5,004 research outputs found
A Distributed Demand-Side Management Framework for the Smart Grid
This paper proposes a fully distributed Demand-Side Management system for
Smart Grid infrastructures, especially tailored to reduce the peak demand of
residential users. In particular, we use a dynamic pricing strategy, where
energy tariffs are function of the overall power demand of customers. We
consider two practical cases: (1) a fully distributed approach, where each
appliance decides autonomously its own scheduling, and (2) a hybrid approach,
where each user must schedule all his appliances. We analyze numerically these
two approaches, showing that they are characterized practically by the same
performance level in all the considered grid scenarios. We model the proposed
system using a non-cooperative game theoretical approach, and demonstrate that
our game is a generalized ordinal potential one under general conditions.
Furthermore, we propose a simple yet effective best response strategy that is
proved to converge in a few steps to a pure Nash Equilibrium, thus
demonstrating the robustness of the power scheduling plan obtained without any
central coordination of the operator or the customers. Numerical results,
obtained using real load profiles and appliance models, show that the
system-wide peak absorption achieved in a completely distributed fashion can be
reduced up to 55%, thus decreasing the capital expenditure (CAPEX) necessary to
meet the growing energy demand
Simulating the deep decarbonisation of residential heating for limiting global warming to 1.5C
Whole-economy scenarios for limiting global warming to 1.5C suggest that
direct carbon emissions in the buildings sector should decrease to almost zero
by 2050, but leave unanswered the question how this could be achieved by
real-world policies. We take a modelling-based approach for simulating which
policy measures could induce an almost-complete decarbonisation of residential
heating, the by far largest source of direct emissions in residential
buildings. Under which assumptions is it possible, and how long would it take?
Policy effectiveness highly depends on behavioural decision- making by
households, especially in a context of deep decarbonisation and rapid
transformation. We therefore use the non-equilibrium bottom-up model FTT:Heat
to simulate policies for a transition towards low-carbon heating in a context
of inertia and bounded rationality, focusing on the uptake of heating
technologies. Results indicate that the near-zero decarbonisation is achievable
by 2050, but requires substantial policy efforts. Policy mixes are projected to
be more effective and robust for driving the market of efficient low-carbon
technologies, compared to the reliance on a carbon tax as the only policy
instrument. In combination with subsidies for renewables, near-complete
decarbonisation could be achieved with a residential carbon tax of
50-200Euro/tCO2. The policy-induced technology transition would increase
average heating costs faced by households initially, but could also lead to
cost reductions in most world regions in the medium term. Model projections
illustrate the uncertainty that is attached to household behaviour for
prematurely replacing heating systems
Upscaling energy control from building to districts: current limitations and future perspectives
Due to the complexity and increasing decentralisation of the energy infrastructure, as well as growing penetration of renewable generation and proliferation of energy prosumers, the way in which energy consumption in buildings is managed must change. Buildings need to be considered as active participants in a complex and wider district-level energy landscape. To achieve this, the authors argue the need for a new generation of energy control systems capable of adapting to near real-time environmental conditions while maximising the use of renewables and minimising energy demand within a district environment. This will be enabled by cloud-based demand-response strategies through advanced data analytics and optimisation, underpinned by semantic data models as demonstrated by the Computational Urban Sustainability Platform, CUSP, prototype presented in this paper. The growing popularity of time of use tariffs and smart, IoT connected devices offer opportunities for Energy Service Companies, ESCo’s, to play a significant role in this new energy landscape. They could provide energy management and cost savings for adaptable users, while meeting energy and CO2 reduction targets. The paper provides a critical review and agenda setting perspective for energy management in buildings and beyond
Scaling energy management in buildings with artificial intelligence
L'abstract è presente nell'allegato / the abstract is in the attachmen
A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments
In recent years, due to the unnecessary wastage of electrical energy in
residential buildings, the requirement of energy optimization and user comfort
has gained vital importance. In the literature, various techniques have been
proposed addressing the energy optimization problem. The goal of each technique
was to maintain a balance between user comfort and energy requirements such
that the user can achieve the desired comfort level with the minimum amount of
energy consumption. Researchers have addressed the issue with the help of
different optimization algorithms and variations in the parameters to reduce
energy consumption. To the best of our knowledge, this problem is not solved
yet due to its challenging nature. The gap in the literature is due to the
advancements in the technology and drawbacks of the optimization algorithms and
the introduction of different new optimization algorithms. Further, many newly
proposed optimization algorithms which have produced better accuracy on the
benchmark instances but have not been applied yet for the optimization of
energy consumption in smart homes. In this paper, we have carried out a
detailed literature review of the techniques used for the optimization of
energy consumption and scheduling in smart homes. The detailed discussion has
been carried out on different factors contributing towards thermal comfort,
visual comfort, and air quality comfort. We have also reviewed the fog and edge
computing techniques used in smart homes
From Iconic Design to Lost Luggage: Innovation at Heathrow Terminal 5
This paper aims to contribute to understanding of how organizations respond to risk and uncertainty by combining and balancing routines and innovation. It shows how approaches to risk and uncertainty are shaped by the contractual framework in large multi-party projects. The paper addresses a gap in the literature on how risk and uncertainty is managed to deliver innovation in large-scale ‘megaprojects’. These megaprojects are notorious for high rates of failure that conventionally evoke organizational strategies avoiding risks and uncertainties. Yet strategies for managing risk and uncertainty are essential to the routines and innovation that overcome the challenges of successfully delivering large-scale, complex projects.
Machine Learning for Smart and Energy-Efficient Buildings
Energy consumption in buildings, both residential and commercial, accounts
for approximately 40% of all energy usage in the U.S., and similar numbers are
being reported from countries around the world. This significant amount of
energy is used to maintain a comfortable, secure, and productive environment
for the occupants. So, it is crucial that the energy consumption in buildings
must be optimized, all the while maintaining satisfactory levels of occupant
comfort, health, and safety. Recently, Machine Learning has been proven to be
an invaluable tool in deriving important insights from data and optimizing
various systems. In this work, we review the ways in which machine learning has
been leveraged to make buildings smart and energy-efficient. For the
convenience of readers, we provide a brief introduction of several machine
learning paradigms and the components and functioning of each smart building
system we cover. Finally, we discuss challenges faced while implementing
machine learning algorithms in smart buildings and provide future avenues for
research at the intersection of smart buildings and machine learning
Occupant Plugload Management for Demand Response in Commercial Buildings: Field Experimentation and Statistical Characterization
Commercial buildings account for approximately 36% of US electricity
consumption, of which nearly two-thirds is met by fossil fuels [1] resulting in
an adverse impact on the environment. Reducing this impact requires improving
energy efficiency and lowering energy consumption. Most existing studies focus
on designing methods to regulate and reduce HVAC and lighting energy
consumption. However, few studies have focused on the control of occupant
plugload energy consumption. In this study, we conducted multiple experiments
to analyze changes in occupant plugload energy consumption due to monetary
incentives and/or feedback. The experiments were performed in government office
and university buildings at NASA Research Park located in Moffett Field, CA.
Analysis of the data reveal significant plugload energy reduction can be
achieved via feedback and/or incentive mechanisms. Autoregressive models are
used to predict expected plugload savings in the presence of exogenous
variables. The results of this study suggest that occupant-in-the-loop control
architectures have the potential to reduce energy consumption and hence lower
the carbon footprint of commercial buildings.Comment: 20 pages, 15 figures, 4 tables, preprin
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