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action dependent heuristic dynamic programming based residential energy scheduling with home energy inter-exchange

ENERGY CONVERSION AND MANAGEMENT 2015年 103卷 553-561页

作者： Xu, Yancai Liu, Derong Wei, Qinglai Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Residential energy scheduling is a hot topic nowadays in the background of energy saving and environmental protection worldwide. To achieve this objective, a new residential energy scheduling algorithm is developed for energy management, based on action dependent heuristic dynamic programming. The algorithm works under the circumstance of residential real-time pricing and two adjacent housing units with energy inter-exchange, which can reduce the overall cost and enhance renewable energy efficiency after long-term operation. It is designed to obtain the optimal control policy to manage the directions and amounts of electricity energy flux. The algorithm's architecture is mainly constructed based on neural networks, denoting the learned characteristics in the linkage of layers. To get close to real situations, many constraints such as maximum charging/discharging power of batteries are taken into account. The absent energy penalty cost is developed for the first time as a part of the performance index function. When the environment changes, the residential energy scheduling algorithm gains new features and keeps adapting in real-time operations. Simulation results show that the developed algorithm is beneficial to energy conversation. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： action dependent heuristic dynamic programming Residential energy management Energy scheduling Control sequence Control strategy

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action dependent heuristic dynamic programming for home energy resource scheduling

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2013年第1期48卷 148-160页

作者： Fuselli, Danilo De Angelis, Francesco Boaro, Matteo Squartini, Stefano Wei, Qinglai Liu, Derong Piazza, Francesco Univ Politecn Marche Dipartimento Ingn Informaz Ancona Italy Univ Illinois Dept Elect & Comp Engn Chicago IL USA Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing Peoples R China

Energy management in smart home environment is nowadays a crucial aspect on which technologies have been focusing on in order to save costs and minimize energy waste. This goal can be reached by means of an energy resource scheduling strategy provided by a suitable optimization technique. The proposed solution involves a class of Adaptive Critic Designs (ACDs) called action dependent heuristic dynamic programming (ADHDP) that uses two neural networks, namely the action and the Critic Network. This scheme is able to minimize a given Utility Function over a certain time horizon. In order to increase the performances of the ADHDP algorithm, suitable Particle Swarm Optimization (PSO) based procedures are used to pretrain the weights of the action and the Critic networks. The results provided by PSO techniques and by a non-optimal baseline approach are also used as elements of comparison. Computer simulations have been carried out in different residential scenarios. An historical data set for solar irradiation has been used to simulate the behavior of a photovoltaic array to obtain renewable energy and the main grid is used to supply the load and charge the battery when necessary. The results confirm that the ADHDP is able to reduce the overall energy cost with respect to the baseline solution and the PSO techniques. Moreover, the validity of this method has also been shown in a more realistic context where only forecasted values of solar irradiation and electricity price can be used. (c) 2012 Elsevier Ltd. All rights reserved.

关键词： Adaptive dynamic programming action dependent heuristic dynamic programming Particle swarm optimization Neural networks Smart grid Home energy management

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An action dependent heuristic dynamic programming Approach for Algal Bloom Prediction With Time-Varying Parameters

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IEEE ACCESS 2020年 8卷 26235-26246页

作者： Zhang, Huiyan Hu, Bo Wang, Xiaoyi Xu, Jiping Wang, Li Sun, Qian Zhao, Zhiyao Beijing Technol & Business Univ Sch Comp & Informat Engn Beijing 100048 Peoples R China Beijing Technol & Business Univ China Light Ind Key Lab Ind Internet & Big Data Beijing 100048 Peoples R China

Algal bloom is a nonlinear and time-varying process, which brings challenges for the accurate prediction. For the existing mechanism model of algae ignores the external key factors, we propose an algae growth model (AGM) optimized by action dependent heuristic dynamic programming (ADHDP). This model has the structure of information interaction with the outside, which can predict algal bloom with well adaptive ability. In this paper, chlorophyll-a concentration is used as the representative factor of algal bloom. We use ADHDP approach to map the external key factors to the time-varying parameters, so the AGM can be adjusted to realize the self-adaptive prediction with the changes in external environments. Compared with different prediction methods, the simulation result shows that the ADHDP-AGM prediction model can accurately predict the chlorophyll-a concentration under different data distributions. Moreover, the prediction process shows that the time-varying parameters in AGM conform to the evolution trend of chlorophyll-a concentration in fact, which further improves the interpretability of prediction model. It provides a new perspective for building a data-driven prediction model with clear physical significance, and makes the mechanism research and data science further fusion.

关键词： Algal bloom action dependent heuristic dynamic programming time-varying parameters identification prediction

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Renewable Energy Management Using action dependent heuristic dynamic programming

Renewable Energy Management Using Action Dependent Heuristic...

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IEEE International Smart Cities Conference (ISC2)

作者： Sterling, Gulnaz Tyler, Benjamin Nazarbayev Univ Dept Comp Sci Astana Kazakhstan

ISBN: (纸本)9781538659595

With increases in global energy demand and the rapid consumption of fossil fuels, the use of green energy and more efficient energy management approaches are receiving serious attention. Our focus is on improving energy resource scheduling in smart buildings and homes to minimize cost, while meeting energy demand. Here, we present an approach using action dependent heuristic dynamic programming (ADHDP) optimization for a smart home set-up using solar panels, wind turbines, and a storage battery. In this work, we trained and evaluated our ADHDP approach using different simulation scenarios with various amounts of available renewable energy. We then demonstrated via computer simulation that our approach is more effective in cost minimization compared to a standard rule-based method. A correlation between optimization improvement and available renewable energy was also confirmed by computer simulation in all scenarios.

关键词： action dependent heuristic dynamic programming Smart Homes Smart Buildings Energy Resource Scheduling Renewable Energy Neural dynamic programming

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Optimal Control for Industrial Sucrose Crystallization with action dependent heuristic dynamic programming

Optimal Control for Industrial Sucrose Crystallization with ...

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8th World Congress on Intelligent Control and Automation (WCICA)

作者： Lin, Xiaofeng Zhang, Heng Wei, Li Liu, Huixia Guangxi Univ Sch Elect Engn Nanning 530004 Guangxi Provinc Peoples R China Guangxi Univ Light Ind & Food Engn Inst Nanning Peoples R China

ISBN: (纸本)9781424467129

This paper applies a neural-network-based approximate dynamic programming (ADP) method, namely, the action dependent heuristic dynamic programming (ADHDP), to an industrial sucrose crystallization optimal control problem. The industrial sucrose crystallization is a nonlinear and slow time-varying process. It is quite difficult to establish a precise mechanism model of the crystallization because of complex internal mechanism and interacting variables. We developed a neural network model of the crystallization based on the data from the actual sugar boiling process of sugar factory. The ADHDP is a learning- and approximation-based approach which can solve the optimization control problem of nonlinear system. The paper covers the basic principle of this learning scheme and the design of neural network controller based on the approach. The result of simulation shows the controller based on action dependent heuristic dynamic programming approach can optimize industrial sucrose crystallization.

关键词： Sucrose crystallization Sugar boiling Neural networks Approximate dynamic programming action dependent heuristic dynamic programming

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Online discrete-time LQR controller design with integral action for bulk Bucket Wheel Reclaimer operational processes via action-dependent heuristic dynamic programming

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ISA TRANSactionS 2019年 90卷 294-310页

作者： de Moura, Jose Pinheiro Moraes Rego, Patricia Helena da Fonseca Neto, Joao Viana Univ Estadual Maranhao Sao Luis Brazil Univ Fed Maranhao Sao Luis Brazil

In this paper, a novel approach for online design of optimal control systems applied to the bulk resumption process by bucket wheel reclaimer (BWR) is presented. This approach is based on reinforcement learning paradigms, more specifically action dependent heuristic dynamic programming (ADHDP), that learn online in real-time the Discrete Linear Quadratic Regulator (DLQR) optimal control solution with integral action. Due to the geometric irregularities of the storage yard stacks and variation in physical and chemical characteristics of the stacked material, the flow control of solid bulks by bucket wheel reclaimer requires methods that are suitable with the high degree of imprecision of process variables and environment uncertainties. The resumption of bulk solids is carried out by dividing the stack into layers, each layer is approximately 4 m high, and the layers are divided into workbenches up to 12 m in length. To take up a workbench several translation steps are required (penetration in the stack), with the translation step varying from 0 to 1 m. In order to maintain the desired ore flow throughout the process, the BWR lance speed must be periodically adjusted. The main advantage of the proposed control method is that besides the decision rule is fully independent of plant model, the gains of the resulting controller are self-adjustable. The control system was designed in such a way that the ADHDP-based DLQR controller with integral action would act in real-time in the plant control, using only the input and output signals and states measured along the system trajectory. (C) 2019 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： Online design Optimal control systems action dependent heuristic dynamic programming Bucket Wheel Reclaimer Discrete Linear Quadratic Regulator Integral action

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Residential Energy Scheduling for Variable Weather Solar Energy Based on Adaptive dynamic programming

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IEEE/CAA Journal of Automatica Sinica 2018年第1期5卷 36-46页

作者： Derong Liu Yancai Xu Qinglai Wei Xinliang Liu Guangdong University of Technology Guangzhou 510006 China The State Key Laboratory of Manage-ment and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China University of Chinese Academy of Sciences Beijing 100049 China The Bureau of Informationization Development Cyberspace Administration of China Beijing 100010 China IEEE

The residential energy scheduling of solar energy is an important research area of smart grid. On the demand side, factors such as household loads, storage batteries, the outside public utility grid and renewable energy resources, are combined together as a nonlinear, time-varying, indefinite and complex system, which is difficult to manage or optimize. Many nations have already applied the residential real-time pricing to balance the burden on their grid. In order to enhance electricity efficiency of the residential micro grid, this paper presents an action dependent heuristic dynamic programming(ADHDP) method to solve the residential energy scheduling problem. The highlights of this paper are listed below. First,the weather-type classification is adopted to establish three types of programming models based on the features of the solar energy. In addition, the priorities of different energy resources are set to reduce the loss of electrical energy ***, three ADHDP-based neural networks, which can update themselves during applications, are designed to manage the flows of electricity. Third, simulation results show that the proposed scheduling method has effectively reduced the total electricity cost and improved load balancing process. The comparison with the particle swarm optimization algorithm further proves that the present method has a promising effect on energy management to save cost.

关键词： action dependent heuristic dynamic programming adaptive dynamic programming control strategy residential energy management smart grid

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Model-Free Optimal Control for Affine Nonlinear Systems With Convergence Analysis

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IEEE TRANSactionS ON AUTOMATION SCIENCE AND ENGINEERING 2015年第4期12卷 1461-1468页

作者： Zhao, Dongbin Xia, Zhongpu Wang, Ding Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

In this paper, a self-learning control scheme is proposed for the infinite horizon optimal control of affine nonlinear systems based on the action dependent heuristic dynamic programming algorithm. The policy iteration technique is introduced to derive the optimal control policy with feasibility and convergence analysis. It shows that the "greedy" control action for each state is uniquely existent, the learned control policy after each policy iteration is admissible, and the optimal control policy is able to be obtained. Two three-layer perceptron neural networks are employed to implement the scheme. The critic network is trained by a novel rule to conform to the Bellman equation, and the action network is trained to yield a better control policy. Both training processes alternate until the optimal control policy is achieved. Two simulation examples are provided to validate the effectiveness of the approach.

关键词： action dependent heuristic dynamic programming adaptive dynamic programming model-free optimal control neural networks policy iteration

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Adaptive Critic Design with ESN Critic for Bioprocess Optimization

Adaptive Critic Design with ESN Critic for Bioprocess Optimi...

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20th International Conference on Artificial Neural Networks

作者： Koprinkova-Hristova, Petia Palm, Guenther Bulgarian Acad Sci Inst Control & Syst Res Sofia Bulgaria Univ Ulm Inst Neural Informat Proc Ulm Germany

ISBN: (纸本)9783642158216

We propose an on-line action-dependent heuristic dynamic programming approach based on recurrent neural network architecture Echo state network (ESN) - as critic network within the frame of adaptive critic design (ACD), to be used for adaptive control. Here it is applied to the optimization of a complex nonlinear process for production of a biodegradable polymer, briefly called PHB. The on-line procedure for simultaneous critic training and process optimization is tested in the absence and presence of measurement noise. In both cases the optimization procedure succeeded in increasing the productivity and in proper training of the adaptive critic network at the same time.

关键词： Adaptive critic design action dependent heuristic dynamic programming Echo state network PHB production process

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Reinforcement learning control with n-step information for wastewater treatment systems

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年 133卷

作者： Li, Xin Wang, Ding Zhao, Mingming Qiao, Junfei Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Beijing Univ Technol Beijing Key Lab Computat Intelligence & Intellige Beijing 100124 Peoples R China Beijing Univ Technol Beijing Lab Smart Environm Protect Beijing 100124 Peoples R China Beijing Univ Technol Beijing Inst Artificial Intelligence Beijing 100124 Peoples R China

Wastewater treatment is important for maintaining a balanced urban ecosystem. To ensure the success of wastewater treatment, the tracking error between the crucial variable concentrations and the set point needs to be minimized as much as possible. Since the multiple biochemical reactions are involved, the wastewater treatment system is a nonlinear system with unknown dynamics. For this class of systems, this paper develops an online action dependent heuristic dynamic programming (ADHDP) algorithm combining the temporal difference with lambda [TD(lambda)], which is called ADHDP(lambda). By introducing the TD(lambda), the future n-step information is considered and the learning efficiency of the ADHDP algorithm is improved. We not only give the implementation process of the ADHDP(lambda) algorithm based on neural networks, but also prove the stability of the algorithm under certain conditions. Finally, the effectiveness of the ADHDP(lambda) algorithm is verified through two nonlinear systems, including a wastewater treatment system and a torsional pendulum system. Simulation results show that the ADHDP(lambda) algorithm has higher learning efficiency compared to the general ADHDP algorithm.

关键词： action dependent heuristic dynamic programming Online control Reinforcement learning Temporal difference with lambda Wastewater treatment processes

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