The Influence of Differential Privacy on Short Term Electric Load Forecasting

There has been a large number of contributions on privacy-preserving smart metering with Differential Privacy, addressing questions from actual enforcement at the smart meter to billing at the energy provider. However, exploitation is mostly limited to application of cryptographic security means between smart meters and energy providers. We illustrate along the use case of privacy preserving load forecasting that Differential Privacy is indeed a valuable addition that unlocks novel information flows for optimization. We show that (i) there are large differences in utility along three selected forecasting methods, (ii) energy providers can enjoy good utility especially under the linear regression benchmark model, and (iii) households can participate in privacy preserving load forecasting with an individual re-identification risk < 60%, only 10% over random guessing.Comment: This is a pre-print of an article submitted to Springer Open Journal "Energy Informatics

Training Latency Minimization for Model-Splitting Allowed Federated Edge Learning

To alleviate the shortage of computing power faced by clients in training deep neural networks (DNNs) using federated learning (FL), we leverage the edge computing and split learning to propose a model-splitting allowed FL (SFL) framework, with the aim to minimize the training latency without loss of test accuracy. Under the synchronized global update setting, the latency to complete a round of global training is determined by the maximum latency for the clients to complete a local training session. Therefore, the training latency minimization problem (TLMP) is modelled as a minimizing-maximum problem. To solve this mixed integer nonlinear programming problem, we first propose a regression method to fit the quantitative-relationship between the cut-layer and other parameters of an AI-model, and thus, transform the TLMP into a continuous problem. Considering that the two subproblems involved in the TLMP, namely, the cut-layer selection problem for the clients and the computing resource allocation problem for the parameter-server are relative independence, an alternate-optimization-based algorithm with polynomial time complexity is developed to obtain a high-quality solution to the TLMP. Extensive experiments are performed on a popular DNN-model EfficientNetV2 using dataset MNIST, and the results verify the validity and improved performance of the proposed SFL framework

Design of Software for Design of Finite Element for Structural Analysis

The concepts of software engineering which allow a user of the finite element method to describe a model, to collect and to check the model data in a data base as well as to form the matrices required for a finite element calculation are examined. Next the components of the model description are conceived including the mesh tree, the topology, the configuration, the kinematic boundary conditions, the data for each element, and the loads. The possibilities for description and review of the data are considered. The concept of the segments for the modularization of the programs follows the components of the model description. The significance of the mesh tree as a globular guiding structure will be understood in view of the principle of the unity of the model, the mesh tree, and the data base. The user-friendly aspects of the software system will be summarized: the principle of language communication, the data generators, error processing, and data security

Investigación sobre la flexibilidad de la demanda en redes eléctricas inteligentes: control directo de cargas

In recent decades, the European Union has made decisive efforts to maintain its global leadership in renewable energies to meet climate change targets resulting from international agreements. There is a deliberate intention to reduce the usage of non-renewable energy sources and promote the exploitation of renewable generation at all levels as shown by energy production data within the Eurozone. The electricity sector illustrates a successful implementation of these energy policies: The electricity coming from combustible fuels was at historical lows in 2018, accounting for 83.6 % of the electricity generated from this source in 2008. By contrast, the pool of renewables reached almost 170 % of the 2008 production. Against this background, power systems worldwide are undergoing deep-seated changes due to the increasing penetration of these variable renewable energy sources and distributed energy resources that are intermittent and stochastic in nature. Under these conditions, achieving a continuous balance between generation and consumption becomes a challenge and may jeopardize the system stability, which points out the need of making the power system flexible enough as a response measure to this trend. This Ph.D. thesis researches one of the principal mechanisms providing flexibility to the power system: The demand-side management, seen from both the demand response and the energy efficiency perspectives. Power quality issues as a non-negligible part of energy efficiency are also addressed. To do so, several strategies have been deployed at a double level. In the residential sector, a direct load control strategy for smart appliances has been developed under a real-time pricing demand response scheme. This strategy seeks to minimize the daily cost of energy in presence of diverse energy resources and appliances. Furthermore, a spread spectrum technique has also been applied to mitigate the highfrequency distortion derived from the usage of LED technology lighting systems instead of traditional ones when energy efficiency needs to be improved. In the industrial sector, a load scheduling strategy to control the AC-AC power electronic converter in charge of supporting the electric-boosted glass melting furnaces has been developed. The benefit is two-fold: While it contributes to demand flexibility by shaving the peaks found under conventional control schemes, the power quality issues related to the emission of subharmonics are also kept to a minimum. Concerning the technologies, this Ph.D. thesis provides smart solutions, platforms, and devices to carry out these strategies: From the application of the internet of things paradigm to the development of the required electronics and the implementation of international standards within the energy industry.En las últimas décadas, la Unión Europea ha realizado esfuerzos decisivos para mantener su liderazgo mundial en energías renovables con el fin de cumplir los objetivos de cambio climático resultantes de los acuerdos internacionales. Muestra una intención deliberada de reducir el uso de fuentes de energía no renovable y promover la explotación de la generación renovable a todos los niveles, como demuestran los datos de producción de energía en la eurozona. El sector de la electricidad ilustra un caso de éxito de estas políticas energéticas: la electricidad procedente de combustibles fósiles estaba en mínimos históricos en 2018, representando el 83,6 % de la electricidad generada a partir de esta fuente en 2008; en cambio, el grupo de renovables alcanzó casi el 170 % de la producción de 2008. En este contexto, los sistemas eléctricos de todo el mundo están experimentando profundos cambios debido a la creciente penetración de estas fuentes de energía renovable y de recursos energéticos distribuidos que son de naturaleza variable, intermitente y estocástica. En estas condiciones, lograr un equilibrio continuo entre generación y consumo se convierte en un reto y puede poner en peligro la estabilidad del sistema, lo que señala la necesidad de flexibilizar el sistema eléctrico como medida de respuesta a esta tendencia. Esta tesis doctoral investiga uno de los principales mecanismos que proporcionan flexibilidad al sistema eléctrico: la gestión de la demanda vista tanto desde la perspectiva de la respuesta a la demanda como de la eficiencia energética. También se abordan los problemas de calidad de suministro entendidos como parte no despreciable de la eficiencia energética. Para ello, se han desplegado varias estrategias a un doble nivel. En el sector residencial, se ha desarrollado una estrategia basada en el control directo de cargas para los electrodomésticos inteligentes siguiendo un esquema de respuesta a la demanda con precios en tiempo real. Esta estrategia busca minimizar el coste diario de la energía en presencia de diversos recursos energéticos y electrodomésticos. Además, también se ha aplicado una técnica de espectro ensanchado para mitigar la distorsión de alta frecuencia derivada del uso de sistemas de iluminación con tecnología LED, empleados para la mejora de la eficiencia energética frente a las tecnologías convencionales. En el sector industrial, se ha desarrollado una estrategia de planificación de cargas para controlar el convertidor AC-AC de los hornos de fundición de vidrio con soporte eléctrico. El beneficio es doble: mientras que se contribuye a la flexibilidad de la demanda al eliminar los picos encontrados en los esquemas de control convencionales, también se reducen al mínimo los problemas de calidad de suministro relacionados con la emisión de subarmónicos. En cuanto a las tecnologías, esta tesis doctoral aporta soluciones, plataformas y dispositivos inteligentes para llevar a cabo estas estrategias: desde la aplicación del paradigma del internet de las cosas hasta el desarrollo de la electrónica necesaria y la implementación de estándares internacionales dentro de la industria energética

The ParaPhrase project : parallel patterns for adaptive heterogeneous multicore systems

Funding: This work has been supported by the European Union Framework 7 grant IST-2011-288570 “ParaPhrase: Parallel Patterns for Adaptive Heterogeneous Multicore Systems”This paper describes the ParaPhrase project, a new 3-year targeted research project funded under EU Framework 7 Objective 3.4 (Computer Systems) , starting in October 2011. ParaPhrase aims to follow a new approach to introducing parallelism using advanced refactoring techniques coupled with high-level parallel design patterns. The refactoring approach will use these design patterns to restructure programs defined as networks of software components into other forms that are more suited to parallel execution. The programmer will be aided by high-level cost information that will be integrated into the refactoring tools. The implementation of these patterns will then use a well-understood algorithmic skeleton approach to achieve good parallelism. A key ParaPhrase design goal is that parallel components are intended to match heterogeneous architectures, defined in terms of CPU/GPU combinations, for example. In order to achieve this, the ParaPhrase approach will map components at link time to the available hardware, and will then re-map them during program execution, taking account of multiple applications, changes in hardware resource availability, the desire to reduce communication costs etc. In this way, we aim to develop a new approach to programming that will be able to produce software that can adapt to dynamic changes in the system environment. Moreover, by using a strong component basis for parallelism, we can achieve potentially significant gains in terms of reducing sharing at a high level of abstraction, and so in reducing or even eliminating the costs that are usually associated with cache management, locking, and synchronisation.Postprin

Modeling randomized data streams in caching, data processing, and crawling applications

caching, search in large graphs) process streams of random key-value records that follow highly skewed frequency distributions. In this work, we first develop stochastic models for the probability to encounter unique keys during exploration of such streams and their growth rate over time. We then apply these models to the analysis of LRU caching, MapReduce overhead, and various crawl properties (e.g., node-degree bias, frontier size) in random graphs. I

Adaptive heterogeneous parallelism for semi-empirical lattice dynamics in computational materials science.

With the variability in performance of the multitude of parallel environments available today, the conceptual overhead created by the need to anticipate runtime information to make design-time decisions has become overwhelming. Performance-critical applications and libraries carry implicit assumptions based on incidental metrics that are not portable to emerging computational platforms or even alternative contemporary architectures. Furthermore, the significance of runtime concerns such as makespan, energy efficiency and fault tolerance depends on the situational context. This thesis presents a case study in the application of both Mattsons prescriptive pattern-oriented approach and the more principled structured parallelism formalism to the computational simulation of inelastic neutron scattering spectra on hybrid CPU/GPU platforms. The original ad hoc implementation as well as new patternbased and structured implementations are evaluated for relative performance and scalability. Two new structural abstractions are introduced to facilitate adaptation by lazy optimisation and runtime feedback. A deferred-choice abstraction represents a unified space of alternative structural program variants, allowing static adaptation through model-specific exhaustive calibration with regards to the extrafunctional concerns of runtime, average instantaneous power and total energy usage. Instrumented queues serve as mechanism for structural composition and provide a representation of extrafunctional state that allows realisation of a market-based decentralised coordination heuristic for competitive resource allocation and the Lyapunov drift algorithm for cooperative scheduling

York River Colloquy

Th e 1997 York River Colloquy is intended to provide an overview of some of the ongoing activities con ducted by VIMS scientists in the York River system. This collection of project summaries in not exhaustive, but it does provide a means to identify the interests of various investigators

Proactive-reactive, robust scheduling and capacity planning of deconstruction projects under uncertainty

A project planning and decision support model is developed and applied to identify and reduce risk and uncertainty in deconstruction project planning. It allows calculating building inventories based on sensor information and construction standards and it computes robust project plans for different scenarios with multiple modes, constrained renewable resources and locations. A reactive and flexible planning element is proposed in the case of schedule infeasibility during project execution