7,738 research outputs found
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
Big Data and the Internet of Things
Advances in sensing and computing capabilities are making it possible to
embed increasing computing power in small devices. This has enabled the sensing
devices not just to passively capture data at very high resolution but also to
take sophisticated actions in response. Combined with advances in
communication, this is resulting in an ecosystem of highly interconnected
devices referred to as the Internet of Things - IoT. In conjunction, the
advances in machine learning have allowed building models on this ever
increasing amounts of data. Consequently, devices all the way from heavy assets
such as aircraft engines to wearables such as health monitors can all now not
only generate massive amounts of data but can draw back on aggregate analytics
to "improve" their performance over time. Big data analytics has been
identified as a key enabler for the IoT. In this chapter, we discuss various
avenues of the IoT where big data analytics either is already making a
significant impact or is on the cusp of doing so. We also discuss social
implications and areas of concern.Comment: 33 pages. draft of upcoming book chapter in Japkowicz and Stefanowski
(eds.) Big Data Analysis: New algorithms for a new society, Springer Series
on Studies in Big Data, to appea
Anticipatory Mobile Computing: A Survey of the State of the Art and Research Challenges
Today's mobile phones are far from mere communication devices they were ten
years ago. Equipped with sophisticated sensors and advanced computing hardware,
phones can be used to infer users' location, activity, social setting and more.
As devices become increasingly intelligent, their capabilities evolve beyond
inferring context to predicting it, and then reasoning and acting upon the
predicted context. This article provides an overview of the current state of
the art in mobile sensing and context prediction paving the way for
full-fledged anticipatory mobile computing. We present a survey of phenomena
that mobile phones can infer and predict, and offer a description of machine
learning techniques used for such predictions. We then discuss proactive
decision making and decision delivery via the user-device feedback loop.
Finally, we discuss the challenges and opportunities of anticipatory mobile
computing.Comment: 29 pages, 5 figure
Activity-Based Recommendations for Demand Response in Smart Sustainable Buildings
The energy consumption of private households amounts to approximately 30% of
the total global energy consumption, causing a large share of the CO2 emissions
through energy production. An intelligent demand response via load shifting
increases the energy efficiency of residential buildings by nudging residents
to change their energy consumption behavior. This paper introduces an activity
prediction-based framework for the utility-based context-aware multi-agent
recommendation system that generates an activity shifting schedule for a
24-hour time horizon to either focus on CO2 emissions or energy cost savings.
In particular, we design and implement an Activity Agent that uses hourly
energy consumption data. It does not require further sensorial data or activity
labels which reduces implementation costs and the need for extensive user
input. Moreover, the system enhances the utility option of saving energy costs
by saving CO2 emissions and provides the possibility to focus on both
dimensions. The empirical results show that while setting the focus on CO2
emissions savings, the system provides an average of 12% of emissions savings
and 7% of cost savings. When focusing on energy cost savings, 20% of energy
costs and 6% of emissions savings are possible for the studied households in
case of accepting all recommendations. Recommending an activity schedule, the
system uses the same terms residents describe their domestic life. Therefore,
recommendations can be more easily integrated into daily life supporting the
acceptance of the system in a long-term perspective
Smart Grid Technologies in Europe: An Overview
The old electricity network infrastructure has proven to be inadequate, with respect to modern challenges such as alternative energy sources, electricity demand and energy saving policies. Moreover, Information and Communication Technologies (ICT) seem to have reached an adequate level of reliability and flexibility in order to support a new concept of electricity network—the smart grid. In this work, we will analyse the state-of-the-art of smart grids, in their technical, management, security, and optimization aspects. We will also provide a brief overview of the regulatory aspects involved in the development of a smart grid, mainly from the viewpoint of the European Unio
Recommended from our members
Interactive Demand Shifting in the context of Domestic Micro-Generation
The combination of ubiquitous computing and emerging energy technologies is radically changing the home energy landscape. Domestic micro-generation, dominated by solar photovoltaic, is increasing at a rapid pace. This represents an opportunity for creating and altering energy behaviours. However, these transformations generate new challenges that we call the domestic energy gap: domestic electricity consumption and microgeneration are out of sync. Micro-generation is mainly uncontrollable production relying on weather while domestic energy consumption tends to happen mostly during the evening. This thesis focuses on understanding and supporting new domestic practices in the context of domestic solar electricity generation, looking at ‘Demand-Shifting’. Specifically, we look at how can digital tools leverage Demand-Shifting practices in the context of domestic micro-generation? Relying on a mixed-method approach, we provide a qualitative and quantitative answer with the collaboration of 38 participating households in several field studies including two spanning more than eight months. Through a deep investigation of laundry and electric mobility routines in the context of domestic micro-generation, we emphasised a natural engagement into Demand-Shifting which appeared as a complex and time-consuming task for participants which was not visible when we analysed their quantitative data. We revealed this complexity through Participatory Data Analyses, a method we designed to analyse the data in collaboration with the participating householders. This provided us with a comprehensive view of the relationship between domestic micro-generation and daily routines. Finally, we highlight the need for timely and contextual support through the deployment of interventions in-the-wild. Building on discussions of our findings in perspective of the literature, we propose a conceptual framework to support domestic interactive Demand-Shifting
Algorithms for appliance usage prediction
Demand-Side Management (DSM) is one of the key elements of future Smart Electricity Grids. DSM involves mechanisms to reduce or shift the consumption of electricity in an attempt to minimise peaks. By so doing it is possible to avoid using expensive peaking plants that are also highly carbon emitting. A key challenge in DSM, however, is the need to predict energy usage from specific home appliances accurately so that consumers can be notified to shift or reduce the use of high energy-consuming appliances. In some cases, such notifications may be also need to be given at very short notice. Hence, to solve the appliance usage prediction problem, in this thesis we develop novel algorithms that take into account both users' daily practices (by taking advantage of the cyclic nature of routine activities) and the inter-dependency between the usage of multiple appliances (i.e., the user's typical consumption patterns). We propose two prediction algorithms to satisfy the needs for fast prediction and high accuracy respectively: i) a rule-based approach, EGH-H, for scenarios in which notifications need to be given at short notice, to find significant patterns in the use of appliances that can capture the user's behaviour (or habits), ii) a graphical{model based approach, GM-PMA (Graphical Model for Prediction in Multiple Appliances) for scenarios that require high prediction accuracy. We demonstrate through extensive empirical evaluations on real{world data from a prominent database of home energy usage that GM-PMA outperforms existing methods by up to 41%, and the runtime of EGH-H is 100 times lower on average, than that of other benchmark algorithms, while maintaining competitive prediction accuracy. Moreover, we demonstrate the use of appliance usage prediction algorithms in the context of demand{side management by proposing an Intelligent Demand Responses (IDR) mechanism, where an agent uses Logistic Inference to learn the user's preferences, and hence provides the best personalised suggestions to the user. We use simulations to evaluate IDR on a number of user types, and show that, by using IDR, users are likely to improve their savings significantly
- …