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1 |
Data Mining Strategies for Real-Time Control in New York City
استراتژی داده کاوی برای کنترل زمان واقعی در شهر نیویورک-2105 The Data Mining System (DMS) at New York City Department of Transportation (NYCDOT) mainly consists of four database
systems for traffic and pedestrian/bicycle volumes, crash data, and signal timing plans as well as the Midtown in Motion (MIM)
systems which are used as part of the NYCDOT Intelligent Transportation System (ITS) infrastructure. These database and
control systems are operated by different units at NYCDOT as an independent database or operation system. New York City
experiences heavy traffic volumes, pedestrians and cyclists in each Central Business District (CBD) area and along key arterial
systems. There are consistent and urgent needs in New York City for real-time control to improve mobility and safety for all
users of the street networks, and to provide a timely response and management of random incidents. Therefore, it is necessary to
develop an integrated DMS for effective real-time control and active transportation management (ATM) in New York City.
This paper will present new strategies for New York City suggesting the development of efficient and cost-effective DMS,
involving: 1) use of new technology applications such as tablets and smartphone with Global Positioning System (GPS) and
wireless communication features for data collection and reduction; 2) interface development among existing database and control
systems; and 3) integrated DMS deployment with macroscopic and mesoscopic simulation models in Manhattan. This study
paper also suggests a complete data mining process for real-time control with traditional static data, current real timing data from
loop detectors, microwave sensors, and video cameras, and new real-time data using the GPS data. GPS data, including using taxi
and bus GPS information, and smartphone applications can be obtained in all weather conditions and during anytime of the day.
GPS data and smartphone application in NYCDOT DMS is discussed herein as a new concept.
© 2014 The Authors. Published by Elsevier B.V.
Selection and peer-review under responsibility of Elhadi M. Shakshu
Keywords: Data Mining System (DMS), New York City, real-time control, active transportation management (ATM), GPS data |
مقاله انگلیسی |
2 |
Towards optimal control of air handling units using deep reinforcement learning and recurrent neural network
به سمت کنترل بهینه واحدهای مدیریت هوا با استفاده از یادگیری تقویتی عمیق و شبکه عصبی بازگشتی -2020 A new generation of smart stormwater systems promises to reduce the need for new construction by enhancing the performance of the existing infrastructure through real-time control. Smart stormwater systems dynamically adapt their response to individual storms by controlling distributed assets, such as valves, gates, and pumps. This paper introduces a real-time control approach based on Reinforcement Learning (RL), which has emerged as a state-of-the-art methodology for autonomous control in the artificial intelligence community. Using a Deep Neu- ral Network, a RL-based controller learns a control strategy by interacting with the system it controls - effectively trying various control strategies until converging on those that achieve a desired objective. This paper formulates and implements a RL algorithm for the real-time control of urban stormwater systems. This algorithm trains a RL agent to control valves in a distributed stormwater system across thousands of simulated storm scenarios, seeking to achieve water level and flow set-points in the system. The algorithm is first evaluated for the control of an individual stormwater basin, after which it is adapted to the control of multiple basins in a larger watershed (4 km 2 ). The results indicate that RL can very effectively control individual sites. Performance is highly sensitive to the reward formulation of the RL agent. Generally, more explicit guidance led to better control performance, and more rapid and stable convergence of the learning process. While the control of multiple distributed sites also shows promise in reducing flooding and peak flows, the complexity of controlling larger systems comes with a number of caveats. The RL controller’s performance is very sensitive to the formulation of the Deep Neural Network and requires a significant amount of computational resource to achieve a reasonable performance en- hancement. Overall, the controlled system significantly outperforms the uncontrolled system, especially across storms of high intensity and duration. A frank discussion is provided, which should allow the benefits and draw- backs of RL to be considered when implementing it for the real-time control of stormwater systems. An open source implementation of the full simulation environment and control algorithms is also provided. Keywords: Real-time control | Reinforcement learning | Smart stormwater systems |
مقاله انگلیسی |
3 |
A robust co-state predictive model for energy management of plug-in hybrid electric bus
یک مدل پیش بینی شده مشترک قدرتمند برای مدیریت انرژی اتوبوس برقی هیبریدی پلاگین-2020 This paper proposes a robust co-state predictive model for Pontryagin’s Minimum Principle (PMP)-based
energy management of plug-in hybrid electric bus (PHEB). The main innovation is that the robust costate
predictive model is only expressed by a simplified formula. Moreover, it is exclusively designed
by the Design For Six Sigma (DFSS) method in consideration of noises of driving cycles and stochastic
vehicle mass. Because the DFSS strives to minimize the weighted sum of mean and standard deviation of
fuel consumption, the proposed strategy can simultaneously improve the fuel economy of the PHEB and
its robustness. The DFSS results show that the coefficients of the robust co-state predictive model can be
found; the simulation results demonstrate that the proposed strategy has similar fuel economy to dynamic
programming (DP); the hardware-in-loop (HIL) results demonstrate that the proposed strategy
has good real-time control performance, and can averagely improve the fuel economy by 35.19%
compared to a rule-based control strategy. Keywords: Plug-in hybrid electric bus | Energy management | PMP | Co-state predictive model | Design for six sigma |
مقاله انگلیسی |
4 |
A deep reinforcement learning approach for real-time sensor-driven decision making and predictive analytics
یک رویکرد یادگیری تقویتی عمیق برای تصمیم گیری در زمان واقعی مبتنی بر حسگر و تجزیه و تحلیل پیش بینی-2020 The increased complexity of sensor-intensive systems with expensive subsystems and costly repairs and failures
calls for efficient real-time control and decision making policies. Deep reinforcement learning has demonstrated
great potential in addressing highly complex and challenging control and decision making problems. Despite its
potential to derive real-time policies using real-time data for dynamic systems, it has been rarely used for sensordriven
maintenance related problems. In this paper, we propose two novel decision making methods in which
reinforcement learning and particle filtering are utilized for (i) deriving real-time maintenance policies and (ii)
estimating remaining useful life for sensor-monitored degrading systems. The proposed framework introduces a
new direction with many potential opportunities for system monitoring. To demonstrate the effectiveness of the
proposed methods, numerical experiments are provided from a set of simulated data and a turbofan engine
dataset provided by NASA. Keywords: Particle filters | Deep reinforcement learning | Real-time control | Decision-making | Remaining useful life estimation |
مقاله انگلیسی |
5 |
Optimal energy management of a residential-based hybrid renewable energy system using rule-based real-time control and 2D dynamic programming optimization method
مدیریت بهینه انرژی یک سیستم انرژی تجدیدپذیر ترکیبی مبتنی بر مسکونی با استفاده از کنترل زمان واقعی مبتنی بر قانون و روش بهینه سازی برنامه نویسی پویا 2D-2020 This paper presents a magnetically coupled hybrid renewable energy system (RES) for residential applications.
The proposed system integrates the energies of a set of PV panels, a fuel cell stack, and a
battery using a multi-winding magnetic link to supply a residential load. It can operate in multiple gridconnected
and off-grid operation modes. An energy management unit including an off-line dynamic
programming-based optimization stage and a real-time rule-based controller is designed to optimally
control the power flow in the system according to the provided energy plan. The system is designed
according to the required standards of the grid-connected residential RES. Different sections of the
proposed system including steady-state operation, control techniques, energy management method and
hardware design are studied in brief. A prototype of the proposed system is developed and experimentally
tested for an energy management scenario considering both sunny and cloudy profiles of the
PV generation. The energy distribution and cost analysis approved the benefits of the proposed system
for residential consumers. Keywords: Energy management | Real-time | Renewable energy system | Residential |
مقاله انگلیسی |
6 |
Multi-period data driven control strategy for real-time management of energy storages in virtual power plants integrated with power grid
استراتژی کنترل داده های چند دوره برای مدیریت زمان واقعی ذخیره انرژی در نیروگاه های مجازی که با شبکه برق ادغام شده اند-2020 This paper investigates a novel real-time stochastic multi-period management strategy of a virtual power plant
(VPP) using a three-layer language protocol based on computer program compiler principle, which takes advantage
of the availability of the battery storage in a VPP to maximize the revenue of the VPP over the entire
trading horizon considering the predicted prices in each slice of that horizon. When the conventional scenario
tree method is used to solve the computational complexity of the multi-period stochastic optimization problem,
it may cause the problem to become intractable when the problem-scale increases. This paper proposes a deterministic
lookahead approach that makes use of a novel formal language that implements a special formal
grammar to manage the real-time control on the battery storages of the VPP. The control of charging/discharging
of the battery storages, which is driven by the real-time spot price and the rolling price prediction, is
formalized by using the proposed recursive grammar and the corresponding non-deterministic finite automaton
(NFA). For validation, the proposed approach is applied to a simple three-bus and an adapted IEEE 14-bus test
system. The simulation results show that the proposed method can obtain optimal revenue by managing each
battery in the VPP to operate as a local generator, a local load, an energy buyer, an energy seller, or by being in
an idle state when the battery is full or empty. Keywords: Time-staged optimization | Energy storage | Non-deterministic finite automaton | Formal grammar | Price-driven | Renewable energy sources | Dynamic economic dispatch |
مقاله انگلیسی |
7 |
یک مدل بهینه سازی کارآمد برای تعهد واحد و اعزام سیستم های چند انرژی و ریز شبکه-2020 Multi-energy systems and microgrids can play an important role in increasing the efficiency of distributed energy
systems and favoring an increasing penetration from renewable sources, by serving as control hubs for the
optimal management of Distributed Energy Resources. Predictive operation planning via Mixed Integer Linear
Programming is an effective way of tackling the optimal management of these systems. However, the uncertainty
of demand and renewable production forecasts can hinder the optimality of the scheduling solution and even
lead to outages. This paper proposes a new Affinely Adjustable Robust Formulation of the day-ahead scheduling
problem for a generic multi-energy system/microgrid subject to multiple uncertainty factors. Piece-wise linear
decision rules are considered in the robust formulation, and their potential use for real-time control is assessed.
Novel features include an ad hoc characterization of the polyhedral uncertainty space aimed at reducing solution
conservativeness, aggregation of uncertain factors and partial-past recourse which allows speeding up the
computational time. The advantages and limitations of the Affinely Adjustable Robust Formulation are thoroughly
discussed and quantified through artificial and real-world test cases. The comparison with a conventional
deterministic approach shows that, despite the limitations of the affine decision rules, the adjustable robust
formulation can ensure full system reliability while attaining at the same time better performance Keywords: Energy Management System | Robust Optimization | Combined Heat and Power | Multi Energy System | Uncertain Scheduling Optimization | Off-grid Microgrid |
مقاله انگلیسی |
8 |
Combined heat and power system intelligent economic dispatch: A deep reinforcement learning approach
توزیع اقتصادی سیستم گرمایشی و هوشمند: یک رویکرد یادگیری تقویتی عمیق-2020 This paper proposed a Deep Reinforcement learning (DRL) approach for Combined Heat and Power (CHP)
system economic dispatch which obtain adaptability for different operating scenarios and significantly decrease
the computational complexity without affecting accuracy. In the respect of problem description, a vast of
Combined Heat and Power (CHP) economic dispatch problems are modeled as a high-dimensional and nonsmooth
objective function with a large number of non-linear constraints for which powerful optimization algorithms
and considerable time are required to solve it. In order to reduce the solution time, most engineering
applications choose to linearize the optimization target and devices model. To avoid complicated linearization
process, this paper models CHP economic dispatch problems as Markov Decision Process (MDP) that making the
model highly encapsulated to preserve the input and output characteristics of various devices. Furthermore, we
improve an advanced deep reinforcement learning algorithm: distributed proximal policy optimization (DPPO),
to make it applicable to CHP economic dispatch problem. Based on this algorithm, the agent will be trained to
explore optimal dispatch strategies for different operation scenarios and respond to system emergencies efficiently.
In the utility phase, the trained agent will generate optimal control strategy in real time based on current
system state. Compared with existing optimization methods, advantages of DRL methods are mainly reflected in
the following three aspects: 1) Adaptability: under the premise of the same network topology, the trained agent
can handle the economic scheduling problem in various operating scenarios without recalculation. 2) High
encapsulation: The user only needs to input the operating state to get the control strategy, while the optimization
algorithm needs to re-write the constraints and other formulas for different situations. 3) Time scale flexibility: It
can be applied to both the day-ahead optimized scheduling and the real-time control. The proposed method is
applied to two test system with different characteristics. The results demonstrate that the DRL method could
handle with varieties of operating situations while get better optimization performance than most of other
algorithms. Keywords: Combined heat and power economic dispatch | Deep reinforcement learning | Proximal policy optimization |
مقاله انگلیسی |
9 |
Edge intelligence based Economic Dispatch for Virtual Power Plant in 5G Internet of Energy
هوش لبه مبتنی بر اجرای اقتصادی برای نیروگاه مجازی در اینترنت 5G انرژی-2020 Nowadays, with a large of complicated geography of Distributed Energy Sources (DES), how to integrate
distributed renewable energy source and reduce the operational costs by Virtual Power Plant (VPP) becomes
a mainstream problem in Internet of energy. The traditional method of energy integration and operational
cost optimization utilizes the cloud computing technology to centralized control the computational task, which
increases the burden of computing. According with the development of information communication technology,
such as Internet of Things and 5G, edge computing technology is an effective way to offload computational
task to the edge side of 5G networks. Moreover, with the increase of collected data, it becomes a key point to
effectively improve the computing power of edge nodes in edge computing. Currently, machine learning is an
effective way to process the big data. Based this situation, it leads the combination of machine learning and
edge computing. In this paper, the Edge Intelligence (EI) structure is proposed to solve the Economic Dispatch
Problem (EDP) in VPP of Internet of Energy. Compared with the traditional edge computing, the proposed EI
structure inherits its original features which reduce the burden of cloud computing, and also the proposed EI
structure improves the computational power of edge computing. Through the splitting model and deploying
the particle model in the terminal, it is facility to real-time control and take the less costs of VPP. Due to
the transmission between the splitting models with counterpart, it transmits the part information and gradient
information, which effectively reduces the consumption of communication. The proposed method has verified
the effectiveness and feasibility through the numerical experiments of real application data sets. Keywords: Virtual Power Plant | Machine leaning | Edge intelligence | Economic Dispatch |
مقاله انگلیسی |
10 |
Least costly energy management for extended-range electric vehicles: An economic optimization framework
مدیریت انرژی کم هزینه برای وسایل نقلیه برقی با برد گسترده: یک چارچوب بهینه سازی اقتصادی-2020 In this work, the energy management strategy problem for extended-range electric vehicles is tackled by considering all the factors affecting the system costs and performance, ranging from traditional fuel consumption to noise emissions up to battery aging and engine start-up costs. To solve the resulting economic optimization problem, a control-oriented model of the powertrain is first derived focusing on power generation, thermal dynamics and noise emissions. Then, the energy management strategy prob- lem is formally stated as a mixed-integer convex program involving all the costs of interest and solved with state-of-the-art optimization tools. The optimal strategy can be used as a benchmark for real-time controls, to understand whether to purchase a range extender is economically effective, or to assess the cost of operating the vehicle. An electric bus case-study is illustrated in detail to show the potential of the proposed approach.. Keywords: Convex programming | Optimal control | Energy management | Electric vehicle | Range extender | Noise emissions |
مقاله انگلیسی |