Related papers: Conversational Demand Response: Bidirectional Aggr…
Power utilities are adopting Automated Demand Response (ADR) to replace the costly fuel-fired generators and to preempt congestion during peak electricity demand. Similarly, third-party Demand Response (DR) aggregators are leveraging…
This paper describes an intelligent management algorithm for an aggregate of domestic electric water heaters called to provide a demand response service. This algorithm is developed using Model Predictive Control. The model of the entire…
Interactive conversational recommender systems have gained significant attention for their ability to capture user preferences through natural language interactions. However, existing approaches face substantial challenges in handling…
Within Multi Agent Systems, communication by means of Agent Communication Languages (ACLs) has a key role to play in the co-operation, co-ordination and knowledge-sharing between agents. Despite this, complex reasoning about agent…
Demand Response (DR) is a program designed to match supply and demand by modifying consumption profile. Some of these programs are based on economic incentives, in which, a user is paid to reduce his energy requirements according to an…
A major challenge to implementing residential demand response is that of aligning the objectives of many households, each of which aims to minimize its payments and maximize its comfort level, while balancing this with the objectives of an…
This paper studies the automated control method for regulating air conditioner (AC) loads in incentive-based residential demand response (DR). The critical challenge is that the customer responses to load adjustment are uncertain and…
This study explores the interaction between aggregators and building occupants in activating flexibility through Demand Response (DR) programs, with a focus on reinforcing the resilience of the energy system considering the uncertainties…
Demand Response (DR) has a widely recognized potential for improving grid stability and reliability while reducing customers energy bills. However, the conventional DR techniques come with several shortcomings, such as inability to handle…
The increasing uptake of distributed energy resources (DERs) in smart home prosumers calls for distributed energy management strategies, and the advances in information and communications technology enable peer-to-peer (P2P) energy trading…
In recent years, recommendation systems have evolved from providing a single list of recommendations to offering a comprehensive suite of topic focused services. To better accomplish this task, conversational recommendation systems (CRS)…
Long-term conversational agents face a fundamental scalability challenge as interactions extend over time: repeatedly processing entire conversation histories becomes computationally prohibitive. Current approaches attempt to solve this…
Retrieval-augmented generation (RAG) agents are increasingly deployed to answer questions over local knowledge bases that cannot be centralized due to knowledge-sovereignty constraints. This results in two recurring failures in production:…
In several smart city applications, multiple resources must be allocated among competing agents that are coupled through such shared resources and are constrained --- either through limitations of communication infrastructure or privacy…
Complex dialog systems often use retrieved evidence to facilitate factual responses. Such RAG (Retrieval Augmented Generation) systems retrieve from massive heterogeneous data stores that are usually architected as multiple indexes or APIs…
We introduce a resource adaptive agent mechanism which supports the user in interactive theorem proving. The mechanism uses a two layered architecture of agent societies to suggest appropriate commands together with possible command…
Retrieval-Augmented Generation (RAG) systems and large language model (LLM)-powered chatbots have significantly advanced conversational AI by combining generative capabilities with external knowledge retrieval. Despite their success,…
Recommender systems are embracing conversational technologies to obtain user preferences dynamically, and to overcome inherent limitations of their static models. A successful Conversational Recommender System (CRS) requires proper handling…
Nowadays the emerging smart grid technology opens up the possibility of two-way communication between customers and energy utilities. Demand Response Management (DRM) offers the promise of saving money for commercial customers and…
The electricity sector transition requires substantial increases in residential demand response capacity, yet Home Energy Management Systems (HEMS) adoption remains limited by user interaction barriers requiring translation of everyday…