How can smart data analytics improve the way of information provision? -The trend of methods and an exploratory analysis-

Transcription

1 How can smart data analytics improve the way of information provision? -The trend of methods and an exploratory analysis- Central Research Institute of Electric Power Industry Hidenori Komatsu Ken-ichiro Nishio 8th International Conference on Energy Efficiency in Domestic Appliances and Lighting August 26 th,

2 Background Introduction Electricity companies are starting to install smart meters in the residential sector Articles on data objectives and analysis of smart meter are scattered and not well organized Objectives Identify the trends and problems by literature review Explanatory analysis: estimation of air conditioners demand etc. 2

3 Research subject Issues for smart meter penetration Information provision to customers Privacy, security Data utilization, service creation Cost reduction Network infrastructures Cost burden Promotion for installation Objective Base technology Data analysis methods Organizing existing insights Explanatory analysis 3

4 DATA OBJECTIVES AND DATA ANALYSIS METHODS 4

5 Data to be focused Meter reading (Master switch;monthly) Transition Electricity smart meter (Master switch;30-min) (Connectibility) HEMS etc. (per line;a unit of min or sec) Other than energy Attributes (No. people etc.) Home appliances Recognition, behaviors, preferences Sensors Buying history Position information Others Smart meter for gas/water (Master switch;hourly etc.) 5

6 Data objectives for smart meters Objectives Support for efficient energy use Support for tariff selection Efficiency improvement of management of end-user appliances Supply of new services Improvement of targeting strategies Improving management of electricity distribution system equipment Improvement of planning power demand and supply Examples Feedback or presentation of tips for energy conservation or demand response programs Proposal of tariff based on forecasted electricity bills Control of home electric appliances cooperating with HEMS Monitoring elderly people, security, healthcare, and product marketing Segmentation of customers and presentation of customized service contents Voltage monitoring of distribution system and outage monitoring to identify equipment breakdown Optimization of power planning, power management, and electricity procurement Various values for managers of power system/ distribution system, or customers 6

7 Smart meter data demand Simplified disaggregation time Examples Air conditioners demand Variable demand Fixed demand +exogenous factors Climate data Weekday or holiday Estimate breakdown based on relationship or regularity Regression analysis Neural networks Difference method Problems Current data resolution (in units of 30-min, 100Wh) is not suitable for detailed disaggregation specifying each home appliance. 7

8 Load pattern categorization Smart meter data demand time Examples Peak-in-morning Peak-in-afternoon Two-peaks Irregular Clustering based on similarity Neural networks (ex. Self Organizing Map) Fuzzy theory Statistical methods Problems Since ways of categorization are infinite, analysis plans suitable for objectives is necessary 8

9 Smart meter data demand Life pattern estimation time State A State B Examples At-home/ active Not-at-home/ inactive +Training data Records of at-home state etc. Determine based on state of demand Distribution of demand or its variation Hidden Markov Models Problems Hard to distinguish at-home & notusing-electricity state from not-athome state 9

10 Smart meter data demand +Training data Attributes of households etc. Attribute estimation time Attribute A= Attribute B= Attribute C= Attribute Accuracy depends on the relationship K-nearest neighbors Discriminant function Analysis Support Vector Machine Examples Single person/ more than one Gross floor space large/small Home appliances many/few Problems A considerable number of training data is necessary for model estimation 10

11 demand Demand forecast time +exogenous factors Climate data Weekday or holiday Forecast based on relationship or regularity Regression analysis Neural networks Others Scale system/region/ group/household Term On the day/the next day/on the month/ in the year/ after a few years Problems Accuracy decreases in lowscale or longterm forecast 11

12 Data analysis methods for smart meter data Analysis Methods Simplified* disaggrega tion Load pattern categorizat ion Life pattern estimation Attribute estimation Demand forecast Main Purpose Estimate of air conditioner use per time period Estimate of temperature sensitivity Categorization of customers based on consumption per time period Detection of anomalous load patterns Detect whether residents are at home or active Attributes strongly correlated with demand (number in household or floor space) is estimated accurately to some extent System-wide or area-wide demand forecast Customer-wide forecast Problems *Data resolutions are different. Ex. simplified: 30-min, detailed: Hz Not suitable for estimating consumption or operating status of individual appliance (detailed disaggregation*) Analysis plan suited to the objective is required because categorization is nonunique Distinguishing between residents being at home and consuming no electricity and residents being out is difficult Certain amount of data labeled with actual attributes is necessary for model Accuracy decreases in small-scale or long-term forecast Combinations of these methods could be possible 12

13 EXPLORATORY DATA ANALYSIS 13

14 House hold Location 1 Sagaragun, Kyoto-fu 2 Ikoma-shi, Nara-ken 3 Nara-shi, Nara-ken 4 Suita-shi, Osaka-fu Household attributes Household size (No. people) We chose a location geographically close to the household Type of building 4 Detache d 4 Detache d 4 Detache d 2 Detache d Gross floor space (m 2 ) Location (AMeDAS) 70 Ueno, Mie-ken 110 Nara, Nara-ken 125 Nara, Nara-ken 139 Osaka, Osaka-fu Public data of 4 detached households in Kansai area, Japan were used 14

15 Disaggregating demand by linear regression 3 formulae are predefined 1 CDH&HDH 2 A quadratic function 3 No temperature-sensitive demand Non-temperature-sensitive demand is excluded Models are estimated per hour using one-year data Employ one model with the highest explanatory power from the (a) 8 am (b) 8 pm Demand (kwh/h) Demand (kwh/h) Temperature( ) Temperature( ) 15

16 Temperature-sensitive demand (monthly) Demand(kWh/month) Temperature-sensitive demand (estimation) Air conditioners demand (reference) 0 May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr Household 2 Demand(kWh/month) Temperature-sensitive demand (estimation) Air conditioners demand (reference) 0 May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr Household 3 The timing of peaks of air conditioners demand was successfully estimated 16

17 Yearly averaged temperature-sensitive demand Demand(Wh/h) Temperature-sensitive demand (estimation) Air conditioners demand (reference) (o'clock) Demand(Wh/h) Temperature-sensitive demand (estimation) Air conditioners demand (reference) (o'clock) Household 1 Household 2 Demand(Wh/h) Temperature-sensitive demand (estimation) Air conditioners demand (reference) (o'clock) Demand(Wh/h Temperature-sensitive demand (estimation) Air conditioners demand (reference) (o'clock) Household 3 Household 4 Data tells what time they are using electricity for air conditioners 17

18 Demand-active level Demand-active level per hour (Time of day) 12 am 3 am 6 am 9 am 12 pm 3 pm 6 pm 9 pm Demand-active level: Frequency of additional demand, except fixed demand caused by such as television or refrigerators. House hold 1 House hold 2 House hold 3 House hold 4 Data tells whether they use electricity at midnight, whether they are at home during the day, and what time they return home 18

19 Demand-active level Demand-active level per day of the week for 1 pm to 4 pm (system peak time) Mon Tue Wed Thr Fri Sat Sun Demand-active level: Frequency of additional demand, except fixed demand caused by such as television or refrigerators. Household 1 Household 2 Household 3 Household 4 Data tells whether they are at home on system peak time per day of the week 19

20 Demand-active level Demand-active level per day of the week for 12 am to 11 pm (whole day) Household 1 Household 2 Household 3 Household 4 Mon Tue Wed Thr Fri Sat Sun Demand-active level: Frequency of additional demand, except fixed demand caused by such as television or refrigerators. Data tells whether the life patterns depend on day of the week. The residents in household 4 might be retired. 20

21 How data analysis methods contributes to data objectives Data objectives Data analysis methods Support for efficient energy use Simplified disaggregation Support for tariff selection Efficiency improvement of management of end-user appliances Supply of new services Improvement of targeting strategies Improving management of electricity distribution system equipment Improvement of planning power demand and supply Load pattern categorization Life pattern estimation Attribute estimation Demand forecast It is essential for analysis to identify which methods are available for the objective 21

22 Discussions Applying analysis methods Accuracy will be improved by combining insights or data Issues for explanatory analysis Needs refinement, or more samples Utilization of analytics Behavioral change, DR information, supply of new services Role sharing with other type of data Other type of data could be suitable for other objectives Using data from gas, water usage, or sensors synergistically/separately 22

23 THANK YOU VERY MUCH FOR YOUR ATTENTION! 23