Observe, Learn, and Adapt (OLA)—An Algorithm for Energy Management in Smart Homes Using Wireless Sensors and Artificial Intelligence

Qela, Blerim; Mouftah, Hussein T.

doi:10.1109/tsg.2012.2209130

Cited by 63 publications

(20 citation statements)

References 14 publications

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“…Although machine learning algorithms try to adjust the phenomenon of starting any electrical device out of the scheduled range by learning actual force start incidents, learning is based on the bulk of data for the concerned instance of time, which requires a longer time period and multiple stages to adjust and give optimality to the desired schedules. Authors in [12] give such a learning mechanism in energy management systems.…”

Section: Motivationmentioning

confidence: 99%

Orchestrating an Effective Formulation to Investigate the Impact of EMSs (Energy Management Systems) for Residential Units Prior to Installation

et al. 2017

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Demand Response (DR) programs under the umbrella of Demand Side Management (DSM) tend to involve end users in optimizing their Power Consumption (PC) patterns and offer financial incentives to shift the load at "low-priced" hours. However, users have their own preferences of anticipating the amount of consumed electricity. While installing an Energy Management System (EMS), the user must be assured that this investment gives optimum comfort of bill savings, as well as appliance utility considering Time of Use (ToU). Moreover, there is a difference between desired load distribution and optimally-scheduled load across a 24-h time frame for lowering electricity bills. This difference in load usage timings, if it is beyond the tolerance level of a user, increases frustration. The comfort level is a highly variable phenomenon. An EMS giving optimum comfort to one user may not be able to provide the same level of satisfaction to another who has different preferences regarding electricity bill savings or appliance utility. Under such a diversity of human behaviors, it is difficult to select an EMS for an individual user. In this work, a numeric performance metric,"User Comfort Level (UCL)" is formulated on the basis of user preferences on cost saving, tolerance in delay regarding use of an appliance and return of investment. The proposed framework (UCL) allows the user to select an EMS optimally that suits his.her preferences well by anticipating electricity bill reduction, tolerable delay in ToU of the appliance and return on investment. Furthermore, an extended literature analysis is conducted demonstrating generic strategies of EMSs. Five major building blocks are discussed and a comparative analysis is presented on the basis of the proposed performance metric.

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Section: Motivationmentioning

confidence: 99%

Orchestrating an Effective Formulation to Investigate the Impact of EMSs (Energy Management Systems) for Residential Units Prior to Installation

et al. 2017

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“…Even the authors declare the algorithm do not need prior knowledge, their algorithm is fit KEM because it uses user knowledge and generate new knowledge by learning. An algorithm for energy management in SH using wireless sensors and artificial intelligence is proposed by Qela and Mouftah (2012). This process is called by authors as Observe, Learn and Adapt (OLA) method.…”

Section: Content Descriptionmentioning

confidence: 99%

Knowledge engineering and management contributions for scientific research in the thermal smart energy context

Preissler¹,

Gonçalves²

2017

Int J Recent Sci Res

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ARTICLE INFO ABSTRACTScientists and researchers have been increasingly studying and seeking alternatives to solve problems related to the power consumption, aiming at environmental conservation and financial savings. In this paper, Smart Energy (SE) is the term used to refer to energy studies in the areas of Smart Grids, Smart Cities, Smart Homes or Buildings. This paper has as a guiding question: how have the scientific studies in Knowledge Engineering and Management (KEM) area contributed to the SE area? The goal of this research is to present a systematic literature review of the scientific research on KEM area in SE context, particularly in thermal heating in Smart Building context, throughout the last 10 years. This systematic literature review, organized in a bibliometric study format uses syntax and content analysis and was conducted in eight stages of research in which were used three known and relevant scientific databases. The present study analyzed 61.662 papers in eight stages. The result was 18 papers strongly related to the scope of this research. The analyses point to a set of researches that mainly use the Elicitation, the Acquisition and the Knowledge Discovery as the most used KEM processes. From the point of view of the use of Information Technology (IT) approaches, it can be highlighted: Machine Learning, Artificial Intelligence algorithms and especially the proposition of their own algorithms. INTRODUCTIONDue to the high levels of CO 2 emissions and to the growth in power consumption it is becoming increasingly important to carry out scientific studies in order to reduce environmental impacts and to contribute to financial savings of final users. In this sense, some approaches and terminologies have been established in the areas of intelligent power consumption such as Smart Grids (SG), Smart Cities (SC), Smart Homes (SH) and Smart Buildings (SB).The term SG is related to the use of Information Technology applied to the management and operation of electrical networks. The term SC has many applications. One of them, in particular, it is the study of consumption and production of sustainable electricity. Finally, SH and SB have the same bias, although being distinct terms, both refer to the study of smart environments such as buildings or houses. The term "smart" is commonly used to equally describe the use of technological innovation and ITs for automation purposes and resource savings, Lund et al. (2012). Such areas of study have many points of convergence. In this study the term Smart Energy (SE), Preissler Junior (2015) is used to refer to research and development initiatives in the cited areas of study. Here, however, the terms are applied to research focusing on energy context. Furthermore, the Knowledge Engineering and Management is an interdisciplinary field which over the past 25 years has been using IT as a tool to "operationalize" the Knowledge Management (KM), Nonaka (2008), Rus and Lindvall (2002). Assuming that the term "smart" is related to the use of IT and considering that IT ...

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“…Qela. B et al [27] introduced an ML algorithm for finding efficient schedule considering a single appliance, i.e., HVAC. In this paper, the authors proposed an algorithm that first observes and learns the timings and user patterns of appliances for a certain amount of time and then schedules it accordingly.…”

Section: Related Workmentioning

confidence: 99%

“…At this sub-time slot, OIA class of appliances is meant to be scheduled. Normally, office timings are 09:00 to 17:00 and so, the user may reach home after 17:00 ±1 h. During this time slot, T3 APP set based upon appliance profiles and UP time is given in Equation (27): Figure 7a anticipates the tariff of this sub-time slot. From 12:00 to 15:00 tariff is 6PKR while from 15:00 to 18:00 the price is 14PKR and then 18PKR per kilowatt for last two hours.…”

Section: Scheduling T3mentioning

confidence: 99%

Realistic Scheduling Mechanism for Smart Homes

et al. 2016

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Abstract:In this work, we propose a Realistic Scheduling Mechanism (RSM) to reduce user frustration and enhance appliance utility by classifying appliances with respective constraints and their time of use effectively. Algorithms are proposed regarding functioning of home appliances. A 24 hour time slot is divided into four logical sub-time slots, each composed of 360 min or 6 h. In these sub-time slots, only desired appliances (with respect to appliance classification) are scheduled to raise appliance utility, restricting power consumption by a dynamically modelled power usage limiter that does not only take the electricity consumer into account but also the electricity supplier. Once appliance, time and power usage limiter modelling is done, we use a nature-inspired heuristic algorithm, Binary Particle Swarm Optimization (BPSO), optimally to form schedules with given constraints representing each sub-time slot. These schedules tend to achieve an equilibrium amongst appliance utility and cost effectiveness. For validation of the proposed RSM, we provide a comparative analysis amongst unscheduled electrical load usage, scheduled directly by BPSO and RSM, reflecting user comfort, which is based upon cost effectiveness and appliance utility.

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Observe, Learn, and Adapt (OLA)—An Algorithm for Energy Management in Smart Homes Using Wireless Sensors and Artificial Intelligence

Cited by 63 publications

References 14 publications

Orchestrating an Effective Formulation to Investigate the Impact of EMSs (Energy Management Systems) for Residential Units Prior to Installation

Orchestrating an Effective Formulation to Investigate the Impact of EMSs (Energy Management Systems) for Residential Units Prior to Installation

Knowledge engineering and management contributions for scientific research in the thermal smart energy context

Realistic Scheduling Mechanism for Smart Homes

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