few uncorrelated principal components. The last step of the failure precursor detection methodology, the anomaly detection decision, is in itself a multi-stage process. The obtained principal components from the data fusion step are first combined into a one-dimensional reconstructed signal representing the overall health assessment of the monitored systems. Then, two damage indicators of the reconstructed signal are defined and monitored for defect using a statistical process control approach. Finally, the Bayesian evaluation method for hypothesis testing is applied to a computed threshold to test for deviations from the healthy band. To model the residual time to failure, the anomaly severity index and the anomaly duration index are defined as defects characteristics. Two modeling techniques are investigated for the prognostication of the survival time after an anomaly is detected: the deterministic regression approach, and parametric approximation of the non-parametric Kaplan-Meier plot estimator. It is established that the deterministic regression provides poor prediction estimation. The non parametric survival data analysis technique of the Kaplan-Meier estimator provides the empirical survivor function of the data set comprised of both non-censored and right censored data. Though powerful because no a-priori predefined lifetime distribution is made, the Kaplan-Meier result lacks the flexibility to be transplanted to other units of a given fleet. The parametric analysis of survival data is performed with two popular failure analysis distributions: the exponential distribution and the Weibull distribution. The conclusion from the parametric analysis of the Kaplan-Meier plot is that the larger the data set, the more accurate is the prognostication ability of the residual time to failure model.PhDCommittee Chair: Mavris, Dimitri; Committee Member: Jiang, Xiaomo; Committee Member: Kumar, Virendra; Committee Member: Saleh, Joseph; Committee Member: Vittal, Sameer; Committee Member: Volovoi, Vital

A wavelet-based system for event detection in online real-time sensor data

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2004.Page 78 blank.Includes bibliographical references (p. 74-77).Sensors are increasingly being used for continuous monitoring purposes, the process of which generates huge volumes of data that need to be mined for interesting events in real-time. The purpose of this research is to develop a method to identify these events, and to provide users with an architecture that will allow them to analyze events online and in real-time, to act upon them, and to archive them for future offline analysis. This thesis is divided into two major portions. The first discusses a general software architecture that performs the functions defined above. The architecture proposed assumes no prior knowledge of the data, and is capable of dealing with multi-source data feed from any type of sensor(s) on one end, and can handle multiple clients on the other. The second part of the thesis discusses a wavelet-based algorithm for detecting certain types of events in real-time in one-dimensional numeric time-series data. Wavelets were judged to be the most appropriate technique for analyzing random sensor signals for which no prior information is available. The wavelet-based method in addition allows users to delve into different levels of abstraction (based on varying time periods) while looking at the data, which cannot be done by any previous method for real-time event detection. This thesis also touches on the fundamental question of how one defines an event, which is more easily possible in a particular domain, for a specific purpose, but is much harder to do in a generic, domain-independent level.by Charuleka Varadharajan.S.M

A design methodology for the implementation of embedded vehicle navigation systems

RÉSUMÉ Au fil des années, en raison de l'augmentation de la densité routière et l'intensité de la circulation, un système de navigation automobile devient nécessaire. Ce système doit fournir non seulement l'emplacement du véhicule mais, surtout, augmentera le contrôle, la sécurité et la performance globale de l'automobile. La baisse du coût des récepteurs de Géo-Positionnement par Satellite (GPS) a vulgarisé leur utilisation dans la navigation automobile. Le système GPS fournit les données de positionnement ainsi que l'information qui concerne la vitesse aux conducteurs. De ce fait, la plupart des dispositifs de navigation des automobiles civiles sont actuellement basés sur la technologie GPS. Cependant, en cas de perte du signal GPS par blocage par feuillage, passages en béton, dense agglomération urbaine, grands immeubles, tunnels et dans le cas d'atténuation, ces dispositifs ne parviennent pas à fonctionner avec précision. Une solution alternative au GPS, qui peut être utilisée dans la navigation automobile, est le système de navigation inertielle (INS). LINS est un système autonome qui n'est pas affecté par des perturbations externes. Il comprend des capteurs inertiels comme trois gyroscopes et trois accéléromètres. Le coût des INS peut être faible mais leur performance se détériore à long terme car ils souffrent des erreurs accumulées. Cependant, il peut fournir des solutions précises sur de courts intervalles de temps. Un système intégré de GPS/INS à faible coût a donc le potentiel de fournir de meilleures informations de position pendant des intervalles courts et longs. L'objectif principal de cette recherche était de mettre en place une solution d'un système de navigation véhiculaire temps réel sur une plateforme embarquée à faible coût. Ceci avait pour but de pouvoir l'utiliser comme un cadre de conception, et comme référence pour d'autres applications embarquées similaires. Pour améliorer la solution de navigation même en cas d'arrêt de fonctionnement du GPS, les données du système GPS/INS ont été fusionnées par la technique de la boucle fermée du filtrage de Kalman décentralisé en utilisant 15 équations d'états d'erreurs d'ENS. En raison de l'utilisation d'accéléromètre à faible coût, ainsi que des capteurs gyroscopiques de données, une technique de prétraitement nommée algorithme de débruitage par ondelettes a été adoptée. L'algorithme a un maximum de 5 niveaux de décomposition, de reconstruction, ainsi que du seuillage non linéaire à chaque niveau. La conception est décrite par un logiciel qui comprend un microprocesseur embarqué. L'implémentation est effectuée à l'aide d'un cœur du processeur MicroBlaze qui gère le processus de contrôle et exécute l'algorithme. Afin de développer une implémentation efficace, des calculs en virgule flottante sont effectués en utilisant l'unité de virgule flottante (FPU) du cœur du processeur Microblaze. Le système est implémenté sur carte FPGA Spartan-3 de Xilinx. Elle contient 200 mille portes logiques cadencées par un oscillateur à 50 MHz, avec une mémoire externe asynchrone SRAM de 1 Mio. Le système comprend également un bus périphérique sur puce (OPB). À ce titre, la solution finale du système de navigation automobile devrait avoir des caractéristiques telles qu'une faible consommation de puissance, un poids léger, une capacité de traitement en temps réel ainsi qu'un petit espace occupé sur puce. D'un point de vue développement, l'utilisation du langage C et d'un cœur de processeur fonctionnant sur FPGA donne à l'utilisateur une plateforme flexible pour tout prototypage d'applications. Les simulations montrent qu'une implémentation purement logicielle de l'algorithme de la boucle fermée du filtrage de Kalman décentralisé sur une plateforme embarquée qui utilise les nombres virgule-flottante à simple précision, peut produire des résultats acceptables. Ceci est conforme aux résultats obtenus sur une plateforme d'un ordinateur de bureau qui utilise les nombres virgule-flottante à double précision. Dans un premier temps, le code du filtrage de Kalman est exécuté à partir d'une mémoire externe SRAM de 1 Mio, soutenue par une mémoire cache de données de 8Kio et une cache d'instructions de 4 Kio. Puis, le même code est lancé à partir du bloc RAM sur puce, à grande vitesse, de 64 Kio. Dans les deux configurations mémoire, les fréquences d'échantillonnage maximales pour lesquelles le code peut être exécuté sont de 80 Hz (période de 12,5 ms) et 119 Hz (période de 8,4 ms), respectivement, tandis que les capteurs fournissent les données à 75 Hz Les même deux configurations de mémoire sont employées dans l'exécution de l'algorithme de débruitage par ondelettes avec 5 niveaux de décomposition, de reconstruction et seuillage non linéaire à chaque niveau. Sur l'accéléromètre et le gyro, les données brutes sont fournies en temps réel en utilisant un mode de fenêtre de non-chevauchement, avec une longueur de fenêtre de 75 échantillons. Les latences d'exécution dans les deux cas sont 5,47 ms et 1,96 ms pour les deux configurations de mémoire précédemment citées, respectivement. En outre, l'analyse temporelle de !'après synthèse des deux configurations matérielles, reporte des apports de 26% et 66% respectivement. Puisque le système fonctionne à 50 MHz, il y a ainsi une marge de manœuvre disponible intéressante pour des perfectionnements algorithmiques. Ainsi, en utilisant la combinaison d'une plate-forme peu coûteuse, une approche flexible de développement et une solution en temps réel, l'exécution montrée dans ce mémoire démontre que la synthèse d'une solution finale de navigation véhiculaire fonctionnant en temps réel, complètement fonctionnelle, panne-résiliente, peu coûteuse est faisable. -------------------ABSTRACT Over the years, due to the increasing road density and intensive road traffic, the need for automobile navigation has increased not just for providing location awareness but also for enhancing vehicular control, safety and overall performance. The declining cost of Global Positioning System (GPS) receivers has rendered them attractive for automobile navigation applications. GPS provides position and velocity information to automobile users. As a result, most of the present civilian automobile navigation devices are based on GPS technology. However, in the event of GPS signal loss, blockage by foliage, concrete overpasses, dense urban developments viz. tall buildings or tunnels and attenuation, these devices fail to perform accurately. An alternative to GPS that can be used in automobile navigation is an Inertial Navigation System (INS). INS is a self-contained system that is not affected by external disturbances. It comprises inertial sensors such as three gyroscopes and three accelerometers. Although low-grade, low-cost INS performance deteriorates in the long run as they suffer from accumulated errors, they can provide adequate navigational solution for short periods of time. An integrated GPS/INS system therefore has the potential to provide better positional information over short and long intervals. The main objective of this research was to implement a real-time navigation system solution on a low cost embedded platform so that it can be used as a design framework and reference for similar embedded applications. An integrated GPS/INS system with closed loop decentralized Kalman filtering technique is designed using trajectory data from low-cost GPS, accelerometer and gyroscope sensors. A data pre-processing technique based on a wavelet de-noising algorithm is implemented. It uses up to five levels of de-composition and reconstruction with non-linear thresholding on each level. The design is described in software which consists of an embedded microprocessor namely MicroBlaze that manages the control process and executes the algorithm. In order to develop an efficient implementation, floating-point computations are carried out using the floating point unit (FPU) of MicroBlaze soft core processor. The system is implemented on a Xilinx Spartan-3 Field Programmable Gate Array (FPGA) containing 200 thousand gates clocked by an onboard oscillator operating at 50 MHz, with an external asynchronous SRAM memory of 1 MiB. The system also includes the IBM CoreConnect On-Chip Peripheral Bus (OPB). As such the final solution for vehicle navigation system is expected to have features like low power consumption, light weight, real-time processing capability and small chip area. From a development point of view, the combination of the standard C programming language and a soft processor running on an FPGA gives the user a powerful yet flexible platform for any application prototyping. Results show that a purely software implementation of the decentralized closed loop Kalman filter algorithm embedded platform that uses single precision floating point numbers can produce acceptable results relative to those obtained from a desktop PC platform that uses double precision floating point numbers. At first, the Kalman filter code is executed from a 1 MiB external SRAM supported by 8KiB of data cache and 4IUB of instruction cache. Then, the same code is run from high speed 64ICiB on-chip Block RAM. In the two memory configurations, the maximum sampling frequencies at which the code can be executed are 80 Hz (period of 12.5 ms) and 119 Hz (period of 8.4 ms) respectively, while accelerometer and gyroscope sensors provide data at 75 Hz. The same two memory configurations are employed in executing a wavelet de-noising algorithm with 5 levels of de-composition, reconstruction and non linear thresholding on each level. Accelerometer and gyroscope raw data are processed in real-time using non-overlapping windows of 75 samples. The execution latencies in the two cases are found to be 5.47 ms and 1.96 ms respectively. Additionally, from the post synthesis timing analyses, the critical frequencies for the two hardware configurations were 63.3 MHz and 83.2 MHz. an enhancement of 26% and 66% respectively. Since the system operates at 50 MHz, there is thus an interesting processing margin available for further algorithmic enhancements. Thus, by employing the combination of a low cost embedded platform, a flexible development approach and a real-time solution, the implementation shown in this thesis demonstrates that synthesizing a completely functional low-cost, outage-resilient, real-time navigation solution for automotive applications is feasible. -------------CONTENT Automobile Navigation -- Global Positioning System -- Navigation Frame -- Earth Models -- Attitude Representations -- Inertial Navigation System -- IMU Sensor Errors -- 2D INS Mechanization Equations -- 3D INS Mechanization Equations -- INS Error Equations -- GPS/INS data fusion using KF -- IMU data prepocessing using Wavelet De-noising -- Hardware/Equipment Setup -- Embedded Platform -- Hardware Platform Development -- Software Coding -- Software Design Issues -- Navigation Solution using MicroBlaze -- Timing Measurements

Advanced machine learning models for online travel-time prediction on freeways

The objective of the research described in this dissertation is to improve the travel-time prediction process using machine learning methods for the Advanced Traffic In-formation Systems (ATIS). Travel-time prediction has gained significance over the years especially in urban areas due to increasing traffic congestion. The increased demand of the traffic flow has motivated the need for development of improved applications and frameworks, which could alleviate the problems arising due to traffic flow, without the need of addition to the roadway infrastructure. In this thesis, the basic building blocks of the travel-time prediction models are discussed, with a review of the significant prior art. The problem of travel-time prediction was addressed by different perspectives in the past. Mainly the data-driven approach and the traffic flow modeling approach are the two main paths adopted viz. a viz. travel-time prediction from the methodology perspective. This dissertation, works towards the im-provement of the data-driven method. The data-driven model, presented in this dissertation, for the travel-time predic-tion on freeways was based on wavelet packet decomposition and support vector regres-sion (WPSVR), which uses the multi-resolution and equivalent frequency distribution ability of the wavelet transform to train the support vector machines. The results are compared against the classical support vector regression (SVR) method. Our results indi-cate that the wavelet reconstructed coefficients when used as an input to the support vec-tor machine for regression (WPSVR) give better performance (with selected wavelets on-ly), when compared against the support vector regression (without wavelet decomposi-tion). The data used in the model is downloaded from California Department of Trans-portation (Caltrans) of District 12 with a detector density of 2.73, experiencing daily peak hours except most weekends. The data was stored for a period of 214 days accumulated over 5 minute intervals over a distance of 9.13 miles. The results indicate an improvement in accuracy when compared against the classical SVR method. The basic criteria for selection of wavelet basis for preprocessing the inputs of support vector machines are also explored to filter the set of wavelet families for the WDSVR model. Finally, a configuration of travel-time prediction on freeways is present-ed with interchangeable prediction methods along with the details of the Matlab applica-tion used to implement the WPSVR algorithm. The initial results are computed over the set of 42 wavelets. To reduce the compu-tational cost involved in transforming the travel-time data into the set of wavelet packets using all possible mother wavelets available, a methodology of filtering the wavelets is devised, which measures the cross-correlation and redundancy properties of consecutive wavelet transformed values of same frequency band. An alternate configuration of travel-time prediction on freeways using the con-cepts of cloud computation is also presented, which has the ability to interchange the pre-diction modules with an alternate method using the same time-series data. Finally, a graphical user interface is described to connect the Matlab environment with the Caltrans data server for online travel-time prediction using both SVR and WPSVR modules and display the errors and plots of predicted values for both methods. The GUI also has the ability to compute forecast of custom travel-time data in the offline mode.Ph.D

A Novel Multilevel-SVD Method to Improve Multistep Ahead Forecasting in Traffic Accidents Domain

Here is proposed a novel method for decomposing a nonstationary time series in components of low and high frequency. The method is based on Multilevel Singular Value Decomposition (MSVD) of a Hankel matrix. The decomposition is used to improve the forecasting accuracy of Multiple Input Multiple Output (MIMO) linear and nonlinear models. Three time series coming from traffic accidents domain are used. They represent the number of persons with injuries in traffic accidents of Santiago, Chile. The data were continuously collected by the Chilean Police and were weekly sampled from 2000:1 to 2014:12. The performance of MSVD is compared with the decomposition in components of low and high frequency of a commonly accepted method based on Stationary Wavelet Transform (SWT). SWT in conjunction with the Autoregressive model (SWT + MIMO-AR) and SWT in conjunction with an Autoregressive Neural Network (SWT + MIMO-ANN) were evaluated. The empirical results have shown that the best accuracy was achieved by the forecasting model based on the proposed decomposition method MSVD, in comparison with the forecasting models based on SWT

Empirical mode decomposition with least square support vector machine model for river flow forecasting

Accurate information on future river flow is a fundamental key for water resources planning, and management. Traditionally, single models have been introduced to predict the future value of river flow. However, single models may not be suitable to capture the nonlinear and non-stationary nature of the data. In this study, a three-step-prediction method based on Empirical Mode Decomposition (EMD), Kernel Principal Component Analysis (KPCA) and Least Square Support Vector Machine (LSSVM) model, referred to as EMD-KPCA-LSSVM is introduced. EMD is used to decompose the river flow data into several Intrinsic Mode Functions (IMFs) and residue. Then, KPCA is used to reduce the dimensionality of the dataset, which are then input into LSSVM for forecasting purposes. This study also presents comparison between the proposed model of EMD-KPCA-LSSVM with EMD-PCA-LSSVM, EMD-LSSVM, Benchmark EMD-LSSVM model proposed by previous researchers and few other benchmark models such as Single LSSVM and Support Vector Machine (SVM) model, EMD-SVM, PCA-LSSVM, and PCA-SVM. These models are ranked based on five statistical measures namely Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Correlation Coefficient ( r ), Correlation of Efficiency (CE) and Mean Absolute Percentage Error (MAPE). Then, the best ranked model is measured using Mean of Forecasting Error (MFE) to determine its under and over-predicted forecast rate. The results show that EMD-KPCA-LSSVM ranked first based on five measures for Muda, Selangor and Tualang Rivers. This model also indicates a small percentage of under-predicted values compared to the observed river flow values of 1.36%, 0.66%, 4.8% and 2.32% for Muda, Bernam, Selangor and Tualang Rivers, respectively. The study concludes by recommending the application of an EMD-based combined model particularly with kernel-based dimension reduction approach for river flow forecasting due to better prediction results and stability than those achieved from single models