Consumer load modeling and fair mechanisms in the efficient transactive energy market

Doctor of PhilosophyDepartment of Electrical and Computer EngineeringSanjoy DasTwo significant and closely related issues pertaining to the grid-constrained transactive distribution system market are investigated in this research. At first, the problem of spatial fairness in the allocation of energy among energy consumers is addressed, where consumer agents that are located at large distances from the substation – in terms of grid layout, are charged at higher rates than those close to it. This phenomenon, arising from the grid’s voltage and flow limits is aggravated during demand peaks. Using the Jain’s index to quantify fairness, two auction mechanisms are proposed. Both approaches are bilevel, with aggregators acting as interface agents between the consumers and the upstream distribution system operator (DSO). Furthermore, in spite of maximizing social welfare, neither mechanism makes use of the agents’ utility functions. The first mechanism is cost-setting, with the DSO determining unit costs. It implements the Jain’s index as a second term to the social welfare. Next, a power setting auction mechanism is put forth where the DSO’s role is to allocate energy in response to market equilibrium unit costs established at each aggregator from an iterative bidding process among its consumers. The Augmented Lagrangian Multigradient Approach (ALMA), which is based on vector gradient descent, is proposed in this research for implementation at the upper level. The mechanism’s lower level comprises of multiple auctions realized by the aggregators. The quasi-concavity of the Jain’s index is theoretically established, and it has been shown that ALMA converges to the Pareto front representing tradeoffs between social welfare and fairness. The effectiveness of both mechanisms is established through simulations carried out using a modified IEEE 37-bus system platform. The issue of extracting patterns of energy usage from time series energy use profiles of individual consumers is the focus of the second phase of this research. Two novel approaches for non-intrusive load disaggregation based on non-negative matrix factorization (NMF), are proposed. Both algorithms distinguish between fixed and shiftable load classes, with the latter being characterized by binary OFF and ON cycles. Fixed loads are represented as linear combinations of a set of basis vectors that are learned by NMF. One approach imposes L0 normed constraints on each shiftable load using a new method called binary load decomposition. The other approach models shiftable loads as Gaussian mixture models (GMM), therefore using expectation-maximization for unsupervised learning. This hybrid NMF-GMM algorithm enjoys the theoretical advantage of being interpretable as a maximum-likelihood procedure within a probabilistic framework. Numerical studies with real load profiles demonstrate that both algorithms can effectively disaggregate total loads into energy used by individual appliances. Using disaggregated loads, a maximum-margin regression approach to derive more elaborate, temperature-dependent utility functions of the consumers, is proposed. The research concludes by identifying the various ways gleaning such information can lead to more effective auction mechanisms for multi-period operation

Bayesian Matrix Factorization and Applications

Nonnegative matrix factorization (NMF) reduces the observed nonnegative matrix into a product of two nonnegative matrices. Nonnegativity entails two major implications: non-negative components and purely additive combination. These characteristics made this method useful in a wide range of applications. In this thesis, we propose two novel Bayesian nonnegative matrix factorization techniques. First, we propose a model dedicated to semi-bounded data where each entry of the observed matrix is supposed to follow an Inverted Beta distribution. Latent variables of the factorized parameter matrices follow a Gamma prior. Variational Bayesian inference and lower bound approximation for the objective function are used to find an analytically tractable solution for the model. An online extension of the algorithm is also proposed for more scalability. Both models are evaluated on five different applications. Second, we propose a Bayesian NMF that can be specifically useful for non intrusive load monitoring (NILM). NILM can be formulated as a source separation problem where the aggregated signal is expressed as linear combination of basis vectors in a matrix factorization framework. The model achieves superior performance by imposing sparsity on the activation matrix using Dirichlet priors. To estimate the parameters of the model, variational Bayesian inference is used. A novel optimization approach is proposed to find an analytically tractable solution for the model. We evaluate the model with three data sets: REDD, AMPds and IRISE, and with multiple experimental setups. The proposed model provides interpretability, flexibility and high performance

Energy Disaggregation using Two-Stage Fusion of Binary Device Detectors

A data-driven methodology to improve the energy disaggregation accuracy during Non-Intrusive Load Monitoring is proposed. In detail, the method is using a two-stage classification scheme, with the first stage consisting of classification models processing the aggregated signal in parallel and each of them producing a binary device detection score, and the second stage consisting of fusion regression models for estimating the power consumption for each of the electrical appliances. The accuracy of the proposed approach was tested on three datasets (ECO, REDD and iAWE), which are available online, using four different classifiers. The presented approach improves the estimation accuracy by up to 4.1% with respect to a basic energy disaggregation architecture, while the improvement on device level was up to 10.1%. Analysis on device level showed significant improvement of power consumption estimation accuracy especially for continuous and non-linear appliances across all evaluated datasets

Filtering in non-Intrusive load monitoring

Being able to track appliances energy usage without the need of sensors can help occupants reduce their energy consumption. Non-intrusive load monitoring (NILM) is one name for this topic. One of the hardest problems NILM faces is the ability to run unsupervised – discovering appliances without prior knowledge – and to run independent of the differences in appliance mixes and operational characteristics found in various countries and regions. This thesis showcases two filters that are used to denoise power signals, which results in better clustering accuracy for NILM event based methods. Both filters show to outperform a state-of-the-art denoising filter, in terms of run-time. A fully unsupervised NILM solution is presented, the algorithm is based on a hybrid knapsack problem with a Gaussian mixture model. Finally, a novel metric is developed to measure NILM disaggregation performance. The metric shows to be robust under a set of fundamental test cases

Modelling of Electrical Appliance Signatures for Energy Disaggregation

The rapid development of technology in the electrical sector within the last 20 years has led to growing electric power needs through the increased number of electrical appliances and automation of tasks. In contrary, reduction of the overall energy consumption as well as efficient energy management are needed, in order to reduce global warming and meet the global climate protection goals. These requirements have led to the recent adoption of smart-meters and smart-grids, as well as to the rise of Non-Intrusive Load Monitoring. Non-Intrusive Load Monitoring aims to extract the energy consumption of individual electrical appliances through disaggregation of the total power consumption as measured by a single smart meter at the inlet of a household. Therefore, Non-Intrusive Load Monitoring is a highly under-determined problem which aims to estimate multiple variables from a single observation, thus is impossible to be solved analytical. In order to find accurate estimates of the unknown variables three fundamentally different approaches, namely deep-learning, pattern matching and single-channel source separation, have been investigated in the literature in order to solve the Non-Intrusive Load Monitoring problem. While Non-Intrusive Load Monitoring has multiple areas of application, including energy reduction through consumer awareness, load scheduling for energy cost optimization or reduction of peak demands, the focus of this thesis is especially on the performance of the disaggregation algorithm, the key part of the Non-Intrusive Load Monitoring architecture. In detail, optimizations are proposed for all three architectures, while the focus lies on deep-learning based approaches. Furthermore, the transferability capability of the deep-learning based approach is investigated and a NILM specific transfer architecture is proposed. The main contribution of the thesis is threefold. First, with Non-Intrusive Load Monitoring being a time-series problem incorporation of temporal information is crucial for accurate modelling of the appliance signatures and the change of signatures over time. Therefore, previously published architectures based on deep-learning have focused on utilizing regression models which intrinsically incorporating temporal information. In this work, the idea of incorporating temporal information is extended especially through modelling temporal patterns of appliances not only in the regression stage, but also in the input feature vector, i.e. by using fractional calculus, feature concatenation or high-frequency double Fourier integral signatures. Additionally, multi variance matching is utilized for Non-Intrusive Load Monitoring in order to have additional degrees of freedom for a pattern matching based solution. Second, with Non-Intrusive Load Monitoring systems expected to operate in realtime as well as being low-cost applications, computational complexity as well as storage limitations must be considered. Therefore, in this thesis an approximation for frequency domain features is presented in order to account for a reduction in computational complexity. Furthermore, investigations of reduced sampling frequencies and their impact on disaggregation performance has been evaluated. Additionally, different elastic matching techniques have been compared in order to account for reduction of training times and utilization of models without trainable parameters. Third, in order to fully utilize Non-Intrusive Load Monitoring techniques accurate transfer models, i.e. models which are trained on one data domain and tested on a different data domain, are needed. In this context it is crucial to transfer time-variant and manufacturer dependent appliance signatures to manufacturer invariant signatures, in order to assure accurate transfer modelling. Therefore, a transfer learning architecture specifically adapted to the needs of Non-Intrusive Load Monitoring is presented. Overall, this thesis contributes to the topic of Non-Intrusive Load Monitoring improving the performance of the disaggregation stage while comparing three fundamentally different approaches for the disaggregation problem

Attention Mechanism for Recognition in Computer Vision

It has been proven that humans do not focus their attention on an entire scene at once when they perform a recognition task. Instead, they pay attention to the most important parts of the scene to extract the most discriminative information. Inspired by this observation, in this dissertation, the importance of attention mechanism in recognition tasks in computer vision is studied by designing novel attention-based models. In specific, four scenarios are investigated that represent the most important aspects of attention mechanism.First, an attention-based model is designed to reduce the visual features\u27 dimensionality by selectively processing only a small subset of the data. We study this aspect of the attention mechanism in a framework based on object recognition in distributed camera networks. Second, an attention-based image retrieval system (i.e., person re-identification) is proposed which learns to focus on the most discriminative regions of the person\u27s image and process those regions with higher computation power using a deep convolutional neural network. Furthermore, we show how visualizing the attention maps can make deep neural networks more interpretable. In other words, by visualizing the attention maps we can observe the regions of the input image where the neural network relies on, in order to make a decision. Third, a model for estimating the importance of the objects in a scene based on a given task is proposed. More specifically, the proposed model estimates the importance of the road users that a driver (or an autonomous vehicle) should pay attention to in a driving scenario in order to have safe navigation. In this scenario, the attention estimation is the final output of the model. Fourth, an attention-based module and a new loss function in a meta-learning based few-shot learning system is proposed in order to incorporate the context of the task into the feature representations of the samples and increasing the few-shot recognition accuracy.In this dissertation, we showed that attention can be multi-facet and studied the attention mechanism from the perspectives of feature selection, reducing the computational cost, interpretable deep learning models, task-driven importance estimation, and context incorporation. Through the study of four scenarios, we further advanced the field of where \u27\u27attention is all you need\u27\u27

Identificación y desagregación de consumo eléctrico por medio de inteligencia artificial.

La gestión eficiente del consumo de energía eléctrica ha cobrado más importancia en las últimas décadas por su impacto en el medio ambiente y la economía. Con el aumento de la adopción de fuentes de energía renovables y la creciente preocupación por el cambio climático, las empresas distribuidoras de energía eléctrica buscan constantemente nuevas metodologías para influir en la demanda de energía eléctrica sin afectar el comfort del usuario. Una técnica comúnmente utilizada para influir en los patrones de consumo es la generación de incentivos a través de tarifas bonificadas para aquellos usuarios que siguen patrones de consumo eficientes. Para aplicar esta técnica es esencial contar con mecanismos de monitoreo de consumo. El presente trabajo aborda el problema de monitoreo de consumo eléctrico mediante técnicas de aprendizaje supervisado con redes neuronales profundas, enfocándose en: i) la identificación del tipo de electrodoméstico a partir de una curva de consumo desagregada y ii) la desagregación del consumo de electrodomésticos a partir de una curva de consumo agregada. Ambos enfoques de monitoreo se realizan sobre datos a muy bajas frecuencias, desde una muestra por minuto hasta una muestra cada 15 minutos. Complementariamente, se pone foco en electrodomésticos poco estudiados en la literatura, como los termotanques que son ampliamente utilizados en Uruguay y los vehículos eléctricos, cuyo uso está en fase expansiva y presentan un consumo elevado. En Uruguay, durante los últimos años, la Administración Nacional de Usinas y Transmisiones Eléctricas del Estado (UTE) ha comenzado a utilizar medidores inteligentes capaces de recolectar datos de consumo eléctrico de los clientes en intervalos de 15 minutos, así como ha proporcionado medidores intrusivos a grupos de usuarios para monitorear el consumo de termotanques cada 1 minuto. En este trabajo se investigan técnicas de aprendizaje profundo, las cuales son entrenadas y evaluadas sobre una amplia cantidad de datos del sistema eléctrico uruguayo. Por otro lado, en el presente estudio se generan y preparan bases de datos, para ser compartidas de forma libre y en un formato estándar, facilitando así su acceso por parte de la comunidad científica. Finalmente, la implementación de los modelos dio lugar a transferencias tecnológicas entre la Facultad de Ingeniería (FING) y UTE, lo que posibilita que UTE utilice los algoritmos de aprendizaje automático desarrollados en aplicaciones específicas de su interés, evidenciando el impacto de este trabajo en el ámbito productivo.Efficient management of electrical energy consumption has gained increasingimportance in recent decades due to its impact on the environment and the eco-nomy. With the growing adoption of renewable energy sources and heightenedconcern for climate change, electric power distribution companies constantly seeknew methodologies to influence electricity demand without affecting user comfort.A common technique used to influence consumption patterns is the generationof incentives through discounted rates for users who follow efficient consumptionpatterns. Therefore, it is essential to have consumption monitoring mechanisms inplace to apply this technique.This study addresses the problem of electricity consumption monitoring usingsupervised learning techniques with deep neural networks, focusing on: i) the iden-tification of appliances from a disaggregated consumption curve and ii) the disag-gregation of appliances from an aggregated consumption curve. Both monitoringapproaches are performed on very low-frequency data, ranging from one sampleper minute to one sample every 15 minutes. Additionally, emphasis is placed onunder-studied appliances in the literature, such as water heaters widely used inUruguay and electric vehicles, whose usage is expanding and present high consum-ption levels.In Uruguay, over recent years, the Administraci ́on Nacional de Usinas y Trans-misiones El ́ectricas del Estado (UTE) has begun using intelligent meters capableof collecting customer electricity consumption data at 15-minute intervals and pro-viding intrusive meters to user groups for monitoring water heater consumptionevery 1 minute. This work investigates deep learning techniques, which are trainedand evaluated on a large amount of data from the Uruguayan electrical system.Furthermore, this study generates and prepares databases to be freely sharedin a standard format, thus facilitating their access by the scientific community.Finally, implementing the models led to technology transfers between the Fa-cultad de Ingenier ́ıa (FING) and UTE, enabling UTE to use the developed machinelearning algorithms in specific applications of their interest, thus demonstratingthe impact of this work on the productive sector.Beca CAPBeca CSI