---
title: 2000_Probabilistic Forecasting Model for Non-normally Distributed EV Charging
tags:  #機械学習  
author: [小平　大輔](https://convert.docswell.com/user/daisuke-kodaira)
site: [Docswell](https://www.docswell.com/)
thumbnail: https://bcdn.docswell.com/page/DKJ4P3RYE1.jpg?width=480
description: D. Kodaira and J. Kondoh, &quot;Probabilistic Forecasting Model for Non-normally Distributed EV Charging Demand,&quot; 2020 International Conference on Smart Grids and Energy Systems (SGES), 2020, pp. 623-626, doi: 10.1109/SGES51519.2020.00116.
published: March 31, 22
canonical: https://convert.docswell.com/s/daisuke-kodaira/5RY63Z-2022-03-31-094020
---
# Page. 1

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Probabilistic Forecasting
Model for Non-normally
Distributed EV Charging
Demand
Daisuke Kodaira
Tokyo University of Science
1


# Page. 2

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Introduction – EV charging control
02 Scheduling
01 Forecasting
Uncertainty
• When they arrive and leave?
• How much energy demand?
• Can EV user book the charge?
Various Strategy
• Various objective functions
• Centralized or Decentralized
• Real-time control
03
Dispatch
Gap
• Unexpected arrival and leave
• Forecast error for the demand
The performance of EV control relies on the forecasting.
How can we evaluate the forecasting quality?
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Deterministic forecasting
EV charging
demand
kwh
The prediction is not always reliable….
Observed Load
6
“Prediction tells us
we don’t need to
care about the peak today”
Predicted
Load
0
1 2 3
････
23 hour
Now
EV control strategies cannot reduce the peak appropriately
in case the prediction has an error
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Probabilistic forecasting
The concept of probability is necessary to evaluate the risk
Prediction Interval (PI)
“Prediction tells us
the EV demand falls into gray
zone with 95% probability”
Forecast the demand in Probabilistic manner!
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# Page. 5

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Simulation data set
• The observed data from Dundee, Scotland from 2017 to 2018
• The charging spots are distributed in 15 km by 30 km square
• Forecast total demand in this area (not individual charging points or vehicles)
“Electric Vehicle Charging Sessions Dundee.” https://data.dundeecity.gov.uk/dataset/ev-charging-data (accessed Jun. 28, 2020)
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Simulation data set
• All charging transactions are recorded in the original data
ChargingEvent
UserID
CPID
StartDate
StartTime
EndDate
EndTime
TotalkWh
Cost
Site
Group
Model
7751157
601838
50914
6/9/2018
23:55
7/9/2018
7:03
22.287
0
Lochee Charging Hub Dundee
APT Controls; CYC; ChargePlace Scotland; Dundee City Council
APT 22kW Dual Outlet
“Electric Vehicle Charging Sessions Dundee.” https://data.dundeecity.gov.uk/dataset/ev-charging-data (accessed Jun. 28, 2020)
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Forecasting framework
•
•
•
•
•
One day-ahead forecasting
Forecast is obtained in every 15 minutes during one year
96 time slots in a day (15 minutes basis)
Forecasting errors in validation data are accumulated for PI composition
First step; Deterministic forecasting → Second Step; Probabilistic forecasting
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Ensemble forecasting
k-means clustering
Simple Neural network
10
5
0
-10
-5
0
-5
5
10
Year
Month
Day
Hour
Quarter
DayOfWeek
Holiday
Charging
demand
-10
Ensemble forecast is obtained in 15 minutes basis during one year
෡𝑡𝑖 + 𝑞𝑡 𝐹෠𝑡𝑖
𝑦ො𝑡𝑖 = 𝑝𝑡 𝐾
෡𝑡𝑖 and 𝐹෠𝑡𝑖 are the deterministic forecast by k-means and neural network models
𝐾
𝑦ො𝑡𝑖 is the ultimate deterministic for time slot t on the i-th day
𝑝𝑡 and 𝑞𝑡 are the weight for each model
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9
Compose Prediction Interval (PI)
Deterministic prediction
Deterministic prediction
Deterministic prediction
・・・・・
Day 2
Probability
Day 1
Day 3
Error distribution
for 0am~1am
Error rate[%]
Create PI
by Sample-based method
Each hour has error distribution respectively
The error is obtained by the forecasting in the past
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Simulation Result for one day
Observed demand is in the PI
Cover rate is 68% in a day
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Simulation Result for one month
Fig. 8. Prediction interval coverage rate during the
forecasting period
• 27 days show more than 80% PI coverage rate, but
only 37% of the days are over 95% cover rate.
Fig. 9. Root-mean-square error during the forecasted 30
days
• All the days have more than 5 kWh residual error.
This certain residual error is derived from the shortterm fluctuations.
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Source Codes and Data are available
daisukekodaira
URL: https://github.com/daisukekodaira/EV-forecasting.git
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Appendix
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Data conversion
• Ensemble forecasting
Start
time
End
time
→
User1
10:00
Charing
20 kWh
11:00
→
User2
10:15
Charing
10 kWh
10:45
Convert
Quarter
Charging
Demand
[kWh]
Participants
10:00-10:15
5
user1
10:15-10:30
5+5=10
user1, user2
10:30-10:45
5+5=10
user1, user2
10:45-11:00
5
user1
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# Page. 15

![Page Image](https://bcdn.docswell.com/page/NP7X48M57X.jpg)

Data for the simulation
• Normal distribution vs non-normal distribution
Fig. 3. Example of a histogram of the EV charging demand
from 5 am to 6 am
Fig. 4. Example of quantile–quantile plot of EV charging
demand from 5 am to 6 am
15


# Page. 16

![Page Image](https://bcdn.docswell.com/page/637KZKGR7D.jpg)

Various PIs example
Past Load and PIs
Chebyshev
Confidence Interval
Sample base
Each PI has another boundaries. Sample-based method shows the most
optimistic one and Chebyshev shows the most pessimistic one
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![Page Image](https://bcdn.docswell.com/page/4LJ3DZNG75.jpg)

Construction of PIs
• Window
Pick the 5% and 95% data in ascending data as the boundaries.
1 2 3 4 5 6 7 8 9 10
𝑙𝑜𝑤𝑒𝑟 𝑏𝑜𝑢𝑛𝑑𝑎𝑟𝑦 = 10 ∗ 0.05 → 1
𝑢𝑝𝑝𝑒𝑟 𝑏𝑜𝑢𝑛𝑑𝑎𝑟𝑦 = 10 ∗ 0.95 → 10
• Confidence Interval
The data is assumed to follow the normal distribution, calculate 95% CI
𝑙𝑜𝑤𝑒𝑟 𝑏𝑜𝑢𝑛𝑑𝑎𝑟𝑦 = 𝜇 − 2𝜎
𝑢𝑝𝑝𝑒𝑟 𝑏𝑜𝑢𝑛𝑑𝑎𝑟𝑦 = 𝜇 + 2𝜎
• Chebyshev
Chebyshev’s inequality ensure the certain percentage of data lays in certain
range
1
𝑃 𝑥 − 𝜇 ≥ 𝑘𝜎 ≤ 2
𝑘
17


