Presentation is loading. Please wait.

Presentation is loading. Please wait.

Towards a Smart Home Framework Moody Alam Agents, Interaction & Complexity (AIC) Group, School of Electronics and Computer Science, University of Southampton.

Similar presentations


Presentation on theme: "Towards a Smart Home Framework Moody Alam Agents, Interaction & Complexity (AIC) Group, School of Electronics and Computer Science, University of Southampton."— Presentation transcript:

1 Towards a Smart Home Framework Moody Alam Agents, Interaction & Complexity (AIC) Group, School of Electronics and Computer Science, University of Southampton

2 What is a smart home? The Future Home, The Jetsons, 1962 Home automation Sensors Connected devices Visions from the past The Present Future Vision! Future Homes, 1969 [A robot serving beer!] The Smart Home, The Jetsons, 1962 Future Homes, 1969 [A robot serving beer!] Green + Wired

3 What is a smart home? No agreed definition! The IBM’s vision: 1.Instrumented 2.Interconnected 3.Intelligent

4 Why is the Smart Home important? 5.38 Million Smart Homes by 2015 [Berg Insight] Renewable energy  electricity  gas  water, etc. home comfort  lights  home automation  Zero Carbon Homes Energy management home care  chronicle diseases  presence  home hospitalization

5 Smart home is an active research area Academia All top 10 Engi. & Tech Universities [Times higher Education]. Caltech, MIT, Princeton, Cali-Berkeley, Southampton* Industry Governments – US, UK, Aus, Canada, China, EU, Hundreds of companies- Microsoft, IBM, British Gas.. Numerous sub-domains: home automation, energy conservation, elderly living. We are interested in those sub-domains which require developing a software model of smart home.

6 Typical workflow in such domains Form a Hypothesis Build a Model Simulate /Optimise Analyse Results Modify/ Conclude Hypothesis

7 Typical workflow in such domains Form a Hypothesis Build a Model Simulate /Optimise Analyse Results Modify/ Conclude Hypothesis Battery reduces cost Matlab / Java code Minimise cost given battery Compare costs True / False

8 Typical workflow in such domains Form a Hypothesis Build a Model Simulate /Optimise Analyse Results Modify/ Conclude Hypothesis Battery reduces cost Matlab / Java code Minimise cost given battery Compare costs True / False

9 What is the problem? Problem: These three phases (modelling, simulation and analysis) take up the most time. Solution: We propose our Smart Home Framework to speed up these phases. We are not the only smart people to have realised this problem! Industry has the proprietary software toolkits. – Cost and Licenses! – Platform-dependency! – Limited interoperability between platforms. – Focused on the company’s business. Academia has very few open-source toolkits: – Focused on narrow research issues – Models are not general and thus not extendable in other related domain

10 Why is SH Framework a good idea? Open-source and free of cost! SHF has three core components each focused on a single phase: – Model Classes  Model building phase – Optimiser  Optimisation / Simulation phase – Visualiser  Analyse Results

11 Smart Home Framework Building a Model Optimisation Analysis

12 Smart Home Framework Building a Model Optimisation Analysis

13 SHF: Model Classes: Overview We take a bottom-up modelling approach: – Smart Home is made of different components (e.g. appliances and storage). – We provide general models for these components. – These components can be integrated to create a smart home. This general model of a smart home: – Has an understanding of its components and how are they related – Can be extended to specific models These smart homes can be connected together to form a smart community.

14 Modelling a smart home A collection of: – Appliances – Generators – Storage – Electric Vehicle Relationships: – Between all above – Grid (Tariff) – Other Smart homes Appliances EVs Storage Generation Grid

15 SH Framework contains – Interfaces – Abstract classes – And Implementation of abstract classes To model – Generation – Storage – Appliances – Appliances’ Use Modelling a smart home

16 SHF: Modelling Generation & Storage Modelling Generation Sources – Microgeneration (e.g. Solar Panels / Wind Turbine) – Grid Modelling Storage Facilities – Electric Batteries – EV Batteries

17 SHF: Appliances and their usage Support to model appliances (i.e. Loads): – SHF already have implementation of common home devices (e.g. TV, Oven) – Abstract classes to include new appliances Modelling appliances’ usage (i.e. Load Events): – Deferrable and Non-Deferrable – Interruptible and Non-Interruptable – Critical – Baseload – Combination of above (e.g. a deferrable interruptible critical load event)

18 SHF: Modelling implicit understanding of devices and their relationships Consumption + Battery Charging = Generation Battery has a limited number of charging cycles. EV battery is available only certain times a day.

19 Modelling is easy: Code Snippets Adding renewable generation and/or grid is easy: – agent.addEnergySource(new SolarPanel(1.5kW)); – agent.addEnergySource(new WindTurbine(2kW)); – agent.addEnergySource(new Grid(tariff)); Creating appliances and Load Events: – TV tv = new TV(0.3kW) – agent.addEvent(new onDeferrableLoadEvent(tv,start,end); Adding storage – agent.addStorage(new Battery( 2kWh, 0.5kW, 10%loss));

20 Smart Home Framework Building a Model Optimisation Analysis

21 SHF: Optimisation in a smart home Optimisation depends on the structure or formation of your smart home model: – Generally speaking, you may be solving a convex or non-convex problem to answer your research question. – Your choice of optimiser will depend on the structure of your problem. SHF architecture allows you to plug-in any optimiser of your choice!

22 SHF comes with a default optimiser IBM’s CPLEX Optimiser is available as the default plug-in optimiser: – Free of cost to academia. – Supports LP, MIP and Convex optimisation – Catch: License needed for commercial use. So if your optimisation problem falls under LP, IP, MIP or certain convex subclasses, then you can use the default optimiser! This optimiser is sufficient for the common optimisation problems. For advanced and complex optimisation problems (e.g. non-convex) you can just plug-in a general solver of your choice.

23 SHF and IBM CPLEX An optimisation problem can be expressed as a: – Model (variables, and constraints.) – Objective function SHF already have a smart home CPLEX model (Java code). Commonly used objective functions are already implemented, e.g. – Maximise Preference, Minimise Cost/Carbon If your objective function is not already implemented, you can just write a new objective function and use the existing CPLEX home model!

24 Smart Home Framework Building a Model Optimisation Analysis

25 SHF: Analysing results SHF comes with a visualiser. Code is there to visualise common devices / events in a smart home. – Plots for generation, consumption, battery usage Visualiser is extendible, easy to include new plots etc. Results available in XML, CSV formats

26 Smart Home Demo: Modelling, Optimisation and Analysis

27 Beyond a single smart home: Smart communities The framework has all the building blocks to create a community of connected homes. A small community be readily modelled to test different communal aspects: – Energy Exchange – Electric vehicle charging – Battery Usage minimisation – Coalition formation for group buying

28 Smart Community Demo: Reducing the battery usage through energy exchange

29 Questions??

30 Thank you!


Download ppt "Towards a Smart Home Framework Moody Alam Agents, Interaction & Complexity (AIC) Group, School of Electronics and Computer Science, University of Southampton."

Similar presentations


Ads by Google