Application of a game-theoretic energy management algorithm in a hybrid predictive-adaptive scenario

Dave, Saraansh; Sooriyabandara, Mahesh

doi:10.1109/isgteurope.2011.6162735

Cited by 9 publications

(12 citation statements)

References 9 publications

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“…Then GARP states that p 1 x 1 ≥ p 1 x 2 =⇒ p 2 x 2 ≤ p 2 x 1 . From (50), the underlying utility function must satisfy u(x 1 ) ≥ u(x 2 ) =⇒ u(x 2 ) ≤ u(x 1 ) where the equality results if x 1 = x 2 .…”

Section: Afriat's Theorem For a Single Agentmentioning

confidence: 99%

“…Though the classical Afriat's theorem holds for linear budget constraints p t x ≤ I t in (50), an identical formulation holds for certain non-linear budget constraints as illustrated in [61]. The budget constraints considered in [61] are of the form {x ∈ R m + |g(x) ≤ 0} where g : R m + → R is an increasing continuous function and R m + denotes the positive orthant.…”

Section: Summary and Extensionsmentioning

confidence: 99%

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Dynamics of Information Diffusion and Social Sensing

Krishnamurthy

Hoiles²

2018

Cooperative and Graph Signal Processing

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Statistical inference using social sensors is an area that has witnessed remarkable progress in the last decade. It is relevant in a variety of applications including localizing events for targeted advertising, marketing, localization of natural disasters and predicting sentiment of investors in financial markets. This chapter presents a tutorial description of four important aspects of sensing-based information diffusion in social networks from a communications/signal processing perspective. First, diffusion models for information exchange in large scale social networks together with social sensing via social media networks such as Twitter is considered. Second, Bayesian social learning models and risk averse social learning is considered with applications in finance and online reputation systems. Third, the principle of revealed preferences arising in micro-economics theory is used to parse datasets to determine if social sensors are utility maximizers and then determine their utility functions. Finally, the interaction of social sensors with YouTube channel owners is studied using time series analysis methods. All four topics are explained in the context of actual experimental datasets from health networks, social media and psychological experiments. Also, algorithms are given that exploit the above models to infer underlying events based on social sensing. The overview, insights, models and algorithms presented in this chapter stem from recent developments in network science, economics and signal processing. At a deeper level, this chapter considers mean field dynamics of networks, risk averse Bayesian social learning filtering and quickest change detection, data incest in decision making over a directed acyclic graph of social sensors, inverse optimization problems for utility function estimation (revealed preferences) and statistical modeling of interacting social sensors in YouTube social networks.

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Section: Afriat's Theorem For a Single Agentmentioning

confidence: 99%

Section: Summary and Extensionsmentioning

confidence: 99%

Dynamics of Information Diffusion and Social Sensing

Krishnamurthy

Hoiles²

2018

Cooperative and Graph Signal Processing

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“…Using the Multiagent Afriat's Theorem the agents preference for using power was estimated. This information can be used to improve the DSM strategies presented in [43,115] to control power consumption in the electricity market. 4.7.…”

Section: Learning Algorithm For Response Predictionmentioning

confidence: 99%

“…This analysis provides useful information for constructing demand side management (DSM) strategies for controlling power consumption in the electricity market. For example, if a utility function exists it can be used in the DSM strategy presented in [43,115].…”

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confidence: 99%

Information diffusion in social sensing

Krishnamurthy¹,

Hoiles²

2016

NACO

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Statistical inference using social sensors is an area that has witnessed remarkable progress in the last decade. It is relevant in a variety of applications including localizing events for targeted advertising, marketing, localization of natural disasters and predicting sentiment of investors in financial markets. This paper presents a tutorial description of three important aspects of sensing-based information diffusion in social networks from a communications/signal processing perspective. First, diffusion models for information exchange in large scale social networks together with social sensing via social media networks such as Twitter is considered. Second, Bayesian social learning models in online reputation systems are presented. Finally, the principle of revealed preferences arising in micro-economics theory is used to parse datasets to determine if social sensors are utility maximizers and then determine their utility functions. All three topics are explained in the context of actual experimental datasets from health networks, social media and psychological experiments. Also, algorithms are given that exploit the above models to infer underlying events based on social sensing. The overview, insights, models and algorithms presented in this paper stem from recent developments in computer-science, economics, psychology and electrical engineering.

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“…When there are multiple customers, if many of them utilize the energy in the same time when the guideline electricity price is low, the total bill and PAR could be significant. In order to tackle this issue, game theory [23] has been explored to develop multiple user scheduling algorithms such as those in [24], [25], [26], [27], [28], [29], [30]. In game theory, the customers interact with each other to share the energy consumption profiles.…”

Section: Community Level Smart Home Systemmentioning

confidence: 99%

Economical and Balanced Energy Usage in the Smart Home Infrastructure: A Tutorial and New Results

Liu

Wang

et al. 2015

IEEE Trans. Emerg. Topics Comput.

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The smart home infrastructure features the automatic control of various household appliances in the advanced metering infrastructure (AMI) which enables the connection of individual smart home systems to a smart grid. In such an infrastructure, each smart meter receives electricity price from utilities and uses a smart controller to schedule the household appliances accordingly. This helps shift the heavy energy load from peak hours to non-peak hours. Such an architecture significantly improves the reliability of the power grid through reducing the peak energy usage, while benefiting the customers through reducing electricity bills. This paper presents a tutorial on the development of the smart controller to schedule household appliances, which is also known as smart home scheduling. For each individual user, a dynamic programming based algorithm that schedules household appliances with discrete power levels is introduced. Based on it, a game theoretic framework is designed for multi-user smart scheduling to mitigate the accumulated energy usage during the peak hours. The simulations demonstrate that it can reduce the electricity bill by 30.11% while still improving peak to average ratio (PAR) in the power grid. Further, the deployment of smart home scheduling techniques in a big city is discussed. In such a context, the parallel computation is explored to tackle the large computational complexity and a machine assignment approximation algorithm is proposed to accelerate the smart home scheduling and a new hieratical framework is proposed to reduce the communication overhead. The simulation results on large testcases demonstrate that the city level hierarchical smart home scheduling can achieve the bill reduction of 43.04% and the PAR reduction of 47.50% on average.

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Application of a game-theoretic energy management algorithm in a hybrid predictive-adaptive scenario

Cited by 9 publications

References 9 publications

Dynamics of Information Diffusion and Social Sensing

Dynamics of Information Diffusion and Social Sensing

Information diffusion in social sensing

Economical and Balanced Energy Usage in the Smart Home Infrastructure: A Tutorial and New Results

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