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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
Getting Smart (Grids): An Efficiency Frontier Assessment
Information and communication technology are reshaping the electricity industry, with economic, environmental, and regulatory consequences. Smart grids allow the growing integration of renewable energy sources, a horizontalization of the roles of producers and consumers, a flatter demand profile which save investments intended to supply peaks of consumption, idle at great extent off-peaks. On the other hand, smart grids require important investments for modernizing technology. Concerning our objectives, firstly, we seek to understand the conceptual consequences of the irruption of smart grids on the electricity sector, and its importance for renewables adoption. Secondly, we discuss policies and regulations needed to accelerate the transformation of the electricity network in a smart grid, and to increase the renewables? share on total energy. Thirdly, our empirical approach runs a Data Envelopment Analysis (DEA) model to estimate the efficiency gains in the transition between traditional and smart grids. Our results show the efficiency levels of those countries whose objective is to deliver electricity with high levels of quality of services, and at the same time, using more renewables (with fewer carbon emissions), and low cost of supply. We conclude discussing the implications of our empirical model, the limitations, and next stages in polishing the results.Fil: Ferro, Gustavo Adolfo. Universidad del Cema; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Romero, Carlos Adrián. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Instituto Interdisciplinario de Economía Política de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Económicas. Instituto Interdisciplinario de Economía Política de Buenos Aires; ArgentinaFil: Ramos, Maria Priscila. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Instituto Interdisciplinario de Economía Política de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Económicas. Instituto Interdisciplinario de Economía Política de Buenos Aires; ArgentinaLV Reunión Anual Asociación Argentina de Economía PolíticaCiudad Autónoma de Buenos AiresArgentinaAsociación Argentina de Economía Polític
A systematic literature review on the use of artificial intelligence in energy self-management in smart buildings
Buildings are one of the main consumers of energy in cities, which is why a lot of research has been generated around this problem. Especially, the buildings energy management systems must improve in the next years. Artificial intelligence techniques are playing and will play a fundamental role in these improvements. This work presents a systematic review of the literature on researches that have been done in recent years to improve energy management systems for smart building using artificial intelligence techniques. An originality of the work is that they are grouped according to the concept of "Autonomous Cycles of Data Analysis Tasks", which defines that an autonomous management system requires specialized tasks, such as monitoring, analysis, and decision-making tasks for reaching objectives in the environment, like improve the energy efficiency. This organization of the work allows us to establish not only the positioning of the researches, but also, the visualization of the current challenges and opportunities in each domain. We have identified that many types of researches are in the domain of decision-making (a large majority on optimization and control tasks), and defined potential projects related to the development of autonomous cycles of data analysis tasks, feature engineering, or multi-agent systems, among others.European Commissio
Residential Energy Management for Renewable Energy Systems Incorporating Data-Driven Unravelling of User Behavior
The penetration of distributed energy resources (DERs) such as photovoltaic (PV) at the residential
level has increased rapidly over the past year. It will inevitably induce a paradigm shift in end-user
and operations of local energy markets. The energy community with high integration of DERs
initiative allows its users to manage their generation (for prosumers) and consumption more
efficiently, resulting in various economic, social, and environmental benefits. Specifically, the local
energy communities and their members can legally engage in energy generation, distribution, supply,
consumption, storage, and sharing to increase levels of autonomy from the power grid, advance
energy efficiency, reduce energy costs, and decrease carbon emissions. Reducing energy
consumption costs is difficult for residential energy management without understanding the users'
preferences. The advanced measurement and communication technologies provide opportunities for
individual consumers/prosumers and local energy communities to adopt a more active role in
renewable-rich smart grids. Non-intrusive load monitoring (NILM) monitors the load activities from a
single point source, such as a smart meter, based on the assumption that different appliances have
different power consumption levels and features. NILM can extract the users' load consumption from
the smart meter to support the development of the smart grid for better energy management and
demand response (DR). Yet to date, how to design residential energy management, including home
energy management systems (HEMS) and community energy management systems (CEMS), with
an understanding of user preferences and willingness to participate in energy management, is still far
from being fully investigated. This thesis aims to develop methodologies for a resident energy
management system for renewable energy systems (RES) incorporating data-driven unravelling of
the user's energy consumption behaviour
Energy Data Analytics for Smart Meter Data
The principal advantage of smart electricity meters is their ability to transfer digitized electricity consumption data to remote processing systems. The data collected by these devices make the realization of many novel use cases possible, providing benefits to electricity providers and customers alike. This book includes 14 research articles that explore and exploit the information content of smart meter data, and provides insights into the realization of new digital solutions and services that support the transition towards a sustainable energy system. This volume has been edited by Andreas Reinhardt, head of the Energy Informatics research group at Technische Universität Clausthal, Germany, and Lucas Pereira, research fellow at Técnico Lisboa, Portugal
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