居住区自供电机器人停车库的智能负载优化

Z. Slanina, Vojtech Blazek, J. Fulneček, Tomáš Vantuch
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引用次数: 0

摘要

本文讨论了自动停车场的综合研究结果,这是一种利用离网技术和先进负载优化的现代方法来实现自供电机器人停车场(S-PRPH)。S-PRPHs代表了在人口密集地区停车的大型技术解决方案。自动停车大楼包括作者团队开发的自动导引车(AGV)系统。AGV托盘是停车楼能耗的一部分。该方案解决了城市停车需求,包括能源自给自足、价格和最佳停车位数量之间的平衡,以及最大限度地利用建筑表面的绿色区域,提供氧气生产、过滤灰尘颗粒和适当的水管理。智能负载优化(ILO)是S-PRPH管理的一部分,基于非主导排序遗传算法II。本文描述了基于多目标优化的国际劳工组织的结果和三种变体的需求侧管理方法。本研究的结果是由于对中欧天气条件下离网系统建筑规模的负荷优化产生了根本性的积极影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Intelligent Load Optimisation for Self-Powered Robotic Parking House in Inhabited Areas
This article discusses the results of a comprehensive study of an automatic parking house a modern approach to utilizing off-grid technologies with advanced load optimization to a Self-Powered Robotic Parking House (S-PRPH). S-PRPHs represent large technical solutions for parking in densely populated areas. The automatic parking building includes Automated Guide Vehicle (AGV) systems developed by the team of authors. AGV pallets are part of the parking building energy consumption. This solution solves urban parking needs, including energetic self- sufficiency, balancing between price and an optimal number of parking spaces, and the maximum use of the building surface for green areas that provide oxygen production, filtration of dust particles, and appropriate water management. Intelligent Load Optimisation (ILO) is a part of S-PRPH management and is based on the Non-dominated sorting genetic algorithm II. The article describes the results of an ILO based on multi-objective optimization and methods of demand-side management in three variants. The results of this study are due to a fundamentally positive impact on load optimization in the off-grid system’s size of building with weather conditions of Central Europe.
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