Distributed Multi-Building Coordination for Demand Response

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The application of kernel-based Machine Learning (ML) techniques to discrete choice modelling using large datasets often faces challenges due to memory requirements and the considerable number of parameters involved in these models. This complexity hampers ...

A de facto standard approach in solving computer vision tasks is to use a common high-resolution camera and choose its placement on an agent based on human intuition. On the other hand, extremely simple and well-designed visual sensors found throughout nat ...

Social Engagement is a novel business model whose goal is transforming final users of a service from passive components into active ones. In this framework, people are contacted by the decision-maker (generally a company) and they are asked to perform task ...

The ever-increasing impact of deploying renewable energy resources reducing power system inertia, requires distributed energy resource (DER) aggregators to secure high-performance, fast-responding primary frequency reserve (PFR) in ancillary service market ...

The formulation of inverse problems in the continuum eliminates discretization errors and allows for the exact incorporation of priors. In this paper, we formulate a continuous-domain inverse problem over a search space of continuous and piecewise-linear f ...

Several techniques have been proposed for automatically searching for FPGA switch-blocks which typically show some tangible advantage over the well-known academic architectures. However, the resulting switch-blocks usually exhibit high levels of irregulari ...

Smart home appliances can time-shift and curtail their power demand to assist demand side management or allow operation with limited power, as in an off-grid application. This letter proposes a scheduling process to start appliances with time-varying deter ...

The emergence of Big Data has enabled new research perspectives in the discrete choice community. While the techniques to estimate Machine Learning models on a massive amount of data are well established, these have not yet been fully explored for the esti ...

Isogeometric shape optimization has been now studied for over a decade. This contribution aims at compiling the key ingredients within this promising framework, with a particular attention to sensitivity analysis. Based on all the researches related to iso ...

Determining appropriate utility specifications for discrete choice models is time-consuming and prone to errors. With the availability of larger and larger datasets, as the number of possible specifications exponentially grows with the number of variables ...

In distributed optimization, parameter updates from the gradient computing node devices have to be aggregated in every iteration on the orchestrating server. When these updates are sent over an arbitrary commodity network, bandwidth and latency can be limi ...

This research presents a mathematical formulation for optimizing integration of complex industrial systems from the level of unit operations to processes, entire plants, and finally to considering industrial symbiosis opportunities between plants. The fram ...

Buses operating in mixed traffic experience problems with delay and travel time variability in peak periods. Numerous transit priority measures are found in the literature but most rely on separating buses from other vehicles. Complete separation such as d ...

Large horsepower induction motors play a critical role in the operation of industrial facilities. In this respect, the distribution network operators dedicate a high priority to the operational safety of these motor loads. In this paper, the induction moto ...

This study evaluates the potential of supervised and transfer learning techniques to assist energy system optimization. A surrogate model is developed with the support of a supervised learning technique (by using artificial neural network) in order to bypa ...

An alternative approach for real-time network-wide traffic control in cities that has recently gained a lot of interest is perimeter flow control. The basic concept of such an approach is to partition heterogeneous cities into a small number of homogeneous ...

In this paper, we focus on the problem of minimizing a network of state facilities that provide essential public services (schools, offices, and hospitals). The goal is to reduce the size of the network in order to minimize the costs associated with it. Ho ...

Distributed Multi-Building Coordination for Demand Response

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On the Trade-off between Flatness and Optimization in Distributed Learning

Scalable kernel logistic regression with Nyström approximation: Theoretical analysis and application to discrete choice modelling

How Far Can a 1-Pixel Camera Go? Solving Vision Tasks Using Photoreceptors and Computationally Designed Visual Morphology

Workforce Allocation for Social Engagement Services via Stochastic Optimization

Stochastic Risk-driven Bidding of a Solar and Storage Aggregator in Primary Frequency and Energy Markets: A Performance-based Capacity Allocation Approach

A Box-Spline Framework for Inverse Problems With Continuous-Domain Sparsity Constraints

Regularity Matters: Designing Practical FPGA Switch-Blocks

Scheduling Delays and Curtailment for Household Appliances With Deterministic Load Profiles Using MPC

Estimation of discrete choice models with hybrid stochastic adaptive batch size algorithms

A New Lighting on Analytical Discrete Sensitivities in the Context of IsoGeometric Shape Optimization

Assisted specification of discrete choice models

LENA: Communication-Efficient Distributed Learning with Self-Triggered Gradient Uploads

A Mixed-Integer Linear Programming Formulation for Optimizing Multi-Scale Material and Energy Integration

Dynamic lane restrictions on congested arterials

A convex model for induction motor starting transients imbedded in an OPF-based optimization problem

Machine learning methods to assist energy system optimization

Linear-parameter-varying model predictive control for multi-region traffic systems

The Minimization of Public Facilities With Enhanced Genetic Algorithms Using War Elimination