The finiteness of fossil fuels implies that future electric power systems may predominantly
source energy from fuel-free renewable resources like wind and solar. Evidently, these …

Building energy consumption prediction plays an irreplaceable role in energy planning,
management, and conservation. Data-driven approaches, such as artificial neural networks …

We derive a second-order ordinary differential equation (ODE) which is the limit of
Nesterov's accelerated gradient method. This ODE exhibits approximate equivalence to …

In this paper, an aggregate game is adopted for the modeling and analysis of energy
consumption control in smart grid. Since the electricity users' cost functions depend on the …

This paper presents a new class of robust zero-order algorithms for the solution of real-time
optimization problems with acceleration. In particular, we propose a family of extremum …

In this brief, a distributed control algorithm is proposed to solve the economic dispatch
problem. Without a central control unit, the generators work collaboratively to minimize the …

This paper introduces a continuous-time constrained control strategy, which mimics the
behavior of a traditional model predictive control scheme using dynamic feedback. The …

We developed distributed continuous-time algorithms to solve the resource allocation
problem with quadratic cost functions and continuously time-varying resources. Since the …

Diffusion approximation provides weak approximation for stochastic gradient descent
algorithms in a finite time horizon. In this paper, we introduce new tools motivated by the …

In this paper, we consider convex optimization problems with constraints. By combining the
idea of a Lie bracket approximation for extremum seeking systems and saddle point …