86,804 research outputs found
Adaptive hybrid optimization strategy for calibration and parameter estimation of physical models
A new adaptive hybrid optimization strategy, entitled squads, is proposed for
complex inverse analysis of computationally intensive physical models. The new
strategy is designed to be computationally efficient and robust in
identification of the global optimum (e.g. maximum or minimum value of an
objective function). It integrates a global Adaptive Particle Swarm
Optimization (APSO) strategy with a local Levenberg-Marquardt (LM) optimization
strategy using adaptive rules based on runtime performance. The global strategy
optimizes the location of a set of solutions (particles) in the parameter
space. The LM strategy is applied only to a subset of the particles at
different stages of the optimization based on the adaptive rules. After the LM
adjustment of the subset of particle positions, the updated particles are
returned to the APSO strategy. The advantages of coupling APSO and LM in the
manner implemented in squads is demonstrated by comparisons of squads
performance against Levenberg-Marquardt (LM), Particle Swarm Optimization
(PSO), Adaptive Particle Swarm Optimization (APSO; the TRIBES strategy), and an
existing hybrid optimization strategy (hPSO). All the strategies are tested on
2D, 5D and 10D Rosenbrock and Griewank polynomial test functions and a
synthetic hydrogeologic application to identify the source of a contaminant
plume in an aquifer. Tests are performed using a series of runs with random
initial guesses for the estimated (function/model) parameters. Squads is
observed to have the best performance when both robustness and efficiency are
taken into consideration than the other strategies for all test functions and
the hydrogeologic application
Demand and Storage Management in a Prosumer Nanogrid Based on Energy Forecasting
Energy efficiency and consumers' role in the energy system are among the strategic research topics in power systems these days. Smart grids (SG) and, specifically, microgrids, are key tools for these purposes. This paper presents a three-stage strategy for energy management in a prosumer nanogrid. Firstly, energy monitoring is performed and time-space compression is applied as a tool for forecasting energy resources and power quality (PQ) indices; secondly, demand is managed, taking advantage of smart appliances (SA) to reduce the electricity bill; finally, energy storage systems (ESS) are also managed to better match the forecasted generation of each prosumer. Results show how these strategies can be coordinated to contribute to energy management in the prosumer nanogrid. A simulation test is included, which proves how effectively the prosumers' power converters track the power setpoints obtained from the proposed strategy.Spanish Agencia Estatal de Investigacion ; Fondo Europeo de Desarrollo Regional
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