Follow Up. Evaluate & Optimize. · Start of extract Forecast made Load forecast exported Hours to...

transcript

Follow Up. Evaluate & Optimize.Alexander Nordling, Application Consultant

Why Follow Up?

• To:• Find Errors

• Improve settings

• Decide investment/deinvestment

• Because:• Improve forecast quality

• Optimize costs

• Condition:• Fast feedback

• Easy data gathering

• Zooming possibility

Follow up

Forecasting

Follow Up in Aiolos• Designed to be flexible

+ Can do more or less anything- Settings to configure (one-time installation)

• Once configured, clear aim to be a 1-click-system

Day-ahead vs Intraday Manual vs Automatic Users specific

Value of working weekends & nights?Different weather suppliers & Weather stations Alternative forecasts / calculation models

Evaluate in different ways

• Analyze forecasts in diagram, table or with statistical key figures• Toggle between different resolution (original, daily, weekly, etc)• Toggle between multiple levels

Follow up basics• Diagram, Statistics

• Docking windows

• Follow up of one model

• Zoom down/Zoom in

• Multiple weather

Evaluate Forecast Providers

ME (Mean Error)

ME% (Relative Mean Error)

MAE (Mean Absolute Error)

RMSE (Root of Mean Squared Errors)

Max AE

Counts

𝑀𝑎𝑥 𝐴𝐸 = 𝑀𝑎𝑥(1

𝑖=1

𝑒𝑖

𝐶𝑜𝑢𝑛𝑡𝑠 =𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑓𝑜𝑟𝑒𝑐𝑎𝑠𝑡 𝑣𝑎𝑙𝑢𝑒𝑠

𝑇𝑜𝑡𝑎𝑙 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑣𝑎𝑙𝑢𝑒𝑠=𝑥

𝑀𝐸 % =1

𝑖=1

𝑛σ𝑖=1𝑛 𝑦𝑖 − 𝑓𝑖σ𝑖=1𝑛 𝑦𝑖 𝑖

𝑖=1

𝑛𝑒𝑖𝑦𝑖

y=measured values, f=forecast value, e=error, n=number of values, x= 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑓𝑜𝑟𝑒𝑐𝑎𝑠𝑡 𝑣𝑎𝑙𝑢𝑒𝑠 𝑤ℎ𝑒𝑛 𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑑 𝑣𝑎𝑙𝑢𝑒𝑠 𝑒𝑥𝑖𝑠𝑡

Simulate forecasts for analysis

Follow up can be based on • Actual made forecasts

• Simulations/Recreations

Forecasting

Follow up

Simulations

Why simulate?

• No waiting time for actual forecasts

• To make an investment decision

• Backtesting, e.g. test a new setting

Simulate without “cheating”

In a real life situation you are probably missing data for a period back in time (energy measurements and weather observations)

Possible to set up restrictions to make it as real-life like as possible

Missingdata

Forecast

Real-life

Forecast

Simulation

Set restriction

Follow up advanced• Extract settings

• Simulations/Recreated forecasts

• Temporal settings

• Mode/User settings

• Different time resolutions

• Different time periods

• Different models

• Multiple weather selections

Automatic Follow Up

• Let the system search for errors• Alerts you if any of the forecast’s quality is

exceeding the set boundaries• Run automatically every week/day/hour• Gives immediate feedback• Set optimal weights dynamically

Thank you!Alexander Nordling, Application Consultant

Automatic Follow Up

• Let the system search for errors• Alerts you if any of the forecast’s quality is exceeding the set boundaries• Run automated Follow up every week/day/hour• Gives immediate feedback

Forecast quality

• Monitor forecast quality• Automated reports

QUALITY SERIVICE

Evaluate different forecast horizons

• In Aiolos you can evaluate the quality of different time horizons.

• Example: • 45 minutes ahead,

• 6 hours ahead,

• Day ahead,

• Week ahead, etc

Tuesday Wednesday

00 - 12 12 - 24 00 - 2412 - 24

72 h forecasts

Load forecast exported Hours to

ignore

Extraction

Several Forecasts for the Same Period?

Tuesday Wednesday Thursday Friday

00 - 12 12 - 24 00 - 24 00 - 24 00 - 2412 - 24

72 h forecast

Load forecast exported Hours to ignore Extraction

Start of extract

2 days ahead forecast: AioForecast2016-02-15@20-00-00.prog

Morning forecast: AioForecast2016-02-16@06-00-00.prog

11 o clock (spot) forecast: AioForecast2016-02-16@11-00-00.prog

Afternoon forecast: AioForecast2016-02-16@16-30-00.prog

Weather Weighting

W Forecast 1

Optimal Weighted E Forecast

W Forecast 2

W Forecast 3

W Forecast 4

W Forecast 5

E Forecast 1

E Forecast 2

E Forecast 3

E Forecast 4

E Forecast 5

Configuration of multiple weather forecast

Weather station 1

Forecast series

Station 2 Station 3 Station 4 Station 5

Forecast series Observation series

Climate series Climate series

F O F O F O F O

Cl Cl Cl Cl Cl Cl Cl Cl

Weighting multiple weather forecasts

Weather station 1

Load forecast model True load

0,30 ∗ 𝐿𝑜𝑎𝑑 𝑓𝑜𝑟𝑒𝑐𝑎𝑠𝑡 1 + 0,1 ∗ 𝐿𝑜𝑎𝑑 𝑓𝑜𝑟𝑒𝑐𝑎𝑠𝑡 2 + ⋯+ 0,3 ∗ (𝑙𝑜𝑎𝑑 𝑓𝑜𝑟𝑒𝑐𝑎𝑠𝑡 5) + 𝑐𝑜𝑛𝑠𝑡𝑎𝑛𝑡

Automatic calculated coefficients

Weather station 2

Weather station 3

Weather station 4

Weather station 5

30 % 10 % 20 % 10 % 30 % Constant

Extract Settings

Tuesday Wednesday Thursday Friday

00 - 12 12 - 24 00 - 24 00 - 24 00 - 2412 - 24

72 h forecast

Load forecast exportedHours to

ignoreExtraction Continuing forecast…

Start of extract

Forecast made

Load forecast

exported

Hours to

ignore

Extractio

Forecast valid period

Continuing forecast…

Start of extract

2 days ahead forecast: AioForecast2016-02-15@20-00-00.prog

Morning forecast: AioForecast2016-02-16@06-00-00.prog

11 o clock (spot) forecast: AioForecast2016-02-16@11-00-00.prog

Afternoon forecast: AioForecast2016-02-16@16-30-00.prog

Extract Settings

Forecast made

Load forecast

exported

Hours to

ignore

Extractio

Forecast valid period

Continuing forecast…

Start of extract

• The starting point

Start of extract

Extract

• Length of forecast to evaluate

• Look for prog-file older than…

Look for prog-file created within this time period

Hours to ignore

Load forecast

exported

Extract Settings

24h Rest of 72h forecast

24h 24h 24h

Tuesday Wednesda

Thursday Friday

AioForecast2013-02-20@11-02-00.prog

Several days’ extracts collected to a timeseries

Evaluating day ahead

Extract Settings

Evaluating 3 days ahead

Extract Settings

Evaluating weekend leave

Extract Settings

Evaluating weekly forecast

Follow up on Follow up Promises

Day-ahead vs IntradayManual vs Automatic

User specific results

Day-aheadExtract = 24Period to ignore = 12Search period = 6Latest

IntradayExtract = 1Period to ignore = 0:45Search period = 2Latest

Value of working weekends & nights?

WeekendExtract = 24-72Period to ignore = 12Search period = 6-54LatestManual/automatic

NightExtract = 1-24Period to ignore = 1-12Search period = 1-6LatestManual/automatic

Weight Tolerances

Max sum of abs weights: |w1| + … + |wn | > 1,25

Min sum of weights: w1 + … + wn < 0,75

Max constant: | c | > 0,05 × mean_load

Dynamic Fractions

• Dynamic fractions:

• Symbol: • Sums up with 𝛼𝑓 + 𝛽• Change values at hours ahead relative

forecast start date

• Evaluation of:

• Separate sub series• Models• Weather points• External

• Optimal subsets of series

Dynamic Fractions vs other tools

Multiple weather forecasts- Max 5 weather stations

+ Simple configuration

+ Support for missing weather

• Alternative models+ Switch models

+ Simple configuration

- Weighting not possible

Dynamic fractions+ Flexible

+ Unlimited number of sub series (weather, models)

+ Possibility to combine weighting of both weather

providers and weather sites

+ Weighting of multiple models+ Export each alternative+ Combine external forecasts- Expanded tree structure- More administrative work

Follow up on Follow up Promises

Alternative forecasts / calculation models

Different weather suppliers & Weather stations

Follow Up. Evaluate & Optimize. · Start of extract Forecast made Load forecast exported Hours to...

Documents