5,364 research outputs found
Connecting to smart cities : analyzing energy times series to visualize monthly electricity peak load in residential buildings
Rapidly growing energy consumption rate is considered an alarming threat to economic stability and environmental sustainability. There is an urgent need of proposing novel solutions to mitigate the drastic impact of increased energy demand in urban cities to improve energy efficiency in smart buildings. It is commonly agreed that exploring, analyzing and visualizing energy consumption patterns in residential buildings can help to estimate their energy demands. Moreover, visualizing energy consumption patterns of residential buildings can also help to diagnose if there is any unpredictable increase in energy demand at a certain time period. However, visualizing and inferring energy consumption patterns from typical line graphs, bar charts, scatter plots is obsolete, less informative and do not provide deep and significant insight of the daily domestic demand of energy utilization. Moreover, these methods become less significant when high temporal resolution is required. In this research work, advanced data exploratory and data analytics techniques are applied on energy time series. Data exploration results are presented in the form of heatmap. Heatmap provides a significant insight of energy utilization behavior during different times of the day. Heatmap results are articulated from three analytical perspectives; descriptive analysis, diagnostic analysis and contextual analysis
Multi-domain analysis of photovoltaic impacts via integrated spatial and probabilistic modelling
Currently, the impacts of wide-scale implementation of photovoltaic (PV) technology are evaluated
in terms of such indicators as rated capacity, energy output or return on investment. However, as PV markets mature,
consideration of additional impacts (such as electricity transmission and distribution infrastructure or socio-economic
factors) is required to evaluate potential costs and benefits of wide-scale PV in relation to specific policy objectives.
This study describes a hybrid GIS spatio-temporal modelling approach integrating probabilistic analysis via a Bayesian
technique to evaluate multi-scale/multi-domain impacts of PV. First, a wide-area solar resource modelling approach
utilising GIS-based dynamic interpolation is presented and the implications for improved impact analysis on electrical
networks are discussed. Subsequently, a GIS-based analysis of PV deployment in an area of constrained electricity
network capacity is presented, along with an impact analysis of specific policy implementation upon the spatial
distribution of increasing PV penetration. Finally, a Bayesian probabilistic graphical model for assessment of socioeconomic
impacts of domestic PV at high penetrations is demonstrated. Taken together, the results show that
integrated spatio-temporal probabilistic assessment supports multi-domain analysis of the impacts of PV, thereby
providing decision makers with a tool to facilitate deliberative and systematic evidence-based policy making
incorporating diverse stakeholder perspectives
Complexity Aided Design: the FuturICT Technological Innovation Paradigm
"In the next century, planet earth will don an electronic skin. It will use
the Internet as a scaffold to support and transmit its sensations. This skin is
already being stitched together. It consists of millions of embedded electronic
measuring devices: thermostats, pressure gauges, pollution detectors, cameras,
microphones, glucose sensors, EKGs, electroencephalographs. These will probe
and monitor cities and endangered species, the atmosphere, our ships, highways
and fleets of trucks, our conversations, our bodies--even our dreams ....What
will the earth's new skin permit us to feel? How will we use its surges of
sensation? For several years--maybe for a decade--there will be no central
nervous system to manage this vast signaling network. Certainly there will be
no central intelligence...some qualities of self-awareness will emerge once the
Net is sensually enhanced. Sensuality is only one force pushing the Net toward
intelligence". These statements are quoted by an interview by Cherry Murray,
Dean of the Harvard School of Engineering and Applied Sciences and Professor of
Physics. It is interesting to outline the timeliness and highly predicting
power of these statements. In particular, we would like to point to the
relevance of the question "What will the earth's new skin permit us to feel?"
to the work we are going to discuss in this paper. There are many additional
compelling questions, as for example: "How can the electronic earth's skin be
made more resilient?"; "How can the earth's electronic skin be improved to
better satisfy the need of our society?";"What can the science of complex
systems contribute to this endeavour?
Energy Poverty
EVALUATE is a multi-sited study, involving extensive research across a variety of cities and countries. Focusing primarily on four Central and
Eastern European cities (Budapest, GdanÌsk, Prague and Skopje) the project
has undertaken a customized survey with 2435 households, supplemented
with insights from in-depth household interviews, âenergy diariesâ
and energy efficiency audits in the homes of approximately 160 households
living in the four cities. EVALUATE has entailed 195 expert interviews
in a much wider range of sites across the world, as well as an analysis
of micro-data from national and European Union surveys of energy poverty.
It has led to more than 200 dissemination activities, while laying the
basis for the European Energy Poverty Observatory as well as a new
European Co-operation for Science and Technology Action on âEuropean
Energy Poverty: Agenda Co-Creation and Knowledge Innovation
Systemic Design for the innovation of home appliances The meaningfulness of data in designing sustainable systems
This work addressed the domestic environment considering this context as a complex system characterised by significant impacts in terms of
resource consumption. Within the theoretical framework of Systemic Design (SD), this thesis focused on home appliances, in order to understand how to reduce the impact directly attributable to them, while optimising and simplifying daily tasks for the user. A design methodology towards environmental sustainability has been structured, by focusing on the use of data for design purposes and on creating value for the user through meaningful products. It considers the user, the product and the environment as central topics, by giving them the same relevance and the literature review is structured accordingly, investigating needs and requirements, ethical issues, but also current products and future scenarios. During my experience at TU Delft, I spent six months in the Department of Internet of Things at the Faculty of Industrial Design Engineering.
Together with computer scientists, we developed a prototype to collect some missing data, establishing the importance of grounding the decision-making on reliable information. IoT and data gathering open a variety of possibilities in monitoring, accessing more precise knowledge of products and households useful for design purposes, up to understand how to fill the gap perceived by the user between needs and solutions. It considered the potential benefits of using IoT indicators to collect missing information about both the product, its use and its operating environment to address critical aspects in the design stage, thus extending productsâ lifetime.
This thesis highlighted the importance of building multidisciplinary design teams to investigate different classes of requirements, and the need for flexible tools to cope with complex and evolving requirements, the co-evolution of problem and solutions and investigating open-ended questions. This approach leaves room for addressing every step of the traditional life-cycle in a more circular way, shifting the focus from the life-cycle centrality of the previous century to a more complex vision about the product
Insights on Significant Implication on Research Approach for Enhancing 5G Network System
With the exponential growth of mobile users, there is a massive growth of data as well as novel services to support such data management. However, the existing 4G network is absolutely not meant for catering up such higher demands of bandwidth utilization as well as servicing massive users with similar Quality of service. Such problems are claimed to be effectively addressed by the adoption of 5G networking system. Although the characteristics of 5G networking are theoretically sound, still it is under the roof of the research. Therefore, this paper presents a discussion about the conventional approach as well as an approach using cognitive radio network towards addressing the frequently identified problems of energy, resource allocation, and spectral efficiency. The study collects the existing, recent researches in the domain of 5G communications from various publications. Different from existing review work, the paper also contributes towards identifying the core research findings as well as a significant research gap towards improving the communication in the 5G network system
Advanced Optimization Methods and Big Data Applications in Energy Demand Forecast
The use of data collectors in energy systems is growing more and more. For example, smart sensors are now widely used in energy production and energy consumption systems. This implies that huge amounts of data are generated and need to be analyzed in order to extract useful insights from them. Such big data give rise to a number of opportunities and challenges for informed decision making. In recent years, researchers have been working very actively in order to come up with effective and powerful techniques in order to deal with the huge amount of data available. Such approaches can be used in the context of energy production and consumption considering the amount of data produced by all samples and measurements, as well as including many additional features. With them, automated machine learning methods for extracting relevant patterns, high-performance computing, or data visualization are being successfully applied to energy demand forecasting. In light of the above, this Special Issue collects the latest research on relevant topics, in particular in energy demand forecasts, and the use of advanced optimization methods and big data techniques. Here, by energy, we mean any kind of energy, e.g., electrical, solar, microwave, or win
A Smart Products Lifecycle Management (sPLM) Framework - Modeling for Conceptualization, Interoperability, and Modularity
Autonomy and intelligence have been built into many of todayâs mechatronic products, taking advantage of low-cost sensors and advanced data analytics technologies. Design of product intelligence (enabled by analytics capabilities) is no longer a trivial or additional option for the product development. The objective of this research is aimed at addressing the challenges raised by the new data-driven design paradigm for smart products development, in which the product itself and the smartness require to be carefully co-constructed.
A smart product can be seen as specific compositions and configurations of its physical components to form the body, its analytics models to implement the intelligence, evolving along its lifecycle stages. Based on this view, the contribution of this research is to expand the âProduct Lifecycle Management (PLM)â concept traditionally for physical products to data-based products. As a result, a Smart Products Lifecycle Management (sPLM) framework is conceptualized based on a high-dimensional Smart Product Hypercube (sPH) representation and decomposition.
First, the sPLM addresses the interoperability issues by developing a Smart Component data model to uniformly represent and compose physical component models created by engineers and analytics models created by data scientists. Second, the sPLM implements an NPD3 process model that incorporates formal data analytics process into the new product development (NPD) process model, in order to support the transdisciplinary information flows and team interactions between engineers and data scientists. Third, the sPLM addresses the issues related to product definition, modular design, product configuration, and lifecycle management of analytics models, by adapting the theoretical frameworks and methods for traditional product design and development.
An sPLM proof-of-concept platform had been implemented for validation of the concepts and methodologies developed throughout the research work. The sPLM platform provides a shared data repository to manage the product-, process-, and configuration-related knowledge for smart products development. It also provides a collaborative environment to facilitate transdisciplinary collaboration between product engineers and data scientists
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