scholarly journals Agent-Based Modeling and Neural Network for Residential Customer Demand Response

2013 ◽  
Author(s):  
Fang Yuan Xu ◽  
Xue Wang ◽  
Loi Lei Lai ◽  
Chun Sing Lai
Keyword(s):  
Neural Network ◽  
Demand Response ◽  
Agent Based Modeling ◽  
Customer Demand ◽  
Agent Based

scholarly journals Agent-based Modeling in doing Logic Programming in Fuzzy Hopfield Neural Network

2021 ◽  
Vol 13 (2) ◽  
pp. 23-32
Author(s):  
Shehab Abdulhabib Saeed Alzaeemi ◽  
◽  
Saratha Sathasivam ◽  
Muraly Velavan
Keyword(s):  
Neural Network ◽  
Logic Programming ◽  
Hopfield Neural Network ◽  
Agent Based Modeling ◽  
Agent Based

Emergent Behavior in a Population of Thermostatically Controlled Loads With Peer-to-Peer Communication

2019 ◽  
Author(s):  
Ryan Schwartz ◽  
John F. Gardner
Keyword(s):  
Demand Response ◽  
Air Conditioning ◽  
Large Scale ◽  
Large Population ◽  
Agent Based Modeling ◽  
Emergent Behavior ◽  
Peer Communication ◽  
Agent Based ◽  
Thermostatically Controlled Loads ◽  
Residential Air Conditioning

Abstract Thermostatically controlled loads (TCLs) are often considered as a possible resource for demand response (DR) events. However, it is well understood that coordinated control of a large population of previously un-coordinated TCLs may result in load synchronization that results in higher peaks and large uncontrolled swings in aggregate load. In this paper we use agent based modeling to simulate a number of residential air conditioning loads and allow each to communicate a limited amount of information with their nearest neighbors. As a result, we document emergent behavior of this large scale, distributed and nonlinear system. Using the techniques described here, the population of TCLs experienced up to a 30% reduction in peak demand following the DR event. This behavior is shown to be beneficial to the goals of balancing the grid and integrating increasing penetration of variable generators.

scholarly journals Agent-Based Modeling of Retail Electrical Energy Markets With Demand Response

2018 ◽  
Vol 9 (4) ◽  
pp. 3465-3475 ◽  
Author(s):  
Kaveh Dehghanpour ◽  
M. Hashem Nehrir ◽  
John W. Sheppard ◽  
Nathan C. Kelly
Keyword(s):  
Demand Response ◽  
Electrical Energy ◽  
Energy Markets ◽  
Agent Based Modeling ◽  
Agent Based

Design Resilience of Demand Response Systems Utilizing Locally Communicating Thermostatically Controlled Loads

2019 ◽  
Author(s):  
Jason Kuwada ◽  
Hoda Mehrpouyan ◽  
John F. Gardner
Keyword(s):  
Demand Response ◽  
Stability Limit ◽  
Agent Based Modeling ◽  
Algebraic Connectivity ◽  
Agent Based ◽  
The Core ◽  
System A ◽  
Population Sizes ◽  
Response Systems ◽  
Thermostatically Controlled Loads

Abstract Thermostatically Controlled Loads (TCLs) have shown great potential for Demand Response (DR) events. The focus of this study is to investigate the effects of adding communication throughout a population of TCLs on the resilience of the system. A Metric for resilience is calculated on varying populations of TCLs and verified with agent based modeling simulations. At the core of this study is an added thermostat criterion created from the combination of a proportional gain and the average compressor operating state of neighboring TCLs. Differing connection architectures are also analyzed. Resilience of the systems under different connection topologies, are calculated by analyzing algebraic connectivity at varying population sizes. The resilience analysis was verified through simulation. Results of the analysis show the effect of on delay schemes and connection architecture on stability limit of each system. Good concurrence was found between predicted and observed resilience for smaller dead-band sizes. Simulations showed varying results on the effect of a simulated attack based on location of the attack within the population.

scholarly journals Agent-based Modeling for Activation Function in Enhancement Logic Programming in Hopfield Neural Network

2020 ◽  
Vol 9 (4) ◽  
pp. 1872-1879
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Logic Programming ◽  
Hamming Distance ◽  
Hopfield Neural Network ◽  
Activation Function ◽  
Agent Based Modeling ◽  
Activation Functions ◽  
Agent Based ◽  
Artificial Neural

Artificial Neural Network (ANN) uses many activation functions to update the state on neuron. The research and engineering have been used activation functions in the artificial neural network as the transfer functions. The most common reasons for using this transfer function were its unit interval boundaries, the functions and quick computability of its derivative, and several useful mathematical properties in the approximation of theory realm. Aim of this study is to figure out the best robust activation functions to accelerate HornSAT logic in the Hopfield Neural Network's context. In this paper we had developed Agent-based Modelling (ABM) assessed the performance of the Zeng Martinez Activation Function (ZMAF) and the Hyperbolic Tangent Activation Function (HTAF) beside the Wan Abdullah method to do Logic Programming (LP) in Hopfield Neural Network (HNN). These assessments are carried out on the basis of hamming distance (HD), the global minima ratio (zM), and CPU time. NETLOGO 5.3.1 software has been used for developing Agent-based Modeling (ABM) to test the proposed comparison of the efficaecy of these two activation functions HTAF and ZMAF.

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