The main objective of this work was to determine the worth of installing an electrical battery in order to reduce peak power consumption. The importance of this question resides in the expensive terms of energy bills when using the maximum power level. If maximum power consumption decreases, it affects not only the revenues of maximum power level bills, but also results in important reductions at the source of the power. This way, the power of the transformer decreases, and other electrical elements can be removed from electrical installations. The authors studied the Spanish electrical system, and a particle swarm optimization (PSO) algorithm was used to model battery sizing in peak power smoothing applications for an electrical consumption point. This study proves that, despite not being entirely profitable at present due to current kWh prices, implanting a battery will definitely be an option to consider in the future when these prices come down. 

/pdf/battery-sizing-optimization-in-power-smoothing-applications-bgstw02v.pdf

Battery Sizing Optimization in Power Smoothing Applications

: The main objective of this work was to determine the worth of installing an electrical battery in order to reduce peak power consumption. The importance of this question resides in the expensive terms of energy bills when using the maximum power level. If maximum power consumption decreases, it affects not only the revenues of maximum power level bills, but also results in important reductions at the source of the power. This way, the power of the transformer decreases, and other electrical elements can be removed from electrical installations. The authors studied the Spanish electrical system, and a particle swarm optimization (PSO) algorithm was used to model battery sizing in peak power smoothing applications for an electrical consumption point. This study proves that, despite not being entirely proﬁtable at present due to current kWh prices, implanting a battery will deﬁnitely be an option to consider in the future when these prices come down.

Quasi-Newton (QN) methods have shown to be effective in training neural networks. However, the computation and the storage of the approximated Hessian in large-scale applications is still a problem. The Memory-less QN (MLQN) was introduced as a method that did not require the storage of the matrix. This paper describes the effectiveness of the momentum term for the accelerated MLQN method through computer simulations on function approximation and classification problems. 

On the study of the memory-less quasi-Newton method with momentum term for neural network training

This study focuses on the Nesterov's accelerated quasi-Newton (NAQ) method in the context of deep neural networks (DNN) and its applications. The thesis objective is to confirm the robustness and efficiency of Nesterov's acceleration to quasi-Netwon (QN) methods by developing practical algorithms for different fields of optimization problems.

On the Practical Robustness of the Nesterov's Accelerated Quasi-Newton Method

Accelerating the Iteratively Preconditioned Gradient-Descent Algorithm using Momentum

Momentum acceleration of quasi-Newton based optimization technique for neural network training

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Momentum acceleration of quasi-Newton based optimization technique for neural network training

On the study of the memory-less quasi-Newton method with momentum term for neural network training