iSEA: IoT-based smartphone energy assistant for prompting energy-aware behaviors in commercial buildings

Providing personalized energy-use information to individual occupants enables the adoption of energy-aware behaviors in commercial buildings. However, the implementation of individualized feedback still remains challenging due to the difficulties in collecting personalized data, tracking personal behaviors, and delivering personalized tailored information to individual occupants. Nowadays, the Internet of Things (IoT) technologies are used in a variety of applications including real-time monitoring, control, and decision-making due to the flexibility of these technologies for fusing different data streams. In this paper, we propose a novel IoT-based smartphone energy assistant (iSEA) framework which prompts energy-aware behaviors in commercial buildings. iSEA tracks individual occupants through tracking their smartphones, uses a deep learning approach to identify their energy usage, and delivers personalized tailored feedback to impact their usage. iSEA particularly uses an energy-use efficiency index (EEI) to understand behaviors and categorize them into efficient and inefficient behaviors. The iSEA architecture includes four layers: physical, cloud, service, and communication. The results of implementing iSEA in a commercial building with ten occupants over a twelve-week duration demonstrate the validity of this approach in enhancing individualized energy-use behaviors. An average of 34% energy savings was measured by tracking occupants’ EEI by the end of the experimental period. In addition, the results demonstrate that commercial building occupants often ignore controlling over lighting systems at their departure events that leads to wasting energy during non-working hours. By utilizing the existing IoT devices in commercial buildings, iSEA significantly contributes to support research efforts into sensing and enhancing energy-aware behaviors at minimal costs

Structural Prediction of Protein–Protein Interactions by Docking: Application to Biomedical Problems

A huge amount of genetic information is available thanks to the recent advances in sequencing technologies and the larger computational capabilities, but the interpretation of such genetic data at phenotypic level remains elusive. One of the reasons is that proteins are not acting alone, but are specifically interacting with other proteins and biomolecules, forming intricate interaction networks that are essential for the majority of cell processes and pathological conditions. Thus, characterizing such interaction networks is an important step in understanding how information flows from gene to phenotype. Indeed, structural characterization of protein–protein interactions at atomic resolution has many applications in biomedicine, from diagnosis and vaccine design, to drug discovery. However, despite the advances of experimental structural determination, the number of interactions for which there is available structural data is still very small. In this context, a complementary approach is computational modeling of protein interactions by docking, which is usually composed of two major phases: (i) sampling of the possible binding modes between the interacting molecules and (ii) scoring for the identification of the correct orientations. In addition, prediction of interface and hot-spot residues is very useful in order to guide and interpret mutagenesis experiments, as well as to understand functional and mechanistic aspects of the interaction. Computational docking is already being applied to specific biomedical problems within the context of personalized medicine, for instance, helping to interpret pathological mutations involved in protein–protein interactions, or providing modeled structural data for drug discovery targeting protein–protein interactions.Spanish Ministry of Economy grant number BIO2016-79960-R; D.B.B. is supported by a predoctoral fellowship from CONACyT; M.R. is supported by an FPI fellowship from the Severo Ochoa program. We are grateful to the Joint BSC-CRG-IRB Programme in Computational Biology.Peer ReviewedPostprint (author's final draft

Introducing smart grids in residential contexts : consumers' perception of smart household appliances

A more energy efficient supply and demand in household settings is high on the agenda. Smart grids, smart meters, demand side management and smart appliances play a crucial role in this context. Many stakeholders are involved, but the exact role of the customer is often neglected. More specifically, his opinion, attitude, drivers or barriers towards new ways of energy consumption and energy management. This paper employs a user-centric perspective. It aims at mapping consumers perception of the possibilities of demand side management through smart household appliances. A quantitative survey was conducted among 500 households spread over Flanders, Belgium. In this paper, the results of this survey with regard to the respondents perception of smart appliances are presented. The Technology Acceptance Model was used as the theoretical framework to measure these perceptions

Agents for educational games and simulations

This book consists mainly of revised papers that were presented at the Agents for Educational Games and Simulation (AEGS) workshop held on May 2, 2011, as part of the Autonomous Agents and MultiAgent Systems (AAMAS) conference in Taipei, Taiwan. The 12 full papers presented were carefully reviewed and selected from various submissions. The papers are organized topical sections on middleware applications, dialogues and learning, adaption and convergence, and agent applications

IMPROVING RESIDENTIAL ENERGY CONSUMPTION AT LARGE USING PERSUASIVE SYSTEMS

The paper presents a persuasive web application that stimulates residential energy conservation. The users of the application received consumption feedback that is based on electricity meter readings which they entered over a period of 6 months and which accounted for specific household characteristics. In a large scale field study which we conducted between April and September of 2010, 6’921 participants used the application. From a research perspective, the system allowed us to experimentally assess the effects of different socio-psychological concepts with regard to different measures such as popularity, choice, and energy conservation. The large user base and the real-world setting contributed to the validity of the findings. The discussion presented is structured along a behavioural change framework we adapted from Ölander’s and J. Thøgersen’s motivation – ability – opportunity model. Besides presenting the quantitative results of multiple studies and providing theoretical cues to outline the mechanisms behind the behaviour, we formulate guidelines that support the development of similar applications in research and industry

Breaking Barriers to Renewable Energy Production in the North American Arctic

As climate change continues to affect our lives, the communities at the northern extremes of our world have witnessed the changes most profoundly. In the Arctic, where climate change is melting permafrost and causing major shoreline erosion, remote communities in Alaska and northern Canada are particularly vulnerable. Furthermore, these communities have limited access to electrical grids and bear oppressive energy costs relying on diesel generators. While some communities have started to incorporate renewable energy into their hamlets and villages, progress has generally been limited with the notable exception of Canada’s Northwest Territories and some coastal communities in western Alaska. During its latest stint as chair of the Arctic Council, the United States outlined community renewable energy in the Arctic as one of its primary goals. This Note focuses on regulatory and practical policy solutions to make that goal possible. It draws on examples from industrialized countries, such as Canada and the United Kingdom, as well as examples from developing countries, such as India and Peru, to examine solutions for the technical, economic, regulatory, and community engagement problems that Arctic communities in Alaska face when setting up new energy projects. Additionally, this Note describes the current political structure of Alaskan villages under the Alaska Native Claims Settlement Act and argues that Alaska Native Corporations should play a role in developing clean, cheap energy sources for their shareholders. Finally, this Note argues that public-private partnerships, like the non-profit Arctic Energy Alliance in the Northwest Territories, shows that clean, renewable energy projects for rural Arctic villages are possible throughout the Arctic. This Note draws lessons from other communities throughout the world and attempts to apply them to the unique situations that remote northern Alaska communities face regarding access to clean, renewable energy