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Application of artificial intelligence technology in the economic development of urban intelligent transportation system

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PEERJ COMPUTER SCIENCE 2025年 11卷 e2728-e2728页

作者： Zhao, Ziming Chen, Jinyu Capital Univ Econ & Business Sch Urban Econ & Publ Adm Beijing Peoples R China Capital Univ Econ & Business Coll Business Adm Beijing Peoples R China

With the rapid development of the social economy and the gradual improvement of residents' living standards, the increasing number of urban cars has exacerbated urban traffic congestion. This article analyzed the application of artificial intelligence (AI) technology in five aspects of urban intelligent transportation systems. Artificial intelligence technology was used in traffic data collection and processing to provide accurate data support for traffic decision-making. A traffic flow prediction model was established for traffic flow prediction and optimized scheduling algorithms were used to dispatch vehicles on congested urban roads intelligently. Artificial intelligence algorithms can be used to optimize urban traffic signal control systems in intelligent traffic signal control;artificial intelligence technology can be applied to develop intelligent driving systems in the fields of intelligent driving and traffic safety;in terms of data analysis and decision support, it can use AI technology to analyze a large number of traffic data to provide decision support for urban traffic managers, and analyze the impact of the application of AI technology in urban intelligent transportation system on urban economic growth. This article evaluated the economic benefits of artificial intelligence technology in urban intelligent transportation systems. The evaluation results show that the total economic cost of the urban intelligent transportation system after the application of AI technology was 2,961 yuan less than before the application of AI technology, significantly reducing the investment cost of roads. This article analyzes the application of artificial intelligence technology in the economic development of intelligent urban transportation systems, which can meet the needs of healthy urban development and ensure road traffic safety.

关键词： Artificial intelligence technology Urban intelligent transportation system Economic development optimal scheduling algorithm Intelligent driving system Computing and informatics

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Multiobjective scheduling Strategy With Genetic algorithm and Time-Enhanced A* Planning for Autonomous Parking Robotics in High-Density Unmanned Parking Lots

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IEEE-ASME TRANSACTIONS ON MECHATRONICS 2021年第3期26卷 1547-1557页

作者： Chen, Guang Hou, Jing Dong, Jinhu Li, Zhijun Gu, Shangding Zhang, Bo Yu, Junwei Knoll, Alois Tongji Univ Dept Automot Engn Shanghai 200092 Peoples R China Univ Sci & Technol China Dept Automat Hefei 230026 Peoples R China Wuhan Univ Technol Wuhan 430070 Peoples R China Shanghai Westwell Informat & Technol Co Ltd Autonomous Driving Grp Shanghai 200050 Peoples R China Tech Univ Munich D-85748 Munich Germany

With the process of urbanization, the problem of insufficient parking spaces has become prominent. Adopting a high-density parking lot with parking robots can greatly improve the land utilization rate of the parking lot. This article tackles the multiple parking robots scheduling problem of high-density layout parking lots, including task execution sequence decision, robot allocation, and cooperative path planning. First, we mathematically describe the parking robot scheduling problem. Existing approximation algorithms are often far from the optimal solution. This article proposes an improved genetic algorithm and a time-enhanced A* path planning algorithm for high-density parking lots. The improved genetic algorithm can efficiently search task execution sequence and robot allocation and converge to the optimal solution even in large-scale complex scenarios. Meanwhile, the time-enhanced A* algorithm takes a new dimension "the time" into consideration, together with the distance, and security factors, to solve the multi-parking-robot path planning problem. Simulation experiments show that our algorithm can improve scheduling performance in many aspects such as task execution time, driving distance, and security in large-scale high-density parking lots. This article provides an efficient and convenient scheduling solution for the implementation of the high-density unmanned parking lot.

关键词： Task analysis Robots Path planning Automobiles scheduling Genetic algorithms scheduling algorithms Genetic algorithm high-density automatic parking multirobot systems optimal scheduling algorithm

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Value stacking of a behind-the-meter utility-scale battery for demand response markets and demand charge management: real-world operation on the UC San Diego campus

Value stacking of a behind-the-meter utility-scale battery f...

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IEEE-Power-and-Energy-Society General Meeting (PESGM)

作者： Chen, Yi-An Greenough, Ryan Ferry, Mike Johnson, Kelsey Kleissl, Jan Univ Calif San Diego Ctr Energy Res La Jolla CA 92093 USA Nuvve Corp 2468 Hist Decatur Rd 200 San Diego CA 92106 USA

ISBN: (纸本)9781665405072

More and more microgrids implement renewable energy resources and integrate energy storage systems which brings opportunities for energy market participation. While many studies analyze energy market bidding strategies and optimal energy resource management, the complications of real-world operation are often not considered. This study offers an economic model including monthly demand charge management and daily demand response auction mechanism (DRAM) market participation for a real-world operation of risk-averse energy storage scheduling on the UC San Diego campus. Market revenue analysis indicates net DRAM revenue in July and August 2020 of $96,025. A case study provides insight into the market operation and validates the feasibility of the economic model and control algorithm.

关键词： Bidding Strategy Demand Charge Management Demand Response Market optimal scheduling algorithm Real-world Operation

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Energy management platform for integrated battery-based energy storage - solar PV system: a case studyInspec keywordsOther keywords

IET ENERGY SYSTEMS INTEGRATION

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IET ENERGY SYSTEMS INTEGRATION 2020年第4期2卷 373-381页

作者： Suthar, Sachinkumar Pindoriya, Naran M. Indian Inst Technol Gandhinagar Elect Engn Palaj Gujarat India

This study develops an energy management platform for battery-based energy storage (BES) and solar photovoltaic (PV) generation connected at the low-voltage distribution network. The sewage treatment plant of Gujarat International Finance Tec-City located in the Gujarat state is considered as a testbed. The contribution of this case study is twofold: (i) Formulation and lab-scale implementation of an optimal scheduling algorithm to charge/discharge BES while minimising the net energy purchase cost. (ii) Development of a fuzzy logic-based speed control strategy for aeration blower to enhance the process efficiency. A stochastic optimisation problem was formulated as mixed-integer linear programming to achieve the charging and discharging schedules of BES. The simulation study on speed control of aeration blower was carried out to demonstrate the air injection control for optimal dissolved oxygen level in the aeration basin at the testbed. Simulation results of the case study and lab-scale hardware realisation (through real-time digital simulator interfaced with modular hardware) were presented to validate the demand- and supply-side energy management platform.

关键词： photovoltaic power systems energy management systems distribution networks fuzzy logic energy storage optimisation fuzzy control battery storage plants velocity control energy management platform integrated battery-based energy storage solar PV system low-voltage distribution network Gujarat International Finance Tec-City lab-scale implementation net energy purchase cost fuzzy logic-based speed control strategy aeration blower solar photovoltaic generation Gujarat state optimal scheduling algorithm charge-discharge BES stochastic optimisation problem mixed-integer linear programming speed control air injection control optimal dissolved oxygen level aeration basin real-time digital simulator modular hardware demand-side energy management platform supply-side energy management platform

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An Approximate optimal Solution to GPU Workload scheduling

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COMPUTING IN SCIENCE & ENGINEERING 2018年第5期20卷 63-76页

作者： Wang, Yanhua Qiao, Jianzhong Lin, Shukuan Zhao, Tinglei Northeastern Univ Shenyang Liaoning Peoples R China

One of the hot topics in graphic processing unit (GPU) research is workload scheduling. For parallel workloads with a large scale, the scheduling strategy can affect seriously system performance. To address this, the authors carry out scheduling of data transfer before workload execution scheduling, and propose an optimal scheduling algorithm for GPU workload. By hiding data transfer in workload execution to the maximum extent, the algorithm can reduce wait time, thus achieving a small timespan. They attribute the problem of hiding data transfer in workload execution to the 0-1 knapsack problem, and propose the pseudo-polynomial time algorithm based on the Dyer-Zemel algorithm. The authors then deduce the fully polynomial-time algorithm scheme for PPTA. By testing on cloud platform equipped with Nvidia Geforce GTX 750, they show that their scheduling algorithm estimates the optimal schedule sequence effectively.

关键词： Computational Complexity Graphics Processing Units Knapsack Problems Optimisation scheduling GPU Workload scheduling Graphic Processing Unit optimal scheduling algorithm Hiding Data Transfer Pseudopolynomial Time algorithm Dyer Zemel algorithm Knapsack Problem Cloud Platform Fully Polynomial Time algorithm Scheme PPTA Data Transfer Graphics Processing Units optimal scheduling optimal Matching scheduling algorithms GPU Workload scheduling Data Transfer Timespan Knapsack Problems FPTAS

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optimal operation scheduling of electric water heaters under dynamic pricingVassilis

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SUSTAINABLE CITIES AND SOCIETY 2017年 31卷 109-121页

作者： Kapsalis, Vassilis Hadellis, Loukas Technol Educ Inst Western Greece Patras 26334 Greece

This paper presents an optimal operation scheduling algorithm and evaluates its performance under adayahead real- time pricing (DA- RTP) tariff as well as under a combination of a DA- RTP and a time varyingbound on power consumption (demand limit). The scheduling algorithm is based on Dijkstra's algorithm and can be applied to continuously controlled loads (e. g., electric water heaters -EWHs, heating, ventilation and air conditioning systems - HVACs), which belong to the category of thermostatically controlled devices (TCAs). In this paper, the algorithm deals with the operation scheduling of EWHs, which are devices with a lot of flexibility as they possess high nominal power ratings and can be used as thermalenergy buffers. User's preferences regarding the preferred water temperature, the to lerable temperature range and the acceptable deviation from the minimum energy cost are mapped to the relative weightsof energy and comfort cost of the objective function, which is strived to be minimized by the scheduling algorithm, in order to optimize energy cost as well as user's comfort. Simulation results that verify the performance of the scheduling algorithm are presented under various realistic scenarios which study the effect of the upper temperature set point and the rated power on both the energy cost and the perceived comfort level. Any deviations between the real- time and the predicted hot water demand due to forecasting errors trigger a real- time adjustment of the operation scheduling. Regarding the implementation issue, the algorithm may be used for the control of smart EWHs by optimally adjusting the temperatureset point at each time slot as well as for the direct ON/OFF control of the heating element of legacy EWHs. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： optimal scheduling algorithm Electric water heater Demand response Dynamic pricing Smart grida

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optimal scheduling of independently operated, locally controlled energy storage systems as dispatchable assets in a competitive electricity market

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IET GENERATION TRANSMISSION & DISTRIBUTION 2017年第6期11卷 1360-1369页

作者： Khani, Hadi Zadeh, Mohammad R. Dadash Varma, Rajiv K. Western Univ Dept Elect & Comp Engn 1151 Richmond St London ON N6A 5B9 Canada ETAP Dept Res & Dev 17 Goodyear Irvine CA 92618 USA

Independently operated energy storage can exploit arbitrage opportunities available due to inter-temporal variation of electricity prices. Storage can be utilised as a dispatchable or non-dispatchable asset. In this study, a new optimal scheduling algorithm is proposed to enable independently operated, locally controlled storage to accept dispatch instructions issued by independent system operators (ISOs). Storage in this case is referred to as dispatchable storage. In addition, a new index is proposed to measure the storage dispatchability. While the operation of locally controlled storage is optimally scheduled at the owner's end, using the proposed algorithm, storage is fully dispatchable at the ISO's end. Dispatchable storage units have great potential to enhance the flexibility of electric grids and are key elements envisioned to enable smart grid realisation. The proposed algorithm outperforms previous algorithms in which storage is either locally controlled at the owner's end and cannot optimally accept ISO's instructions;or storage is centrally controlled by the grid operator to achieve some technical objectives. The efficacy and feasibility of the proposed algorithm are validated using real-world data. It is demonstrated that the proposed algorithm can enable the storage to accept all ISO's instructions. Revenue values of dispatchable and non-dispatchable storage are computed and compared.

关键词： power markets energy storage power generation scheduling power generation economics power generation dispatch pricing smart power grids independently operated locally controlled energy storage system dispatchable asset competitive electricity market electricity price intertemporal variation nondispatchable asset optimal scheduling algorithm independent system operator storage dispatchability dispatchable storage unit electric grid flexibility enhancement smart grid realisation

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Optimization of Tasks in Cloud Computing Based on MAX-MIN, MIN-MIN and Priority 4

Optimization of Tasks in Cloud Computing Based on MAX-MIN, M...

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4th International Conference on Web Research (ICWR)

作者： Derakhshan, Majid Bateni, Zohreh Univ Sci & Culture Comp Engn Tehran Iran

ISBN: (纸本)9781538653647

Considering the growing use of cloud computing and the need for optimal use of resources in the cloud, and attention to users that pay for services they use based on their pay-as-you-go basis, There should be a quicker way for users to decrease the user's waiting time and task's waiting time. The main purpose of this paper is to provide an optimal algorithm using the advantages of the two traditional Min-Min and Max-Min algorithms. The other point that follow in this algorithm (TOMMP) is the priority of the tasks. There are a lot of scheduling algorithms in the world today, but the priority given to the tasks has been neglected and overlooked in most algorithms. In this algorithm, priority is firstly selected for tasks based on a prioritization algorithm, and then using the median number to decide which one of the Min-Min or Max-Min algorithms is to be used. It should be noted that according to the TOMMP algorithms, its waiting time is lower than comparisons of the compared algorithms and is shown to be better than the comparable algorithms.

关键词： TOMMP algorithm optimal scheduling algorithm scheduling algorithm cloud scheduling algorithm Min-Min algorithm Max-Min algorithm cloud computing

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On optimal scheduling algorithms for Well-Structured Workflows in the Cloud with Budget and Deadline Constraints

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PARALLEL PROCESSING LETTERS 2016年第2期26卷 1650009-1650009页

作者： Wang, Yang Shi, Wei Kent, Kenneth B. Chinese Acad Sci Shenzhen Inst Adv Technol Ctr Cloud Comp Beijing 100864 Peoples R China Univ Western Ontario Inst Technol Fac Business & IT Oshawa ON Canada Univ New Brunswick Fac Comp Sci Fredericton NB E3B 5A3 Canada

In this paper, we consider optimal scheduling algorithms for scientific workflows with two typical structures, fork&join and tree, on a set of provisioned (virtual) machines under budget and deadline constraints in cloud computing. First, given a total budget B, by leveraging a bi-step dynamic programming technique, we propose optimal algorithms in pseudo-polynomial time for both workflows with minimum scheduling length as a goal. Our algorithms are efficient if the total budget B is polynomially bounded by the number of jobs in respective workflows, which is usually the common case in practice. Second, we consider the dual of this optimization problem to minimize the cost when the deadline of the computation D is fixed. We change this problem into the standard multiple-choice knapsack problem via a parallel transformation.

关键词： Workflow scheduling budget and deadline constraints optimal scheduling algorithm dynamic programming cloud

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An optimal scheduling algorithm in Spatial TDMA Mobile Ad Hoc Network

An Optimal Scheduling Algorithm in Spatial TDMA Mobile Ad Ho...

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4th Microwave and Radar Week (MRW)/18th International Conference on Microwaves, Radar and Wireless Communications (MIKON)

作者： Guo Zhong-hai Chen Yong-guang Acad Equipment Command & Technol Beijing Beijing 101416 Peoples R China Ordnance Engn Coll Hebei 050003 Peoples R China

ISBN: (纸本)9781424459148

Spatial TDMA (STDMA) is a "conflict-free" MAC protocol so that concurrent communication among the connected nodes. Therefore it can enable high spectral utilization, if optimal scheduling algorithm is achieved. As directional antenna technology developed, point to point transmission to other node could generate higher data transmission rate at the same time, lower interference with other nodes than omnidirectional antenna. Therefore an optimal scheduling algorithm needs to design to allocate the right slot to the right node in order to achieve high efficiency spatial reuse. To achieve this aim, we propose a new optimal scheduling algorithm: Max Spatial Reuse scheduling algorithm (MARSA). By using of directional antenna, it allows 1-hop distance neighbor node communication with other node in same slot unless one node connect to two node. Thus this concurrent scheduling algorithm allows more node communication with other nodes in one slot. Our method based on communication graph can be achieved with low complexity.

关键词： Spatial TDMA optimal scheduling algorithm MANET MARSA

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