Advice Provision for Energy Saving in an Automobile Climate‐Control System

Azaria, Amos; Rosenfeld, Ariel; Kraus, Sarit; Goldman, Claudia V.; Tsimhoni, Omer

doi:10.1609/aimag.v36i3.2603

Cited by 9 publications

(4 citation statements)

References 22 publications

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“…It is important to note that our study does not consider additional factors which may influence the decision to adopt and configure an air ventilation solution in a certain way (see [53,54] for more details). For example, thermal comfort may play an important role as some may be more or less willing than others to reduce their energy consumption at the expense of some thermal discomfort [55]. Since thermal comfort is extremely hard to obtain, and highly personal in nature, we leave its investigation for future work.…”

Section: Discussionmentioning

confidence: 99%

The Trade-Off between Airborne Pandemic Control and Energy Consumption Using Air Ventilation Solutions

Alexi

Rosenfeld

Lazebnik

2022

Sensors

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A single paragraph of about 200 words maximum. For research articles, abstracts should give a pertinent overview of the work. We strongly encourage authors to use the following style of structured abstracts, but without headings: (1) Background: place the question addressed in a broad context and highlight the purpose of the study; (2) Methods: describe briefly the main methods or treatments applied; (3) Results: summarize the article’s main findings; (4) Conclusions: indicate the main conclusions or interpretations. The abstract should be an objective representation of the article, it must not contain results which are not presented and substantiated in the main text and should not exaggerate the main conclusions. Airborne diseases cause high mortality and adverse socioeconomic consequences. Due to urbanization, more people spend more time indoors. According to recent research, air ventilation reduces long-range airborne transmission in indoor settings. However, air ventilation solutions often incur significant energy costs and ecological footprints. The trade-offs between energy consumption and pandemic control indoors have not yet been thoroughly analyzed. In this work, we use advanced sensors to monitor the energy consumption and pandemic control capabilities of an air-conditioning system, a pedestal fan, and an open window in hospital rooms, classrooms, and conference rooms. A simulation of an indoor airborne pandemic spread of Coronavirus (COVID-19) is used to analyze the Pareto front. For the three examined room types, the Pareto front consists of all three air ventilation solutions, with some ventilation configurations demonstrating significant inefficiencies. Specifically, air-conditioning is found to be efficient only at a very high energy cost and fans seem to pose a reasonable alternative. To conclude, a more informed ventilation policy can bring about a more desirable compromise between energy consumption and pandemic spread control.

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Section: Discussionmentioning

confidence: 99%

The Trade-Off between Airborne Pandemic Control and Energy Consumption Using Air Ventilation Solutions

Alexi

Rosenfeld

Lazebnik

2022

Sensors

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“…Advice may be provided by the agent to the human (e.g. [3][4][5]18]) or be provided by the human to assist the agent in its learning process. Indeed, advice-based techniques are used also in human-aided reinforcement learning.…”

Section: Related Workmentioning

confidence: 99%

Criticality-Based Varying Step-Number Algorithm for Reinforcement Learning

Spielberg¹,

Azaria²

2022

Preprint

Self Cite

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In the context of reinforcement learning we introduce the concept of criticality of a state, which indicates the extent to which the choice of action in that particular state influences the expected return. That is, a state in which the choice of action is more likely to influence the final outcome is considered as more critical than a state in which it is less likely to influence the final outcome.We formulate a criticality-based varying step number algorithm (CVS) -a flexible step number algorithm that utilizes the criticality function provided by a human, or learned directly from the environment. We test it in three different domains including the Atari Pong environment, Road-Tree environment, and Shooter environment. We demonstrate that CVS is able to outperform popular learning algorithms such as Deep Q-Learning and Monte Carlo.

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“…Recommender systems present advice to users in the form of suggestions [67,88] often based on collaborative filtering and a social context [44,84]. Recommender systems have been used to provide advice in a wide range of domains and use cases [46], including advice on energy saving [5], nutrition [91], finance [39], and remedial behavior for computer programmers [42]. Users' interactions with such systems are susceptible to similar confirmation biases as other instances of decision-making and advice selection [47], which has led researchers to investigate diverse recommendation and dissenting information strategies [19,68].…”

Section: Online Advice and Recommender Systemsmentioning

confidence: 99%

Good for the Many or Best for the Few? A Dilemma in the Design of Algorithmic Advice

Dove,

Balestra,

Mann

et al. 2020

Preprint

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Applications in a range of domains, including route planning and well-being, offer advice based on the social information available in prior users' aggregated activity. When designing these applications, is it better to offer: a) advice that if strictly adhered to is more likely to result in an individual successfully achieving their goal, even if fewer users will choose to adopt it? or b) advice that is likely to be adopted by a larger number of users, but which is sub-optimal with regard to any particular individual achieving their goal? We identify this dilemma, characterized as Goal-Directed vs. Adoption-Directed advice, and investigate the design questions it raises through an online experiment undertaken in four advice domains (financial investment, making healthier lifestyle choices, route planning, training for a 5k run), with three user types, and across two levels of uncertainty. We report findings that suggest a preference for advice favoring individual goal attainment over higher user adoption rates, albeit with significant variation across advice domains; and discuss their design implications.CCS Concepts: • Human-centered computing → Empirical studies in HCI.

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Advice Provision for Energy Saving in an Automobile Climate‐Control System

Cited by 9 publications

References 22 publications

The Trade-Off between Airborne Pandemic Control and Energy Consumption Using Air Ventilation Solutions

The Trade-Off between Airborne Pandemic Control and Energy Consumption Using Air Ventilation Solutions

Criticality-Based Varying Step-Number Algorithm for Reinforcement Learning

Good for the Many or Best for the Few? A Dilemma in the Design of Algorithmic Advice

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