Proceedings ArticleDOI
Peak-load shaving in smart homes via online scheduling
Giuseppe Tommaso Costanzo,Jan Kheir,Guchuan Zhu +2 more
- pp 1347-1352
TLDR
In this paper, the authors address the problem of load-shaving in smart homes in view of improving energy efficiency in Smart Grids and propose an architecture of home power management systems, allowing the separation of domestic power load control from grid dynamics.Abstract:
This paper addresses the problem of load-shaving in Smart Homes in view of improving energy efficiency in Smart Grids. An architecture of home power management systems, allowing the separation of domestic power load control from grid dynamics, is introduced. In this framework, the operation of appliances is modeled as a finite state machine which enables the implementation of online scheduling borrowed from the techniques developed in real-time computing systems. A scheduling algorithm is developed for peak-load shaving and the simulation results confirm the effectiveness and the efficiency of the proposed approach.read more
Citations
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Book ChapterDOI
Home energy management systems: A review of modelling and complexity
Marc Beaudin,Hamidreza Zareipour +1 more
TL;DR: A set of HEMS challenges such as forecast uncertainty, modelling device heterogeneity, multi-objective scheduling, computational limitations, timing considerations and modelling consumer well-being are discussed.
Journal ArticleDOI
Efficient energy consumption and operation management in a smart building with microgrid
TL;DR: In this article, the optimal scheduling of smart homes' energy consumption is studied using a mixed integer linear programming (MILP) approach, in order to minimise a 1-day forecasted energy consumption cost, DER operation and electricity consumption household tasks are scheduled based on real-time electricity pricing, electricity task time window and forecasted renewable energy output.
Journal ArticleDOI
A System Architecture for Autonomous Demand Side Load Management in Smart Buildings
TL;DR: This architecture can encapsulate the system functionality, assure the interoperability between various components, allow the integration of different energy sources, and ease maintenance and upgrading, and allows seamless integration of diverse techniques for online operation control, optimal scheduling, and dynamic pricing.
Journal ArticleDOI
Smart Home Activities: A Literature Review
TL;DR: In this paper, the authors present a review of the literature related to energy management system scheduling with respect to its control, automation, and communication, including a number of price schemes and load models needed for solving related scheduling optimization problems.
Journal ArticleDOI
Short-term smart learning electrical load prediction algorithm for home energy management systems
TL;DR: In this article, the authors presented a simple efficient day-ahead electrical load prediction approach for any energy management system (EMS) within buildings, which was designed to be apart of any generic EMS and it does not require to be associated with a prepared statistical or historical databases, or even to get connected to any kinds of sensors.
References
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Book
Hard Real-Time Computing Systems: Predictable Scheduling Algorithms and Applications
TL;DR: This book introduces the fundamental concepts of real-time computing, illustrates the most significant results in the field, and provides the essential methodologies for designing predictable computing systems which can be used to support critical control applications.
Journal ArticleDOI
Domestic electricity use: A high-resolution energy demand model
TL;DR: In this paper, a high-resolution model of domestic electricity use is presented based upon a combination of patterns of active occupancy (i.e. when people are at home and awake), and daily activity profiles that characterise how people spend their time performing certain activities.
Journal ArticleDOI
A decision tree method for building energy demand modeling
TL;DR: The results demonstrate that the use of decision tree method can classify and predict building energy demand levels accurately, identify and rank significant factors of building EUI automatically, and provide the combination of significant factors as well as the threshold values that will lead to high building energy performance.
Proceedings ArticleDOI
Two Market Models for Demand Response in Power Networks
TL;DR: Two abstract market models for designing demand response to match power supply and shape power demand are considered, characterized in competitive as well as oligopolistic markets, and proposed distributed demand response algorithms to achieve the equilibria.
Proceedings ArticleDOI
Incentive-Based Energy Consumption Scheduling Algorithms for the Smart Grid
Stéphane Caron,George Kesidis +1 more
TL;DR: A dynamic pricing scheme incentivizing consumers to achieve an aggregate load profile suitable for utilities, and how close they can get to an ideal flat profile depending on how much information they share is studied.