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Recommendation of Workplaces in a Coworking Building: A Cyber-Physical Approach Supported by a Context-Aware Multi-Agent System

Publication . Gomes, Luis; Almeida, Carlos; Vale, Zita

Recommender systems are able to suggest the most suitable items to a given user, taking into account the user’s and item`s data. Currently, these systems are offered almost everywhere in the online world, such as in e-commerce websites, newsletters, or video platforms. To improve recommendations, the user’s context should be considered to provide more accurate algorithms able to achieve higher payoffs. In this paper, we propose a pre-filtering recommendation system that considers the context of a coworking building and suggests the best workplaces to a user. A cyber-physical context-aware multi-agent system is used to monitor the building and feed the pre-filtering process using fuzzy logic. Recommendations are made by a multi-armed bandit algorithm, using ϵ -greedy and upper confidence bound methods. The paper presents the main results of simulations for one, two, three, and five years to illustrate the use of the proposed system

2020-06Journal article

Open access

Using diverse sensors in load forecasting in an office building to support energy management

Publication . Ramos, Daniel; Teixeira, Brígida; Faria, Pedro; Gomes, Luis; Abrishambaf, Omid; Vale, Zita

The increasing penetration of renewable energy sources led to the development of several energy management approaches. One of the main topics in this field is related to the load forecast in buildings, which can contribute to more intelligent and sustainable energy consumption. However, it is necessary to build a proper forecast model, capable of detecting an accurate consumption profile. The minimum effort to achieve this is to extract a historic with energy consumptions to use as input. Additional information should be considered in order to achieve improvements in forecasting results. This way, information regarding the day of the week is discussed as a reliable source of information that may enhance the load forecast. In this paper, two forecasting techniques, namely neural networks and support vector machine, are used to predict the energy consumption of a building for all 5 min from a period. The proposed model finds the best forecasting technique and determines if the additional information regarding the day of the week enhances the load forecast. In this case study, a period of two years and a half data with a 5-minute time interval is used. Moreover, several tests are performed for varied inputs to understand if the insights are consistent for these tests. This data has been adapted from an office building to illustrate the advantages of the proposed methodology.

2020Journal article

Open access

Adaptive learning in multiagent systems for automated energy contacts negotiation

Publication . Pinto, Tiago; Vale, Zita

This paper presents the Adaptive Decision Support for Electricity Markets Negotiations (AiD-EM) system. AiD-EM is a multi-agent system that provides decision support to market players by incorporating multiple sub-(agent-based) systems, directed to the decision support of specific problems. These sub-systems make use of different artificial intelligence methodologies, such as machine learning and evolutionary computation, to enable players adaptation in the planning phase and in actual negotiations in auction-based markets and bilateral negotiations.

2020Book

Open access