Predictive Simulation & Big Data Analytics ISD Analytics

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1 Predictive Simulation & Big Data Analytics ISD Analytics

2 Overview Simulation can play a vital role in the emerging $billion field of Big Data analytics to support Government policy and business strategy decisions Overview How simulation plays a key part in the Big Data Predictive Analytics process Introduce Simulait simulation-based consumer analytics platform Case studies: water, energy, emergence response, retail, transport Simulait Online simulation in the cloud for on-demand access and large scale simulations

3 Data Analytics & Decision Process Past Future Observe Descriptive Analytics Predict Predictive Analytics Influence Prescriptive Analytics Business Questions: What happened? Why did it happen? What is happening? Why is it happening? Solutions: Data mining & forensics Real-time analytics & mining Market segmentation Reporting & dashboards Ad-hoc database queries Business Questions: What is likely to happen? Solutions: Simulation Statistics & linear regression Predictive data-mining Forecasting & trend reporting Business Questions: What should I do about it? How do I influence the future? What are the consequences? Solutions: Simulation Optimisation Less data, greater insight, greater value * Based on Gartner s model of analytics

4 Projection vs Prediction Traditional statistical approaches project future behaviour by extrapolating past behaviour Observe and forecast what people do but not why they do it Unable to effectively represent complex consumer behavior Limited functionality unable to address a broad range of business problems Past demand is not always a good predictor of the future Influence future sales by testing strategies with Simulait Total Sales Changing population & consumer trends

5 SimulAIt An Analogy SimulAIt is a real life SimCity application where businesses or Government can predict and test strategies to influence the behaviour of large populations Diverse domains: water, energy, emergency response, retail, transport,... Diverse applications: strategy, policy, pricing, demand forecasting, marketing, community behaviour and social planning, new product uptake, etc... Global applicability: Australia, Europe, USA Cloud solution: SimulAIt Online can be accessed on-demand using a web browser

6 Simulait: A Truly Predictive Approach Accurate: proven approach, demonstrated over 95% accuracy Model not built on past demand data demand data used to validate the model Accuracy due to greater representation of a broad range of consumer factors Benefits are more than accuracy it s the scenarios that you can test with it!!

7 Simulait Architecture

8 Case Study 1: Victorian Water Utilities Objectives Isolate and quantify the effectiveness of past water conservation strategies economic, regulatory, social (communications) & environmental Forecast bounce-back in water demand from easing restrictions & price increases Assess impact of product uptake on demand and revenue Build a business case to industry regulators pricing review Build demographic demand profiles Blind validation: Used 4 yrs of demand data to calibrate outdoor water use and then forecast next 6 years of demand without access to actual demand data

9 Case Study 1: Victorian Water Utilities Blind validation results 35 Average monthly household water consumption Water consumption Simulated Actual-calibration data 5 Actual - blind validation data 0 Jul-00 Jul-01 Jul-02 Jul-03 Jul-04 Jul-05 Jul-06 Jul-07 Jul-08 Jul-09 Jul-10

10 Case Study 1: Victorian Water Utilities Key outcomes and benefits Informed capital expenditure, corporate plans, water restriction schedules Rigorous business case to industry regulators to maximise product price and revenue Isolated and quantified the effectiveness of past & future strategies (campaign analysis) Informed & increased ROI on future strategies

11 Case Study 2: Water in USA & France Key outcomes and benefits Model transferable to different countries Better for long term forecasting tendering, strategic & financial planning, design future cities, etc... Support water conservation, regulation, new water rates, impact of recession, etc... Calibration point >90% Accuracy

12 Case Study 3: Rebates/Retail Objective Identify a mix of products and prices for the water rebates program that maximises efficiency and keeps within the program budget Three projects, and now a 3 year license to 2015 Approach Simulated 2 million households, 4.5 million consumers Incorporated consumer preference and affordability, and product age, failure and price Simulated product uptake and efficiency with different prices Key outcomes and benefits Accurate predictions of product up-take and budget spend Prevented budgets blow-outs Cost/benefit (triple bottom line) analysis of different strategies Forecast the ROI of different demographics and regions, and to assist with targeted (micro)-marketing of the rebate program

13 Case Study 4: Energy Customer Personalization Using 1% of CRM data in the first 6 months, Simulait was able to accurately predict what each specific customer will do, and why, for the next 2 years!!! Energy load forecasting accuracy Total Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec % 99.2% 97.9% 98.8% 98.0% 95.0% 98.5% 99.6% 97.0% 99.6% 98.7% 96.5% 85.0% % 96.7% 99.3% 99.3% 99.0% 98.9% 98.4% 98.8% 95.1% 97.3% 93.1% 98.6% 98.3% % 91.9% 97.9% 97.1% 97.6% 98.6% 98.1% 99.1% 97.1% 87.8% Calibration Prediction Actual Forecast

14 Case Study 5: Energy - EV Uptake & Transport Objective Predict the uptake of Electric Vehicles over time to 2040 Predict usage and charging behaviour of electric vehicles Impact on the electricity network (extra peak load) to support reliability and quality risk management

15 Case Study 5: Energy - EV Uptake & Transport Approach EV Uptake consumer decision model Simulated the new and used vehicle market across Australia Considers many dynamic factors: consumer type, petrol and elec price, car range, charge times, charge infrastructure, upfront price, ongoing costs, dwelling suitability, battery replacement, depreciation, market penetration, etc... EV usage: transport/activity model Model each consumer s daily activities and transport/vehicle use Factors include: consumer type (e.g. occupation, family structure), day of week, number of vehicles in the household, activity types (work, school, shopping, entertainment, family/social visits, etc...) Other factors: passenger trips, infant trips to carers if both parents working, separate household activities for independents, vacation from work (e.g. for parents during school holidays), etc... EV charging and increase in peak demand Charge times and location: home, work, fuel station, shopping centre, etc.. Other complex factors: power point upgrades, vehicle-to-grid system

16 Case Study 6: Emergency response - bushfire Following the 2009 bushfires that claimed 173 lives, the Victorian Royal Commission identified that...strategies must reflect how people actually behave... Timely and accurate warnings can provide triggers, but the content and delivery must be carefully developed to elicit the right response

17 Case Study 6: Emergency response - bushfire Objective Model community behaviour to bushfires and warnings to support bushfire strategy and policy, and ultimately save lives The model predicts: What people do and when: Stay, leave or wait and see Where will people go: neighbours, designated shelter, leave region, open area

18 Case Study 6: Emergency response - bushfire Approach Based on a health model of behaviour change individual s life is at risk Potential to be applied to support health policy and manage the unsustainably increasing health costs Given where people are, who they are, what they are observing, the warnings they are receiving (and which mediums, e.g. radio, text, etc.), and the progression of the bushfire, we determine the level of threat, vulnerability and uncertainty for each individual/family, and thus response Level of motivation to act Wait Wait Stay Leave Decision threshold Decision threshold Perceived Threat Wait Wait Wait Wait Perceived Vulnerability

19 Case Study 6: Emergency response - bushfire Validation & outcomes Applied the model to two bushfires in Victoria and South Australia and demonstrated >90% accuracy Currently assessing hypothetical bushfire scenarios to support bushfire policy and strategy Can be applied in emergency response situations beyond bushfires...

20 SimulAIt Online (SOL) Access SimulAIt via a web browser SimulAIt Online allows: Access validated models online Add many users Create multiple scenarios test assumptions and what-if analysis Share scenarios (models), results, notes and descriptions Refresh data and configure assumptions, parameters, etc... Run simulations Download results disaggregated via region and time or other factor Benefits On-demand access to models, for technical and non-technical users Control, visibility, ease of use Facilitates collaboration and consistency: share scenarios and results Maximise ROI: execute many scenarios when required Hosted solution: no installation of software or hardware required to run large scale simulations

21 Case Study 7: Vic ESC & Retailers Challenge Limited availability of suitable data and forecasting models presents a challenge for regional water retailers to produce accurate forecasts for their pricing review Approach Team members collaboratively used SOL to create validated models with minimal data Key outcomes and benefits SOL enabled team members to access, configure, validate, and share models and forecast results Demand forecasts were used to support their pricing review

22 Summary Simulation can add significant value to support strategic decision making and policy for Government and business Unique and important role to play in Big Data Provide the right information to make better decisions: predict and how to influence Simulait is a practical approach for problems involving consumers and populations: i.e. human behaviour High level of accuracy and functionality Demonstrated in various domains and countries with minimal configuration Simulait Online web/cloud based solution provides on demand access for users globally Collaborative tool: access, share, run scenarios, and download results Access to limitless computing power to run large scale scenarios

23 Questions? ISD Analytics 27 Chesser Street, Adelaide, South Australia, 5000 Phone: www. isdanalytics.com

24 SOL Technical Overview SimulAIt Online (SOL) SimulAIt Hosting Centre Web User Interface CPU On Demand SOL Server Application Users Configure scenarios View/compare results Internet Scenarios & Results SimulAIt Platform and Models (SPM) Dynamic MultiDimensional Database SimulAIt Micro-Simulation Engine Models Domain Specific Models Models Models (water, energy, retail, f inance,...) Census Data Rules, behaviors, logic, reasoning,... Population Dynamics New/Updated Models Data utilised: Market research & social data Econometric & statistical data Engineering and environmental data Customer data (billing, purchases...)

25 Main SOL screen Scenario menu items Session messages Admin menu items User & logout Model type Session message log Scenario groups Scenarios Active scenario Working pane

26 Scenario: Configuration Time associated with parameter values Config input type Parameters tree: hierarchical to reduce complexity Slide to increase working pane Time explicit parameter values (cells) Scroll cells through time

27 Run scenario SimulAIt! Start simulating the scenario Set the scenario start and end times Region tree Selected regions Save the selected regions for the scenario

28 Scenario: Results Range of results to download: Water, energy, carbon, revenue, etc. Monthly, yearly Disaggregated into different regions, appliances

29 Outputs: Monthly Demand

30 Outputs: Yearly Demand

31 Outputs: Household Usage

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