Conversational Agents for Building Energy Efficiency -- Advising Housing Cooperatives in Stockholm on Reducing Energy Consumption
Shadaab Ghani, Anne H{\aa}kansson, Oleksii Pasichnyi, Hossein Shahrokni

TL;DR
This paper presents SPARA, a conversational agent system that leverages retrieval-augmented generation to provide energy efficiency advice to housing cooperatives in Stockholm, achieving advice accuracy comparable to municipal experts.
Contribution
Introduction of SPARA, a novel conversational agent system using RAG with LMs to support energy efficiency decisions for housing cooperatives.
Findings
SPARA achieves 80% precision in energy advice.
Preliminary pilot shows SPARA's advice is comparable to municipal experts.
Supports stakeholders in energy transition efforts.
Abstract
Housing cooperative is a common type of multifamily building ownership in Sweden. Although this ownership structure grants decision-making autonomy, it places a burden of responsibility on cooperative's board members. Most board members lack the resources or expertise to manage properties and their energy consumption. This ignorance presents a unique challenge, especially given the EU directives that prohibit buildings rated as energy classes F and G by 2033. Conversational agents (CAs) enable human-like interactions with computer systems, facilitating human-computer interaction across various domains. In our case, CAs can be implemented to support cooperative members in making informed energy retrofitting and usage decisions. This paper introduces a Conversational agent system, called SPARA, designed to advise cooperatives on energy efficiency. SPARA functions as an energy efficiency…
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Taxonomy
TopicsSpreadsheets and End-User Computing · AI in Service Interactions · Smart Grid Energy Management
