The international community has largely recognized that the Earth’s climate is changing. Mitigating its global effects requires international actions. The European Union (EU) is leading several initiatives focused on reducing the problems. Specifically, the Climate Action tries to both decrease EU greenhouse gas emissions and improve energy efficiency by reducing the amount of primary energy consumed, and it has pointed to the development of efficient building energy management systems as key. In traditional buildings, households are responsible for continuously monitoring and controlling the installed Heating, Ventilation, and Air Conditioning (HVAC) system. Unnecessary energy consumption might occur due to, for example, forgetting devices turned on, which overwhelms users due to the need to tune the devices manually. Nowadays, smart buildings are automating this process by automatically tuning HVAC systems according to user preferences in order to improve user satisfaction and optimize energy consumption. Towards achieving this goal, in this paper, we compare 36 Machine Learning algorithms that could be used to forecast indoor temperature in a smart building. More specifically, we run experiments using real data to compare their accuracy in terms of R-coefficient and Root Mean Squared Error and their performance in terms of Friedman rank. The results reveal that the ExtraTrees regressor has obtained the highest average accuracy (0.97%) and performance (0,058%) over all horizons.

Quantified self apps and other personal informatics designs have rapidly grown in popularity through the advent of convenient Internet of Things related wearables and improved sensor technology. The rapid growth has, however, left its mark on how we study this area as the utility that could be provided has become the centerpiece of attention. This appears somewhat contradictory, as personal informatics technology becomes integrated in life in nuanced ways, contributing value beyond the instrumental data analysis role. In this paper, we propose relying on postphenomenology as a useful foundation for extending the study of personal informatics, and provide three concluding implications intended to guide the discussion of such an extension.

As a result of technological advancements, functional capacity assessments, such as the 6-minute walk test, can be performed remotely, at home and in the community. Current studies, however, tend to overlook the crucial aspect of data quality, often limiting their focus to idealised scenarios. Challenging conditions may arise when performing a test given the risk of collecting poor-quality GNSS signal, which can undermine the reliability of the results. This work shows the impact of applying filtering rules to avoid noisy samples in common algorithms that compute the walked distance from positioning data. Then, based on signal features, we assess the reliability of the distance estimation using logistic regression from the following two perspectives: error-based analysis, which relates to the estimated distance error, and user-based analysis, which distinguishes conventional from unconventional tests based on users’ previous annotations. We highlight the impact of features associated with walked path irregularity and direction changes to establish data quality. We evaluate features within a binary classification task and reach an F1-score of 0.93 and an area under the curve of 0.97 for the user-based classification. Identifying unreliable tests is helpful to clinicians, who receive the recorded test results accompanied by quality assessments, and to patients, who can be given the opportunity to repeat tests classified as not following the instructions.

With the increasing diffusion of Internet of Things (IoT) technologies, the transportation of goods sector is in a position to adopt novel intelligent services that cut across the otherwise highly fragmented and heterogeneous market, which today consists of a myriad of actors. Legacy systems that rely upon direct integration between all actors involved in the transportation ecosystem face considerable challenges for information sharing. Meanwhile, IoT based services, which are designed as devices that follow goods and communicate directly to cloud-based backend systems, may provide services that previously were not available. For the purposes of this paper, we present a theoretical framework for classification of such intelligent goods systems based on a literature study. The framework, labelled as the Intelligent Goods Service (IGS) framework, aims at increasing the understanding of the actors, agents, and services involved in an intelligent goods system, and to facilitate system comparisons and the development of new innovative solutions. As an illustration of how the IGS framework can be used and contribute to research in this area, we provide an example from a direct industry-academia collaboration.

Adaptive structuration theory (AST)has been used for a number of years in the information systems discipline to study the use of new technologies in organizations. In this paper it is applied to a relatively new technology, context-aware applications. AST provides a useful lens for examining the impact of a particular context-aware application, CABdriver. Used in conjunction with the repertory grid technique and lead users in an action research study, a research approach for exploring the ways in which the technology impacts individuals within small groups is presented.