Utility-Based Context-Aware Multi-Agent Recommendation System for Energy Efficiency in Residential Buildings

Riabchuk, Valentyna; Hagel, Leon; Germaine, Felix; Zharova, Alona

doi:10.48550/arxiv.2205.02704

Cited by 1 publication

(7 citation statements)

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“…However, the inclusion of the weather data allows for a substantial increase in performance toward 0.9. Complex models in particular benefit considerably from the inclusion of the data, outperforming the approach of Riabchuk et al (2022).…”

Section: Resultsmentioning

confidence: 99%

“…We address the limitations in Riabchuk et al (2022) by enhancing the performance of the Availability and the Usage Agents. In particular, we apply the K-nearest neighbors (KNN), extreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), and Random Forest to predict the availability and usage probabilities in the smart home environment.…”

Section: Explainable Multi-agent Recommendation Systemmentioning

confidence: 99%

“…The Load Agent is evaluated by investigating the mean squared error (MSE) of the predicted load profile to real usages. We refer to Riabchuk et al (2022) for details. We do not make any changes to the prediction approach of the Load Agent and, therefore, we do not report its performance evaluation.…”

Section: Experimental Designmentioning

confidence: 99%

“…First, we suggest an explainable multi-agent recommendation system for energy efficiency in private households. In particular, we extend a multi-agent approach of Riabchuk et al (2022) by designing and implementing an Explainability Agent that provides explainable recommendations for optimal appliance scheduling in a textual and visual manner. Second, we enhance the predictive capacity of other agents by including weather data and applying state-of-the-art models.…”

Section: Introductionmentioning

confidence: 99%

“…We also provide an overview of predictive and explainability performance. Riabchuk et al (2022) introduce a utility-based context-aware multi-agent recommendation system that provides load-shifting recommendations for household devices for the next 24 h. Their system includes six agents: Price Agent (prepares external hourly electricity prices), Preparation Agent (prepares data for the other agents), Availability Agent (predicts the hourly user availability for the next 24 h), Usage Agent (calculates the devices' usage probabilities for the prediction day), Load Agent (extracts the typical devices' loads), and Recommendation Agent (collects the inputs from the other agents and provides recommendations). The multi-agent architecture is flexible and can be easily integrated into existing smart home systems.…”

Section: Introductionmentioning

confidence: 99%

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An explainable multi-agent recommendation system for energy-efficient decision support in smart homes

Zharova,

Boer,

Knoblauch

et al. 2024

Environ. Data Science

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Transparent, understandable, and persuasive recommendations support the electricity consumers’ behavioral change to tackle the energy efficiency problem. This paper proposes an explainable multi-agent recommendation system for load shifting for household appliances. First, we extend a novel multi-agent approach by designing and implementing an Explainability Agent that provides explainable recommendations for optimal appliance scheduling in a textual and visual manner. Second, we enhance the predictive capacity of other agents by including weather data and applying state-of-the-art models (i.e., k-nearest-neighbors, extreme gradient boosting, adaptive boosting, Random Forest, logistic regression, and explainable boosting machines). Since we want to help the user understand a single recommendation, we focus on local explainability approaches. In particular, we apply post-model approaches local, interpretable, model-agnostic explanation and SHapley Additive exPlanations as model-agnostic tools that can explain the predictions of the chosen classifiers. We further provide an overview of the predictive and explainability performance. Our results show a substantial improvement in the performance of the multi-agent system while at the same time opening up the “black box” of recommendations.

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Section: Resultsmentioning

confidence: 99%

Section: Explainable Multi-agent Recommendation Systemmentioning

confidence: 99%

Section: Experimental Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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