The energy consumption of residential and commercial buildings has risen steadily in recent years, an increase largely due to their HVAC systems. Expected energy loads, transportation, and storage as well as user behavior influence the quantity and quality of the energy consumed daily in buildings. However, technology is now available that can accurately monitor, collect, and store the huge amount of data involved in this process. Furthermore, this technology is capable of analyzing and exploiting such data in meaningful ways. Not surprisingly, the use of data science techniques to increase energy efficiency is currently attracting a great deal of attention and interest. This paper reviews how Data Science has been applied to address the most difficult problems faced by practitioners in the field of Energy Management, especially in the building sector. The work also discusses the challenges and opportunities that will arise with the advent of fully connected devices and new computational technologies.

Internet users are assisted by means of distributed intelligent agents in the information gathering process to find the fittest information to their needs. In this paper we present a distributed intelligent agent model where the communication of the evaluation of the retrieved information among the agents is carried out by using linguistic operators based on the 2-tuple fuzzy linguistic representation as a way to endow the retrieval process with a higher flexibility, uniformity and precision. The 2-tuple fuzzy linguistic representation model allows to make processes of computing with words without loss of information. IntroductionIn the framework of the information retrieval one of the most current problems nowadays for which the fuzzy linguistic approach may be very useful, is the retrieval, handling and identification of relevant information through the Internet.The fuzzy linguistic approach is an approximate technique, which represents qualitative aspects as linguistic values by means of linguistic variables, that is, variables whose values are not numbers but words or sentences in a natural or artificial language [36]. This approach has been applied successfully to different areas as economics [14,31], planning [1], decision-making [12, 32], information retrieval [3,9,18,19], etc.Intelligent agents [4,11,26,29,34] deal with the information gathering process assisting the Internet users to find the fittest information to their needs. Several proposals about intelligent software agents have been emerging in the recent last years, but the lack of connection, communication and consensus among them have lead to a decrease in the quality and suitability of the retrieved information besides the efficiency of the system in the recovering and filtering task. This fact keeps the need of proposals in the field, and emphasizes the importance of the design and development of intelligent software agent organisations, as well as hierarchies and architectures that hold up such structures [7,10,21,28,29].However, not only is needed some organization, but also a protocol of communication among the agents. The great variety of representations and evaluations of the information in the Internet is the main obstacle to this communication, and the problem becomes more noticeable when the user takes part in the process. The complexity of all these processes reveals the need of more flexibility in the communication among agents and between agents and the user [9,34,35]. For this purpose, several approaches related to mechanisms to introduce and handle flexible information through linguistic labels have been proposed both at levels of agents and users [8,33].The main drawback of these approaches is the lack of precision in the final results, due to the fact that appears a loss of information in the processes of computing with words (CW). To overcome this drawback in [15] was presented a linguistic computational model based on linguistic 2-tuples which provides a computational technique to deal with linguistic information in a precise ...

The discovery of new knowledge by mining medical databases is crucial in order to make an effective use of stored data, enhancing patient management tasks. One of the main objectives of data mining methods is to provide a clear and understandable description of patterns held in data. We introduce a new approach to ®nd association rules among quantitative values in relational databases. The semantics of such rules are improved by introducing imprecise terms in both the antecedent and the consequent, as these terms are the most commonly used in human conversation and reasoning. The terms are modeled by means of fuzzy sets de®ned in the appropriate domains. However, the mining task is performed on the precise data. These``fuzzy association rules'' are more informative than rules relating precise values. We also introduce a new measure of accuracy, based on Shortliffe and Buchanan's certainty factors [Shortliffe E, Buchanan B. Math Biosci 1975;23:351±79]. Also, the semantics of the usual measure of usefulness of an association rule, called support are discussed and some new criteria are introduced. Our new measures have been shown to be more understandable and appropriate than ordinary ones. Several experiments on large medical databases show that our new approach can provide useful knowledge with better semantics in this ®eld. #

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