Domestic Energy Usage One Size Fits None. February 2014

Transcription

1 Domestic Energy Usage One Size Fits None February 2014

2 Outline Brief introduction to ONZO Customer segmentation in the UK now Exploratory research into spatial variation using smart meter data Customer understanding in the UK future Challenges 2

3 Introducing ONZO ONZO writes and licenses algorithms that derive customer insight from energy use data ONZO can write algorithms for specific use cases ONZO runs pilots to demonstrate the value that can be derived from data ONZO have a uniquely large database of high resolution energy data ONZO no longer Makes energy displays Provides a consumer web portal 3

4 Current state of UK domestic market segmentation 2 segments Standard and Economy 7 12 archetypes recent CSE study commissioned by OFGEM Economy 7 Standard Source: Tariff type variation in the domestic market, DECC, December 2012 Source: Beyond Average Consumption Development of a framework for assessing impacts of policy proposals on different consumer groups CSE, November

5 What can we infer from this data on spatial variation of Economy 7 use? Data from storage heater manufacturers indicates 11% homes using storage heating, more further North or in rural areas. Does this data agree? Source: Tariff type variation in the domestic market, DECC, December 2012 Do customers have the right information? Are utilities using the data they have? 5

6 CSE study - Beyond Average Consumption - diversity analysis, which the industry needs Aim: assess impact of policy proposals on different consumer groups Used ONS Living Costs and Food survey. Identified predictors for energy use: Non-gas heated households split by fuel type and income Gas heated households split by tenure, dwelling type and income Comparison with annual consumption figures, location data, stickiness, and other demographics then provides richer descriptions of each archetype. E.g.: Archetype 4: non-gas heated, non-elec heated, higher income = Wealthy, market-savvy families in rural detached properties Archetype 5: Gas heated, social rented flats, lowest income = Low income, out-of-work single adults in small 1 bed social rented flats in London What are the possibilities for smart meter data analysis? 6

7 Exploratory research into spatial variation using smart meter data The following slides present examples from various data sets: By climate By country By area within country By rural/urban 7

8 Utilities often serve more than one climate this dominates spatial variation of energy use Odessa Dallas Houston Corpus Christi 8

9 Utilities often serve more than one climate this dominates spatial variation of energy use 9

10 What are the implications of the UK s mild climate? Different Demand response Impact on spatial variation analysis Need for finer segmentation Common Grid management Customer management 10

11 % of customers who have turned on electrical heating Example: UK analysis the onset of winter heating in Scotland and England - Scotland - England What might this prove? Possible impacts: Scheduling of winter fuel payments Targetting of campaigns like The Earlier the Better Energy efficiency programs Need to combine with relevant data about the customer to get most impact 11

12 Example: UK analysis rural vs urban We have smart meter energy use data tagged with location Compare: - Urban area, lowest consumers - Urban area, highest consumers - Rural area, lowest consumers - Rural area, highest consumers This will show the variation 12

13 Example: UK analysis rural vs urban actual data Is this what we d expect to see? What does this data tell us? 13

14 We need to understand more about what is happening in the home The conversion of infrastructure from analogue to digital transformed these industries 14

15 The utility industry needs to emulate dunnhumby s approach to data Diversification Clothing Consumer electronics Fuel Garden centres Insurance Banking 15

16 So, how many segments should we have? Not a well posed question Depends on purpose Tags can be more appropriate than segments 16

17 Smart meter data analysis can extend segmentation to personalisation. Why personalise? Existing behaviour change tools deliver small reductions in energy use - they are blunt instruments, treating everyone the same Existing energy use reduction programs have low uptake - they are not targeted to those they are most relevant to Utilities are not able to assess the effectiveness of their energy efficiency programs We can use smart meter data to - Segment the customer base into meaningful groups - Provide relevant and actionable information to them 17

18 Personalisation example: ONZO use smart meter data to disaggregate load 18

19 Challenges Drivers for consumption Available methods for time series data Availability of labelled data 19

20 There is no single driver for energy consumption Location Weather Size of home Number of occupants Type of occupants Income We need to understand more about what is going on in the home to understand variation in energy consumption. 20

21 Energy Example: Correlation of number of occupants with energy consumption But: this doesn t mean we can predict number of occupants with energy consumption alone 21

22 Energy Example: Correlation of number of bedrooms with energy consumption Would correlation be expected here? Why? 22

23 Energy Example: Correlation of internet access with energy consumption Those without internet access use more energy? 23

24 Challenges in clustering smart meter data Clustering raw time series data is demanding, techniques are limited, and gaps are a problem in real data Can also choose features, then cluster these must be robust and representative 24

25 Challenges in classifying smart meter data Availability of labelled data Validity of available data The industry needs long term, large scale studies which release relevant data. 25

26 Applications for segmentation and personalisation extend beyond billing Energy efficiency programs Customer understanding Inform future policy Plan generation Enhance grid Segmentation needs to be driven by the use cases. 26

27 The customer in focus Customers are people using energy. Not just people paying bills. ONZO is using data to let utilities focus on the customer 27

28 Domestic Energy Usage One Size Fits None February 2014 Katie Russell Head of Data & Analytics Onzo Ltd. 7 Praed Street, London W2 1NJ, United Kingdom T +44 (0) M +44 (0) onzo.com