A Multi-agent Home Automation System for Power Management

Abras, Shadi; Ploix, Stéphane; Pesty, Sylvie; Jacomino, Mireille

doi:10.1007/978-3-540-79142-3_6

Cited by 50 publications

(37 citation statements)

References 4 publications

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“…This layer relies on the most abstract models. The reactive layer has been detailed in (Abras et al, 2006). Its objective is to manage adjustments of energy assignment in order to follow up a plan computed by the upper anticipative layer in spite of unpredicted events and perturbations.…”

Section: Principle Of Control Mechanismmentioning

confidence: 99%

Home Energy Management Problem: towards an Optimal and Robust Solution

Ha¹,

Ploix²,

Jacomino³

et al. 2010

Energy Management

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Section: Principle Of Control Mechanismmentioning

confidence: 99%

Home Energy Management Problem: towards an Optimal and Robust Solution

Ha¹,

Ploix²,

Jacomino³

et al. 2010

Energy Management

Self Cite

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“…This mechanism, which rehes on the negotiation protocol [1], reacts quickly to avoid violations of energy constraints due to unpredicted perturbations and to guarantee a good level of inhabitant satisfaction. Therefore, the reactive mechanism adjusts, with a short sample time, the set points coming from the predicted plan, the device's current state (device satisfaction value) and the constraints and inhabitant criteria.…”

Section: Reactive Mechanismmentioning

confidence: 99%

“…The predicted set points can be directly transmitted to devices or adjusted by the reactive mechanism in case of constraint violation. Because the reactive mechanism has been presented in detail in [1], this paper focuses on the anticipative mechanism.…”

Section: Anticipative Mechanismmentioning

confidence: 99%

A Multi-Agent Design for a Home Automation System dedicated to power management

Abras¹,

Ploix²,

Pesty³

et al. 2007

IFIP the International Federation for Information Processing

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A b s t r a c t . This paper presents the principles of a Home Automation System dedicated to power management that adapts power consumption to available power ressources according to user comfort and cost criteria. The system relies on a multi-agent paradigm. Each agent, supporting a type of service achieved by one or several devices, cooperates and coordinates its action with others in order to find an acceptable near-optimal solution. The control algorithm is decomposed into two complementary mechanisms: a reactive mechanism, which protects from constraint violations, and an anticipation mechanism, which computes a predicted plan of global consumption according to predicted productions and consumptions and to user criteria. The paper presents a design for the Multi Agent Home Automation System.

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“…Multiagent systems (MAS) have been used as a tool to support sustainable building energy management (Abras et al, 2008;Cook, 2009;Kamboj et al, 2011;Rogers et al, 2011;Mamidi et al, 2012;Kwak et al, 2013). MAS is a subarea of artificial intelligence (AI) that particularly focuses on agent interactions and technologies that contribute to such interactions.…”

Section: Introductionmentioning

confidence: 99%

“…Instead, MAS considers all aspects together related to such agent interactions. Specifically for applications in buildings, researchers have employed MAS to address challenges of adaptively controlling a home heating system in order to minimize cost (Abras, Ploix, Pesty, & Jacomino, 2008;Rogers, Maleki, Ghosh, & Jennings, 2011) and to compute energy-efficient schedules for the optimal use of limited resources (Kamboj et al 2011;Stein et al 2012;Miller et al 2012;Kwak et al, 2013). This paper proposes to use a simulation model, which interacts with a MAS based framework for modeling energy behaviors in commercial buildings, to address another important challenge in commercial building management, namely the prediction of heating, ventilation and air conditioning (HVAC) related energy consumption.…”

Section: Introductionmentioning

confidence: 99%

Predicting HVAC Energy Consumption in Commercial Buildings Using Multiagent Systems

Kwak²,

Becerik-Gerber³

et al. 2013

Proceedings of the 30th International Symposium on Automation and Robotics in Construction and Mining (ISARC 2013): Building Th

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Energy consumption in commercial buildings has been increasing rapidly in the past decade. The knowledge of future energy consumption can bring significant value to commercial building energy management. For example, prediction of energy consumption decomposition helps analyze the energy consumption patterns and efficiencies as well as waste, and identify the prime targets for energy conservation. Moreover, prediction of temporal energy consumption enables building managers to plan out the energy usage over time, shift energy usage to off-peak periods, and make more effective energy purchase plans. This paper proposes a novel model for predicting heating, ventilation and air conditioning (HVAC) energy consumption in commercial buildings. The model simulates energy behaviors of HVAC systems in commercial buildings, and interacts with a multiagent systems (MAS) based framework for energy consumption prediction. Prediction is done on a daily, weekly and monthly basis. Ground truth energy consumption data is collected from a test bed office building over 267 consecutive days, and is compared to predicted energy consumption for the same period. Results show that the prediction can match 92.6 to 98.2% of total HVAC energy consumption with coefficient of variation of the root mean square error (CV-RMSE) values of 7.8 to 22.2%. Ventilation energy consumption can be predicted at high accuracies (over 99%) and low variations (CV-RMSE values of 3.1 to 16.3%), while cooling energy consumption accounts for majority of inaccuracies and variations in total energy consumption prediction.

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A Multi-agent Home Automation System for Power Management

Cited by 50 publications

References 4 publications

Home Energy Management Problem: towards an Optimal and Robust Solution

Home Energy Management Problem: towards an Optimal and Robust Solution

A Multi-Agent Design for a Home Automation System dedicated to power management

Predicting HVAC Energy Consumption in Commercial Buildings Using Multiagent Systems

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