Explanations engine for energy management systems in buildings

Alyafi, Amr Alzouhri; Guillbaud, Jean-Phillipe; Reignier, Patrick; Ploix, Stéphane

doi:10.1109/idaacs.2017.8095185

Cited by 1 publication

(1 citation statement)

References 14 publications

(10 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The following is an example of a generated textual explanation (in French): -Dans le créneau horaire 12h-13h, si vous aviez laissé la porte ouverte beaucoup plus longtemps, le confort thermique aurait augmenté beaucoup, la qualité de l'air un petit peu et le créneau 13h-14h aurait été impacté, il y aurait eu un courant d'air sensible vers le couloir‖. More details can be found in [65].…”

Section: Natural Language Text Generationunclassified

From Usable to Incentive-Building Energy Management Systems

Alyafi¹,

Nguyen²,

Laurillau³

et al. 2018

MUC

Self Cite

View full text Add to dashboard Cite

Reducing energy consumption is an individual and collective challenge that requires people to be proactive and fully involved. However, most approaches and solutions to this problem promote automated and autonomous systems that take full control of the decisions. Although these systems relieve users from setting the temperature in offices and homes, the level of thermal comfort chosen by the technology may not meet the individuals' requirements. Automating control is an obvious solution but it keeps the human out of the loop. In this paper, an alternative approach is promoted based on system-human cooperation that allows people to make the final decision. However, to make sound decisions, users need to understand the system functioning and rationale and they need to be convinced and motivated to change their habits with regard to energy consumption. Our approach is based on an e-coach system, called Involved, which provides users with contextual explanations along with a user interface designed to persuade the user to stay involved. The results of this research are the development of an early prototype that provides end-users with a 24-hour plan of recommended actions along with contextual explanations that justify each action. This plan satisfies the inhabitant's preferred compromise between the thermal comfort, air quality and financial cost specified by the inhabitant (i.e. the user) using a novel interaction technique, the Trade-Off-Pareto sliders. Users can edit the plan (e.g. suppress an action), skip some actions, perform additional actions, or even change their preferred compromise, and be informed in real time of the consequences on energy consumption and comfort. RÉSUMÉ. Réduire la consommation énergétique est un défi individuel et collectif qui exige que les personnes soient proactives et pleinement impliquées. La plupart des approches et des solutions actuelles reposent sur des systèmes automatisés et autonomes prenant le contrôle total des décisions. Bien que ces systèmes libèrent les occupants du réglage de la température dans leur bureau ou leur maison, le niveau de confort thermique choisi par la technologie peut ne pas satisfaire leurs exigences. L'automatisation du contrôle est une solution évidente, mais elle écarte l'humain du processus. Nous proposons une approche alternative reposant sur la coopération entre un gestionnaire énergétique et les occupants leur permettant de prendre la décision finale. Cependant, pour prendre de bonnes décisions, les occupants doivent comprendre le fonctionnement et la logique du système et être convaincus et motivés pour changer leurs habitudes en matière de consommation énergétique. Notre approche propose un assistant virtuel qui fournit aux occupants des explications contextuelles ainsi qu'une interaction homme-machine conçue pour persuader et maintenir l'utilisateur impliqué.

show abstract

Section: Natural Language Text Generationunclassified