Mireille Jacomino

Tabu search for the optimization of household energy consumption

This paper focuses on demand-side load management applied to the residential sector. A home automation system controlling household energy is proposed. It is decomposed into three layers: anticipation, reactive and device layers. This paper deals with an anticipation layer that allocates energy by taking into account predicted events. It consists in computing both the starting times of some services and in determining set points of others while satisfying the maximal power constraint. A constraint satisfaction problem formulation has been proposed. Because the complexity is NP-hard, a tabu search is used to solve the problem. It maximizes user comfort and minimizes energy cost. An application example is presented

A Multi-agent Home Automation System for Power Management

This paper presents the principles of a Home Automation system dedicated to power management that adapts power consumption to available power ressources according to user comfort and cost criteria. The system relies on a multi-agent paradigm. Each agent is embedded into a power resource or an equipment, which may be an environment (thermal-air, thermal-water, ventilation, luminous) or a service (washing, cooking), and cooperates and coordinates its action with others in order to find acceptable near-optimal solution. The control algorithm is decomposed into two complementary mechanisms: an emergency mechanism, which protects from constraint violations, and an anticipation mechanism, which computes the best future set-points according to predicted consumptions and productions and to user criteria. The paper details a negotiation protocol used by the both mechanisms and presents some preliminary simulation results.

An Anticipation Mechanism for Power Management in a Smart Home using Multi-Agent Systems

This paper presents the principles of a home automation system dedicated to power management that adapts power consumption to available power ressources according to inhabitant comfort and cost criteria. The system relies on a multi-agent paradigm. Each agent supports a service achieved by several devices, it cooperates and coordinates its action with others in order to find an acceptable near-optimal solution. The control algorithm is decomposed into two complementary mechanisms: a reactive mechanism, which protects from constraint violations, and an anticipation mechanism, which computes a plan for global consumption according to predicted productions and consumptions and to inhabitant criteria. The paper shows how to compute a global consumption plan relying on Tabu Searchs algorithm and how to reduce the problem complexity by dividing the whole problem into independent sub-problems.

User behavior prediction in energy consumption in housing using Bayesian networks

This paper deals with the problem of the user behavior prediction in a home automation system. Anticipating the needed energy for a service is based on the available prediction (like user requests) which contains the uncertainties. When the future users requests are not available in a home automation system thanks to programmatic, it is interesting to predict it to anticipate the energy needed in order to avoid some problems like peak consumption. A general method to predict users requests for services in energy consumption is proposed. The method relies on Bayesian networks to predict and diagnose users behavior in housing. Some results and perspectives are presented in this paper.

Minimizing setup costs for parallel multi-purpose machines under load-balancing constraint

This article focuses on the minimization of the setup costs of a workshop modeled with parallel multi-purpose machines. Any admissible workshop configuration has to ensure that a load-balanced production plan meeting a given demand exists. This problem is shown to be NP-hard in the strong sense, and is stated as a mixed integer linear program. It is shown that under some hypotheses, it can be stated as a transportation problem and solved in polynomial time. An upper bound and lower bound are proposed, as well as a performance ratio assessment that is reached only when degenerate optimal solutions to the transportation problem exist.

A HOME AUTOMATION SYSTEM TO IMPROVE HOUSEHOLD ENERGY CONTROL

Abstract This paper presents a new three-layer household energy control system capable both to satisfy the maximum available electrical energy constraint and to maximize user comfort criteria. These layers are composed by: an equipment layer, with local and fast control mechanism, a protection layer, which is triggered when energy constraints are violated, and an anticipation mechanism, which adjusts future set-points of equipments in order to tackle energy events that can be forseen. The scheduling problem of the set-points of equipments is modelled by a graph and solved by the Bellman-Fords algorithm. This control mechanism makes it possible to have a more flexible control of the overall power consumption in housing in exploiting natural thermal energy accumulation.

Advantages of MAS for the Resolution of a Power Management Problem in Smart Homes

This paper contributes to the design of intelligent buildings. A Multi- Agents Home Automation System (MAHAS) is proposed which controls appliances and energy sources in buildings. The objective of this paper is to show that by using intelligent agents related to appliances, it is possible to improve the energy consumption/ production in buildings. The proposed MAHAS system is characterised by its openness, its scalability and its capability to manage diversity. In this paper, we show how a multi-agent system, well adapted to solve problems spatially distributed and opened, can dynamically adapt the consumption of energy to various constraints by exploiting the flexibilities of the services provided by domestic devices (services shifting, energy accumulating). Finally, we conclude on the contribution of Multi-Agent approach to the power management problem.

A multi-agent design for a home automation system dedicated to power management

This paper presents the principles of a Home Automation System dedicated to power management that adapts power consumption to available power ressources according to user comfort and cost criteria. The system relies on a multi-agent paradigm. Each agent, supporting a type of service achieved by one or several devices, cooperates and coordinates its action with others in order to find an acceptable near-optimal solution. The control algorithm is decomposed into two complementary mechanisms: a reactive mechanism, which protects from constraint violations, and an anticipation mechanism, which computes a predicted plan of global consumption according to predicted productions and consumptions and to user criteria. The paper presents a design for the Multi Agent Home Automation System.

An algebraic method for delay fault testing

This paper presents an algebraic method allowing an accurate analysis of delay faults. This method is based on the fact that some input values remain constant when two successive input vectors are applied. For a transition between two input states, the output function is reduced to a function of few variables. An analysis of the reduced function allows one to obtain the delay faults which are detected by the corresponding transition. The analysis allows one to know if a fault is robustly testable or non robustly testable and validatable, or weakly verifiable: in every case the corresponding tests are obtained. An application of the results to random testing of faults allows one to observe that some non robustly testable faults are easier to detect than some robustly testable faults.

Optimal power control for smart homes

Abstract This paper depicts a formulation of the global home electricity management problem, which consists in adjusting the electric energy consumption to the cost and availability variations of the power supply. The available flexibilities of the services to inhabitants are used to compute optimal plans for appliances control. Two optimization criteria are addressed: the users comfort and the economic cost. A global multi-layer solving approach is depicted that includes first the computation of consumption/production coordination plans and then the dynamic matching to the actual consumption/production data. The paper focuses on the computation of the anticipative plan through a MILP formulation of the energy management problem. Application examples are given.