An Agent Framework for Home Energy Management System

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Proceedings of the World Congress on Engineering 2015 Vol I WCE 2015, July 1 - 3, 2015, London, U.K. An Agent Framework for Home Energy Management System Visit Hirankitti, Member, IAENG  Abstract—A smart home is a promising green technology not only to save people money on their electricity bills but also to alleviate the climate change in a micro-level. In this paper we report on our on-going research project how we are developing a smart home prototype. Here we focus on the essential part of a smart home, i.e. HEMS (Home Energy Management System), by investigating how we develop HEMS using an agent framework. A model of agents for constructing HEMS based on both object-oriented and logic programming approaches is proposed. The agent model is implemented in Python. We also argue for the benefit of combining both approaches into a unified agent framework. Index Terms— Intelligent Agent, Smart Home, Home Energy Management System, Internet of Things. I. INTRODUCTION T he world is moving towards a green technology. A technology that can alleviate the climate change. For the electricity domain, in many countries, their governments are now promoting more environmental-friendly ways to produce electricity while encouraging their people to utilize electricity more efficiently. In this paper we present how we develop a smart home, a green technology that will help a household resident to utilize electricity more efficiently and the home itself can also produce electricity from an alternative source, such as solar or wind energy. The essential part of a smart home is Home Energy Management System (HEMS); this is what we will investigate in great detail in this paper. When we refer to HEMS, we mean a software to operate a smart home by monitoring and controlling all its electrical devices in such a way to use the electricity in an efficient way as well as to make use of the most from its own generated electricity. To operate a smart home, HEMS requires many hardware components, see Fig 1, and the HEMS itself is running on a home gateway. In this paper we shall propose an agent framework for constructing HEMS. This is an on-going research conducted under the PEA Smart Home Project funded by Provincial Electricity Authority (PEA) the major utility company in Thailand. In this project we are developing HEMS, a home Visit Hirankitti is with the Department of Computer Engineering, Faculty of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Ladkrabang, Bangkok 10520, Thailand (phone: +662-329-8341; e-mail: gateway, smart plugs, and home sensors, whilst all home appliances are commercial products to be acquired in. To prove our concepts, we will build the real house with all these devices installed and operating in order to test our HEMS in the real-life situation and to evaluate its merit. The project will be completed in November this year. A lot of research interest now moving towards developing a green technology. How to use energy efficiently in a building and in a home also attracts much attention over recent years. A smart building and smart home with smart energy management system is a trend of recent research. The intelligent agent approach is one of a few promising techniques which has been applied to develop a smart home and smart building. For example, a recent research [1] applied defeasible logic for intelligent control of appliances in a building, such as light bulbs and air conditioners, in order to save energy. For the same purpose [2] adopted BDI agent model and used finite state machine to model agents’ behaviors. Another direction of smart home research was to develop an agent to assist the elderly and disabled [3] by proposing a logical framework based on Event Calculus to model events and actions. For us we consider this research as being related to our previous work on intelligent control of traffic-lights [4], [5] where we proposed an agent approach using rules and logic programming for intelligent control. The remainder of the paper is organized as follows. Section II describes the nature and scope of our smart home and a model of an agent designed based on a smart meter. We then propose our agent framework for HEMS in Section III. In Section IV we point out how our framework can be evaluated. We later discuss related work in section V and conclude this research work in section VI. II. THE PROBLEM DESCRIPTION A. Our Smart Home Hardware Landscape Our smart home will be equipped with many hardware components as seen in Fig 1. Mainly they are divided into two groups. The first group is to design and develop by us in the smart home project, i.e. a home gateway, smart plugs, and home sensors. The other group is commercial off-the-shelf products, i.e. home appliances, mobile devices, a solar rooftop, a wind turbine, and an energy storage. The last piece of the components is a smart meter which we have completely designed and developed in a previous research project also funded by PEA. ISBN: 978-988-19253-4-3 ISSN: 2078-0958 (Print); ISSN: 2078-0966 (Online) WCE 2015 Proceedings of the World Congress on Engineering 2015 Vol I WCE 2015, July 1 - 3, 2015, London, U.K. PEA Smart Home HEMS Hardware ZigBee WiF i ZigBee ZigBee Power Line ZigBee WiF i Solar Roof-top & Energy Storage ZigBee Wind Turbine Power Line Smart Meter Home Gateway WiF i Smart Plug ZigBee In-home Display Fig 1 Our smart home hardware landscape. Home Sensor Next we shall explain what hardware components are doing in our smart home: - Home Gateway HEMS is the software operating the smart home and it is installed and runs on the home gateway, so the home gateway is the central computer to monitor and control other hardware components in the smart home. - Smart Meter The smart meter monitors characteristics of the electricity consumed in the house. It can notify some abnormality of the electricity, such as over voltage/current. Its measurement of home energy usage will be used to calculate a tariff. The meter is so-designed to be able to disconnect or connect loads of the house if necessary that can be controlled by the utility company who supplies the meter. - Smart Plug To allow an ordinary home appliance without any smart feature to be able to control by HEMS, such a home appliance needs to connect to take electricity from a smart plug. Our smart plug is a stripped-down version of our smart meter, so it has ability of advanced metering. The smart plug can be controlled by HEMS to turn on/turn off a home appliance via a wireless communication using either ZigBee or Bluetooth. In addition, the plug is also programmable to be turned on/off automatically according to a preprogrammed schedule uploaded from a smart phone via a ZigBee or Bluetooth communication. - In-home Display Home residents can access to HEMS using the In-home Display. These users will be provided with a mobile application and a web application running on it. The inhome display is a smart phone and a tablet. It can connect to the home gateway either via Wi-Fi or Bluetooth. Once it gets connected to HEMS on the home gateway, the users can manage or configure their home devices, monitor the home activities, and even write a program/script to control these home devices. - Home Sensors ISBN: 978-988-19253-4-3 ISSN: 2078-0958 (Print); ISSN: 2078-0966 (Online) WCE 2015 Proceedings of the World Congress on Engineering 2015 Vol I WCE 2015, July 1 - 3, 2015, London, U.K. A home sensor monitors an ever changing state of the home and also detects an internal or external event of the home. The sensor then feeds this information to HEMS for a control purpose. Each home sensor is equipped with a ZigBee module or a Bluetooth module so that it can be connected wirelessly. The home sensors deployed in our smart home are, for example, pyroelectric infrared sensors and Bluetooth Beacons (sensors) used to detect human movement and position, thermometer sensors used to report temperature, photo sensors used to detect a lighting condition, etc. - Home Appliances They provide functionalities to the residents to serve their personal purposes. The device activities can be monitored and controlled by HEMS wirelessly over Wi-Fi, ZigBee, or Bluetooth. The way to control each home appliance could be as simple as turning on/off it (using a smart plug) or an advanced control depending on how smart that kind of home appliance is. - Solar Roof-Top and Wind Turbine They are the power generators of the smart home. The two devices are autonomous, they can work on their own and keep charging electricity into the home energy storage. - Energy Storage All the electricity generated by the two generators is stored in the energy storage, a kind of a long-lasting rechargeable battery. The current it directly supplies is DC, so it needs a DC-to-AC inverter to convert that into AC to be able to supply to the loads in the home. HEMS can decide and control when the home should get the electricity from the energy storage and/or otherwise from the source from PEA by only controlling another smart meter equipped with the energy storage. Essentially, this smart meter will connect/disconnect the electricity supply from the energy storage as well as can measure how much energy being supplied by the energy storage to the home. B. A Lesson Learned from Smart Meters In 2012 KMITL was funded with one and half year research project by PEA to design and develop industrial prototypes of (single-phase and poly-phase) AMI smart meters. According our complete industrial smart meter prototypes, we think they are complicated enough and can serve well as intelligent agents, so we apply an objectoriented approach to model them as follows. An Agent: A Smart Meter - Mater State State Variables represent a meter state, e.g. time, current voltage, real-time current, power factor, etc. State Updater, the measurement module, keeps updating the state variables continuously. A real-time clock keeps an accurate time. Historical Record of the State stores historical data of these State Variables. - Events Event data is notified by an event listener (or a sensor), e.g. removal of meter cover will trigger a pedal switch which in turn creates an anti-tampering event. The event could be internal, e.g. an event of over current, or external, an event of anti-tampering. Historical Record of the Events stores historical data of these emerging events. - Events–Response Rules The smart meter has simple Event–Response Rules, e.g. if the meter cover is removed it will record this event and notify the utility company; if a power outage happens it will send the current meter status to the utility company and shut down the meter (the meter has a power back-up battery, so it can operate for a short period of time after a power outage happens). When an event gets notified, an event handler will fire the appropriate rule and execute the corresponding Response action. - Operational and Scheduled Tasks This is the task the meter always operates as its main task, e.g. instant reading and displaying of various metering data. Some tasks are performed in a timely manner (i.e. according to a pre-defined schedule), e.g. storing a snap-shot of metering data on every 15 minutes into the meter load profile. - Provided Services These are services to serve the outside world. They are stored procedures to be executed once a request is issued from the outside and the meter responses to the request by executing the matched procedure. - Communication Protocols Our smart meter deploys many communication protocols, i.e. HDLC, ZigBee Pro, G3-PLC (Power Line Carrier), and DLMS/COSEM. A smart meter is connected via some communication ports, i.e. serial ports. Fig 2 One of our completed smart meter prototypes and its structure. From what we learned from a smart meter, we can use it as a basis to design a general framework of an intelligent agent for HEMS. III. AN AGENT FRAMEWORK A. A Generic Agent An agent in our design is in a form of an object-oriented class. In this case State Variables, State Historical Records, Events, Event Historical Records, and Event–Response Rules are predefined data attributes, while the State Updaters, Event Listeners, and Event Handlers are methods to manipulate on these data attributes. For the Operational ISBN: 978-988-19253-4-3 ISSN: 2078-0958 (Print); ISSN: 2078-0966 (Online) WCE 2015 Proceedings of the World Congress on Engineering 2015 Vol I WCE 2015, July 1 - 3, 2015, London, U.K. and Scheduled Tasks, Provided Services, and Communication Protocols, they can also be implemented by methods in OOP. To make it more extensive we add Goals, Constraints, and Policies which are high-level commitment an agent wants to achieve at the global level. A Generic Agent - State State Variables State Updater Historical Record of the State - Events Event listeners Historical Record of the Events - Events–Response Rules Event handlers - Operational and Scheduled Tasks - Provided Services - Communication Protocols - Ports - Goals - Constraints - Policies When an agent operates, it has to perform many processes for the following tasks simultaneously, these tasks are: timer task continuous state reading, updating, recording of the historical data event listening, recording, and event handling routine tasks scheduled tasks waiting to run a service task in response to a request solving the pre-defined goals to satisfy the constraints and policies For a low-level embedded-system agent, like a smart meter, this multi-tasking jobs can be implemented in C with a support by an RTOS (Real-Time OS), but for a high-level agent, we can employ multi-threading programming supported by the software and OS platform; and for our agent implementation Python copes with this quite well. The essential part of this agent framework is how the framework deals with events together with event handling, and similarly how to deal with goals, constraints, and policies; both of these two issues certainly involve some form of logical reasoning. Due to the limited length of this paper, we shall reveal only the former and leave out the latter to be investigated elsewhere. To cope with logical reasoning in this agent framework, the framework needs to accommodate logic. In the next section we will explain how logic can be merged into our object-oriented framework. B. Enhancing Object-Oriented based Agent with Logic Objects and classes are the central concepts behind an object-oriented approach, and this is not so different in logic where objects (including persons), together with their properties and their relationships are expressed in terms of logical sentences; and these sentences are being reasoned with using logical inferences. Apart from objects, other formalism, like numbers, mathematical expressions, and functions, both objectoriented approach and logic all have these in common. However, what are missing from an object-oriented approach are predicates (predicate symbols to be precise) which describe objects’ properties and relations between objects. Provided with objects, numbers, variables, predicate symbols, function symbols, and logical connectives, one can form a logical sentence. Taking an object-oriented approach for granted, we can enhance it with logic by introducing predicates and logical sentences, in terms of facts and rules (similar to those in logic programming). We could add a new syntax in an object-oriented language, such as Python the following: preddef father(X:Human,Y:Human): Rule(father(X,Y) if male(X) and parent(X,Y)) This is a definition of predicate father which takes two object variables X and Y, both belong to class Human (a variable name begins with a capital letter). A predicate definition is so-designed that it can be asserted with more rule later, for example class Human(object): def __init__(self,objname): self.__name = objname john = Human(“john”) mary = Human(“mary”) father.assert(Rule(father(john,mary)if True)) The predicate definition is added with a fact, a rule with a body True. In addition this predicate can be proved as a query by a method solve, which returns a set of answers. father(A,B).solve() > {father(john,mary)} The rule we employ in our agent framework serves for two purposes, one is for defining a predicate which is in a form of a Horn-clause, and the other is for expressing an Event– Response rule, which is in a form of a Conditional Statement. An Event–Response rule is in the form: if , where is a sequence of program statements and is a conjunction of logical atomic sentences. For example, send_message(M,“The cover is opened.”,pea) if unauthorized_at(M,T1) and meter_cover_is_opened(M,T2) and T2 >= T1. An event is expressed by an atomic predicate with at least one argument specifying the time stamp to signify when the event occurs. The event will be raised to be observed by the event listener which will then hand it to the event-handler to ISBN: 978-988-19253-4-3 ISSN: 2078-0958 (Print); ISSN: 2078-0966 (Online) WCE 2015 Proceedings of the World Congress on Engineering 2015 Vol I WCE 2015, July 1 - 3, 2015, London, U.K. response with some matched Event–Response rules being fired. Events, rules, and predicates are data attributes of an agent in our framework; and they are treated as objects at the meta level and they will be handled by their associated methods accordingly. In this context we allow objects, object variables, functions and methods of an objected-oriented approach to be used in a logical context and conversely a logical sentence to be used in the context of object orientation quite naturally. C. A Smart Home Agent Considering a smart home, or HEMS to be precise, as an with probable Meniere’s were selected among patients referred to the outpatient clinic of Amir-Aalam hospital between the years 2012 and 2015. Ninety-one healthy controls were selected from normal healthy subjects from the same region. The control group underwent history analysis and a physical exam to confirm their health status. We matched our control group for sex and age with both groups. Audiological, vestibular, and functional status in each patient was evaluated based on the American Academy of OtolaryngologyHead and Neck Surgery guidelines (14). The diagnosis was confirmed in all patients by ruling out the presence of anatomic lesions using magnetic resonance imaging (MRI). All the participants were requested to sign written informed consent. Patients didn’t pay for the complementary tests and their identity remained hidden throughout the study. HLA typing After taking 5cc of blood from each patient, samples were collected in EDTA tubes with anticoagulating agent stored at 20*C. The DNA was extracted using salting out method and HLA-Cw typing was performed using sequence specific primers (SSP) polymerase chain reaction [DynalAllset+TM SSP Kit] (15). Statistical analysis HLA-Cw Allele frequencies were calculated for each group and were compared using the pearson’s chi-square test and fischer exact test. The significance of association between HLA-Cw Allele and Meniere’s disease was estimated by odds ratio (OR) and 95% confidence intervals (95% CI) using the stata software (version8). P values less than 0.05 were considered to be statistically significant. 262 Iranian Journal of Otorhinolaryngology, Vol.28(4), Serial No.87, Jul 2016 HLA-CW Alleles in Meniere disease Results The clinical features of the patients are shown in table 1. HLA-Cw allele frequencies were determined in all patients. The distributions of HLA-Cw frequencies were compared among Table1: Clinical characteristics of the patients. Sex (M/F) Mean age Onset time (Years) Unilateral/bilateral Level of Mild hearing Moderate Severe Total number of patients *Meniere’s Disease Definite MD* 13/10 34.96±13.047 (13-71) 3.74±3.107 (1-12) 21/1 13 8 2 23 different groups. There was a statistically significant difference in the frequency of HLA-Cw*04 in patients with definite or probable Meniere’s disease (18%) and healthy controls (1%) (P=0.001) (Table.2). Probable MD 17/7 41.04±16.815 (15-76) 7.63±7.033 (1-24) 19/5 9 5 9 24 Control 51/40 38.53±10.513(14-78) N/A N/A N/A N/A N/A 91 Table 2: Distribution of HLA-Cw Alleles in patients and controls. HLA-C MD* patients (n=47) (%) # Control group (n=91) (%) # P value allele OR (CI 95%) Cw*01 Cw*02 Cw*03 Cw*04 Cw*06 Cw*07 Cw*08 Cw*12 Cw*13 Cw*14 Cw*15 Cw*16 Cw*17 Cw*18 Total *Meniere’s disease 2 (2.1%) 0 (0%) 2 (2.1%) 17 (18.1%) 10 (10.6%) 13 (13.8%) 4(4.3%) 8(19.1%) 0 (0%) 7(7.4%) 1(1.1%) 17(18.1%) 3(3.2%) 0(0%) 94(100%) 7 (3.8%) 7 (3.8%) 9 (4.9%) 2 (1.1%) 33 (18.1%) 25 (13.7%) 4(2.2%) 43(23.6%) 3(1.6%) 17(9.3%) 0(0%) 9(4.9%) 7(3.8%) 16(8.8%) 182(100%) 0.72 0.09 0.34 <0.0001 0.10 0.98 0.45 0.39 0.55 0.59 0.34 <0.0001 1.00 0.002 0.543(0.111-2.669) 0.418(0.88-1.975) 19.870(4.481-88.101) 0.538(0.252-1.145) 1.008(0.490-2.074) 1.978(0.483-8.092) 0.766(0.413-1.419) 0.781(0.312-1.955) 4.244(1.811-9.943) 0.824(0.208-3.263) HLA-Cw*16 allele frequency was also significantly higher in patients with definite or probable Meniere’s disease compared to the controls. [p: 0.0001, OR: 4.244, 95% CI(1.81-9.94)] (Table.2). On the other hand, HLA-Cw *18 allele frequency was significantly decreased in patients compared to the control group (P=0.002) (Table. 2). Frequencies of HLA-Cw alleles showed significant differences between probable Meniere’s disease and definite Meniere’s disease group. There was an increase in the frequency of HLA-Cw*12 in probable Meniere’s disease group compared to definite Meniere’s disease group (P=0.046, OR; 0.328, 95% CI (0.107- 1.012)] (Table. 3). Iranian Journal of Otorhinolaryngology, Vol.28(4), Serial No.87, Jul 2016 263 Dabiri S, et al Table 3: Distribution of HLA-Cw Alleles in definite MD, probable MD patients, and controls. HLA-C allele Definite MD* patients (n=23) (%)# Probable MD* patients (n=24) (%)# Control group (n=91) (%) # P value (Definite vs. probable) P value (Definit vs. Control) P value (probable vs. Control) Cw*01 Cw*02 Cw*03 Cw*04 2 (4.3%) 0(0%) 1 (2.2%) 8(17.4%) 0(0%) 0(0%) 1(2.1%) 9(18.8%) 7 (3.8%) 7 (3.8%) 9 (4.9%) 2 (1.1%) 0.23 N/A >0.99ǂ 0.86 >0.99 0.35ǂ 0.69ǂ 10-4 0.35 0.35 0.69ǂ 10-4 Cw*06 7(15.2%) 3(6.3%) 33 (18.1%) 0.15 0.64 0.46 Cw*07 Cw*08 8(17.4%) 0(0%) 5(10.4%) 4(8.3%) 25 (13.7%) 4(2.2%) 0.32 0.11ǂ 0.52 0.58ǂ 0.54 0.06ǂ Cw*12 Cw*13 5(10.9%) 0(0%) 13(27.1%) 0(0%) 43(23.6%) 3(1.6%) 0.04 N/A 0.05 >0.99ǂ 0.62 >0.99ǂ Cw*14 Cw*15 3(6.5%) 1(2.2%) 4(8.3%) 0(0%) 17(9.3%) 0(0%) >0.99 0.48ǂ 0.77 0.48ǂ >0.99 N/A Cw*16 Cw*17 Cw*18 9(19.6%) 2(4.3%) 0(0%) 8(16.7%) 1(2.1%) 0(0%) 9(4.9%) 7(3.8%) 16(8.8%) 0.71 0.61ǂ N/A 0.001 >0.99ǂ 0.04ǂ >0.99 0.02ǂ 0.06ǂ Total #46(100%) #48(100%) #182(100%) *Meniere’s Disease, ǂ analyzed with fisher exact test, others are analyzed using chi square #The number of alleles is twice the number of patients, as each person has two alleles—one maternal and one paternal. Frequencies of HLA-Cw*04 (P<0.001) and HLA-Cw*16 (P=0.001) were found to be significantly higher in definite Meniere’s disease patients compared to the control subjects. Though, the allele frequency of HLA-Cw*18 was significantly lower in definite Meniere’s disease compared to the controls (P= 0.047) (Table. 3). Discussion The frequency of HLA-Cw alleles in 3 groups of definite Meniere’s disease patients, probable Meniere’s disease patients, and healthy controls has been appraised in this study. A significant association between HLA-Cw*04 and HLA-Cw*16 alleles with both probable and definite Meniere’s disease was observed in our patients. This was in agreement with the study of Koyama and colleagues in which they found significant association between HLA-Cw4 and HLADRB1*1602 in a Japanese sample. (16) Khorsandi et al. also found that HLA-Cw4 is found more in Iranian definite Meniere’s disease patients (13). The frequency of HLA-Cw*18 allele was significantly higher in the control group. HLA-Cw*12 allele frequency was significantly higher in probable Meniere’s disease group compared to the patients with definite Meniere’s. The observed association of HLA-Cw alleles in our patients suggested an autoimmune mechanism in both definite and probable Meniere’s disease. This is in agreement with Greco’s study who discussed Meniere’s autoimmunity in literature (4). A mechanism based approach to the disease diagnosis and treatment warrants more accurate diagnostic methods and more straightforward and powerful treatment approaches. Earlier studies have identified an association between the HLA-Cw7 antigen and Meniere’s disease in a British population (17). A significant increase in the frequency of HLA-Cw7 alleles in patients with Meniere’s disease was observed compared to other inner ear diseases and healthy subjects (18). Some studies have found an association between HLA-DRB1 alleles and Meniere’s disease. However the results had not been repeated in other populations (19,20). Yeo et al. have found that frequency of HLA-Cw*0303 and HLA-Cw*0602 alleles 264 Iranian Journal of Otorhinolaryngology, Vol.28(4), Serial No.87, Jul 2016 HLA-CW Alleles in Meniere disease were significantly increased in Korean patients with MD compared to healthy controls; on the other hand, HLA-B44 and HLA-Cw*0102 were significantly decreased in their patients (19). This can support the idea of the association between probable Meniere’s disease and the HLA Alleles. In another study on 80 Mediterranean patients with bilateral Meniere’s disease, an association between HLA-DRB1 *1101 and bilateral Meniere’s disease was observed; but there was no association between HLACw and bilateral Meniere’s disease (20). The number of patients with bilateral Meniere’s disease was very low in our studied population. The discrepancies in results are probably due to the ethnic or geographic background or also might be as a result of differences in phenotypic or clinical manifestations of patients in different studies which require careful interpretation. To the best of our knowledge, this is the first study which compares HLA-Cw allele frequencies in definite Meniere’s disease and probable Meniere’s disease patients in an Iranian sample. Some HLA loci are inherited as haplotype blocks. Therefore the association found between HLA-Cw and Meniere’s disease in our study might be due to an association with another unknown gene in linkage with the 75–85. 4. Bachmann F (1994) Molecular aspects of plasminogen, plasminogen activators and plasmin. In: Bloom AL, Forbes CD, Thomas DP, Tuddenham EGD, eds. Haemostasis and thrombosis. U.K.: Chuchill Livingston. pp 525–600. 5. Castellino FJ, Powell JR (1981) Human plasminogen. Methods Enzymol 80 Pt C: 365–378. 6. 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