Short-term forecasting for household electricity load with dynamic feature selection using power cepstrum

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceElectrical energy is present in our civilization and has positive and negative impacts in our environment, renewable and green energy like solar and wind energy works with significant less negative environmental impacts, reducing disasters and fuel dependency. Although, the transition to renewable and green energy demands advanced technologies to manage energy distribution in society, since the clean sources are stochastic. In this study the research will be done to improve electricity consumption forecasting in a household, a tool that can help the energy distribution management by microgrids to determine the amount of energy used by consumers at a particular moment, resulting in reducing energy waste and allowing P2P energy trading. The goal of this study is to do short-term forecasting and test the ability of Power Cepstrum to select autoregressive features, the dataset used is of minute-by-minute electricity consumption in kilowatts of a single household in the town of Sceaux,, France, between December 2006 and November 2010, the model tested was the Convolutional Long Short-Term Memory neural network with selected auto-regressive feature model (CLSAF), a Convolutional Long Short-Term Memory model working with Persistence model with dynamic feature selection, the ability of Power Cepstrum to select the autoregressive feature is tested and compared to CLSAF using Autocorrelation Function to select the autoregressive feature, the results are compared either to state of art models such as ConvLSTM and Persistence model. The tests were done comparing different theta threshold, input lags, resolutions, and input length. The result show that Power Cepstrum can be used as a replacement for Autocorrelation Function, CLSAF have comparable accuracy to ConvLSTM model and better runtime performance when using y[t-1] as input lag, for 30 minutes resolution is possible to observe great difference between runtime prediction without losing accuracy performance, Power Cepstrum showed better runtime prediction when compared to autocorrelation function, also, higher input length improved models performance

A Survey on Acoustic Side Channel Attacks on Keyboards

Most electronic devices utilize mechanical keyboards to receive inputs, including sensitive information such as authentication credentials, personal and private data, emails, plans, etc. However, these systems are susceptible to acoustic side-channel attacks. Researchers have successfully developed methods that can extract typed keystrokes from ambient noise. As the prevalence of keyboard-based input systems continues to expand across various computing platforms, and with the improvement of microphone technology, the potential vulnerability to acoustic side-channel attacks also increases. This survey paper thoroughly reviews existing research, explaining why such attacks are feasible, the applicable threat models, and the methodologies employed to launch and enhance these attacks.Comment: 22 pages, conferenc

Western Mediterranean wetlands bird species classification: evaluating small-footprint deep learning approaches on a new annotated dataset

The deployment of an expert system running over a wireless acoustic sensors network made up of bioacoustic monitoring devices that recognise bird species from their sounds would enable the automation of many tasks of ecological value, including the analysis of bird population composition or the detection of endangered species in areas of environmental interest. Endowing these devices with accurate audio classification capabilities is possible thanks to the latest advances in artificial intelligence, among which deep learning techniques excel. However, a key issue to make bioacoustic devices affordable is the use of small footprint deep neural networks that can be embedded in resource and battery constrained hardware platforms. For this reason, this work presents a critical comparative analysis between two heavy and large footprint deep neural networks (VGG16 and ResNet50) and a lightweight alternative, MobileNetV2. Our experimental results reveal that MobileNetV2 achieves an average F1-score less than a 5\% lower than ResNet50 (0.789 vs. 0.834), performing better than VGG16 with a footprint size nearly 40 times smaller. Moreover, to compare the models, we have created and made public the Western Mediterranean Wetland Birds dataset, consisting of 201.6 minutes and 5,795 audio excerpts of 20 endemic bird species of the Aiguamolls de l'Empord\`a Natural Park.Comment: 17 pages, 8 figures, 3 table

Smart Grid Sensor Monitoring Based on Deep Learning Technique with Control System Management in Fault Detection

The smart grid environment comprises of the sensor for monitoring the environment for effective power supply, utilization and establishment of communication. However, the management of smart grid in the monitoring environment isa difficult process due to diversifieduser request in the sensor monitoring with the grid-connected devices. Presently, context-awaremonitoring incorporates effective management of data management and provision of services in two-way processing and computing. In a heterogeneous environment context-aware, smart grid exhibits significant performance characteristics with the grid-connected communication environment for effective data processing for sustainability and stability. Fault diagnoses in the automated system are formulated to diagnose the fault separately. This paper developed anoptimized power grid control model (OPGCM) model for fault detection in the control system model for grid-connected smart home appliances. OPGCM model uses the context-aware power-awarescheme for load management in grid-connected smart homes. Through the adaptive smart grid model,power-aware management is incorporated with the evolutionary programming model for context-awareness user communication. The OPGCM modelperforms fault diagnosis in the grid-connected control system initially, Fault diagnosis system comprises of a sequential process with the extraction of the statistical features to acquirea sustainable dataset with effective signal processing. Secondly, the features are extracted based on the sequential process for the acquired dataset with a reduction of dimensionality. Finally, the classification is performed with the deep learning model to predict or identify the fault pattern. With the OPGCM model, features are optimized with the whale optimization model to acquire features to perform fault diagnosis and classification. Simulation analysis expressed that the proposed OPGCM model exhibits ~16% improved classification accuracy compared with the ANN and HMM model

Computation of the one-dimensional unwrapped phase

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.Includes bibliographical references (p. 101-102). "Cepstrum bibliography" (p. 67-100).In this thesis, the computation of the unwrapped phase of the discrete-time Fourier transform (DTFT) of a one-dimensional finite-length signal is explored. The phase of the DTFT is not unique, and may contain integer multiple of 27r discontinuities. The unwrapped phase is the instance of the phase function chosen to ensure continuity. This thesis presents existing algorithms for computing the unwrapped phase, discussing their weaknesses and strengths. Then two composite algorithms are proposed that use the existing ones, combining their strengths while avoiding their weaknesses. The core of the proposed methods is based on recent advances in polynomial factoring. The proposed methods are implemented and compared to the existing ones.by Zahi Nadim Karam.S.M

Non-intrusive load monitoring and classification of activities of daily living using residential smart meter data

This paper develops an approach for household appliance identification and classification of household Activities of Daily Living (ADLs) using residential smart meter data. The process of household appliance identification, i.e. decomposing a mains electricity measurement into each of its constituent individual appliances, is a very challenging classification problem. Recent advances have made deep learning a dominant approach for classification in fields such as image processing and speech recognition. This paper presents a deep learning approach based on multi-layer, feedforward neural networks that can identify common household electrical appliances from a typical household smart meter measurement. The performance of this approach is tested and validated using publicly-available smart meter data sets. The identified appliances are then mapped to household activities, or ADLs. The resulting ADL classifier can provide insights into the behaviour of the household occupants, which has a number of applications in the energy domain and in other fields

Probabilistic multiple kernel learning

The integration of multiple and possibly heterogeneous information sources for an overall decision-making process has been an open and unresolved research direction in computing science since its very beginning. This thesis attempts to address parts of that direction by proposing probabilistic data integration algorithms for multiclass decisions where an observation of interest is assigned to one of many categories based on a plurality of information channels

A Data-Driven Perspective on Residential Electricity Modeling and Structural Health Monitoring

In recent years, due to the increasing efficiency and availability of information technologies for collecting massive amounts of data (e.g., smart meters and sensors), a variety of advanced technologies and decision-making strategies in the civil engineering sector have shifted in leaps and bounds to a data-driven manner. While there is still no consensus in industry and academia on the latest advances, challenges, and trends in some innovative data-driven methods related to, e.g., deep learning and neural networks, it is undeniable that these techniques have been proven to be considerably effective in helping our academics and engineers solve many real-life tasks related to the smart city framework. This dissertation systematically presents the investigation and development of the cutting-edge data-driven methods related to two specific areas of civil engineering, namely, Residential Electricity Modeling (REM) and Structural Health Monitoring (SHM). For both components, the presentation of this dissertation starts with a brief review of classical data-driven methods used in particular problems, gradually progresses to an exploration of the related state-of-the-art technologies, and eventually lands on our proposed novel data-driven strategies and algorithms. In addition to the classical and state-of-the-art modeling techniques focused on these two areas, this dissertation also put great emphasis on the proposed effective feature extraction and selection approaches. These approaches are aimed to optimize model performance and to save computational resources, for achieving the ideal characterization of the information embedded in the collected raw data that is most relevant to the problem objectives, especially for the case of modeling deep neural networks. For the problems on REM, the proposed methods are validated with real recorded data from multi-family residential buildings, while for SHM, the algorithms are validated with data from numerically simulated systems as well as real bridge structures