Energy production forecasting based on renewable sources of energyS. Leva

Politecnico di Milano, Dipartimento di Energia

Via La Masa 34, 20156 Milano, Italy

sonia.leva@polimi.it, www.solartech.polimi.it

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Sonia Leva

1. Introduction: the energy production forecasting and the role of RES set up by the international energy agency

2. The energy forecasting from RES

3. Weather forecasting

4. The PV forecasting and error definitions, some examples

5. The wind forecasting, some examples

6. Conclusions

Goal and outline

The goal of this speech is to analyze how, starting from weather forecast, we can predict in term of hourly-curve the energy production by RES for one day – two days, a week ahead.

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1. Introduction: the energy production forecasting and the role of RES in the world and in Italy

2. The energy forecasting from RES

3. Weather forecasting

4. The PV forecasting and error definition, some examples

5. The wind forecasting, some examples

6. Conclusions

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Introduction: the energy production forecasting and the role of RES

The IEA forecasts confirm that the demand for energy (not just electricity) will grow especially in non-OECD

Global energy demand rises by over one-third in the period to 2035, underpinned by rising living standards in China, India & the Middle East

Share of global energy demand

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IEA predictions for the future (scenario "reference"): oil, gas, coal continue to dominate the energy (not just electricity) production

Introduction: the energy production forecasting and the role of RES

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IEA predictions of how will be satisfied the demand of electricity in the world.

«KING» COAL!

Introduction: the energy production forecasting and the role of RES

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Introduction: the role of RES in Italy

In five years the electricity generation by RES in Italy has doubled.

HydroGeothermalBioenergyWindSolar

The data are really up to date: august 2013!

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Introduction: the role of RES in Italy

• Number of plants producing electricity passes in a decade from 1 thousand to 550,000

• Centralized system tends towards a mixed system of generation (distributed generation)

• A growing number of households and factories now are involved in electricity generation

Electricity generation in Italy in the first seven monthes of 2013

Thermoelectric fossil

Hydro

geothermalBioenergy

wind

PV

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1. Introduction: the energy production forecasting and the role of RES in the world and in Italy

2. The energy forecasting from RES

3. Weather forecasting

4. The PV forecasting and error definition, some examples

5. The wind forecasting, some examples

6. Conclusion

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The energy forecasting from RES

Distributed system:• grid-connected RES installations are

decentralized • RESs energy production has a stochastic

behavior.• RESs are much smaller than traditional utility

generators• Today's available transformation and storage

capabilities for electric energy are limited and cost-intensive.

Challenges of controlling and maintaining energy from inherently intermittet sources involves many aspects: efficicency, reliability, safety, stability of the grid and ability to forecast energy production.

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Forecasting of PV/wind is an estimation from expected power production of the plant in the future.

• For monitoring and maintenance purposes

• To help the grid operators to better manage the electric balance between power demand and supply and to improve embedding of distributed renewable energy sources.

• In stand alone hybrid systems energy forecasting can help to size all the components and to improve the reliability of the isolated systems.

The energy forecasting from RES

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Timehorizon Range Applications

Very short-termFew seconds to

30minutes ahead - Control and adjustment actions

Short-term 30 minutes to 6hours ahead

- Economic Dispatch Planning- Load Increment/Decrement

Decisions

Medium-term 6 hours to 1 dayahead

- Generator Online/Offline Decisions- Operational Security in Day-Ahead

- Electricity Market

Long-term 1 day to 1 weekor more ahead

- Unit Commitment Decisions- Reserve Requirement Decisions

- Maintenance Scheduling to Obtain Optimal Operating Cost

Time scale classification for RES Forecasting

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1. Introduction: the energy production forecasting and the role of RES in the world and in Italy

2. The energy forecasting from RES

3. Weather forecasting

4. The PV forecasting and error definition, some examples

5. The wind forecasting, some examples

6. Conclusion

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Weather forecast

• This is an orthogonal step to a grid operator: weather data is usually obtained from meteorological services.

• The most influencing factor for output determination are:• solar energy production: global irradiation forecast. • wind energy production: wind speed amplitude and direction

forecast, pressure forecast• The use of precise weather forecast models is essential before

reliable energy output models can be generated.

Forecasts of RES production is based on weather forecasts.

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Weather forecast models

Numerical Weather Prediction (NWP)

Complex global NWP models are used to predict a number of variables describing the dynamic of the atmosphere and then to derive the weather at a specific point of interest.Post processing techniques are applied to obtain down scaled models (1.5 km).

European Center for Medium-Range Weather-Forecasts Model (ECMWF)Global Forecast System (GFS),North American Mesoscale Model (NAM)

3-6 hors

Cloud Imagery

Influence of local cloudiness is considered to be the most critical factor for estimation of solar irradiation.The use of satellite provide high-quality medium term forecast.

Satellite-based (METEOSAT),Total Sky Imager,

24h-48h

Statistical Methods

based on historical observation data using time series regression models

ARIMAArticial Neural Networks (ANN),Fuzzy Logic (FL),ARMA/TDNNANFISRBFNNMLP

Long term

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Time horizon is a crucial aspect. Sunshine and wind speed can only be predicted with accuracy a few days in advance.

The number and type of variables describing the physics and dynamic of the atmosphere are fundamental topics.

Cloudy index or irradiation are two indexes that can impact on the forecast in a different way.

Meteorology remains a field of

uncertainty.

Weather forecast

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1. Introduction: the energy production forecasting and the role of RES in the world and in Italy

2. The energy forecasting from RES

3. Weather forecasting

4. The PV forecasting and error definition, some examples

5. The wind forecasting, some examples

6. Conclusion

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The PV forecast: different Models.

Physical Modelsto describe the relation between environmental data and power

- highly sensitive to the weather prediction

- have to be designed specifically for a particular energy system and location

Statistical Modelsare based on persistent prediction or on the time series' history

Persistent prediction, Similar-days Model

Stochastic Time Series

Machine LearningArtificial neural network (ANN) learn to recognize patterns in data using training data sets.They need historical weather forecasting data and PV-plant measured data for their training

Hybrid Models are any combination of two or more of the previously described methods. They could be two different stochastic models or a stochastic model and a physical model.

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The PV forecast: Physical Models.

PhysicalAlgorithm

Plant Description; Monitoring System

PV energy forecast

Weather forecast

Global Irradiation, Cloud cover, Temperature, ecc

Measured data

PV energy

forecast

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The PV forecast: Statistical Models

TRAINED NEURAL NETWORK

Environmental temperature

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Error Definitions

In order to correctly define the accuracy of the prediction and the relative error it is necessary to analyze different definitions of error. The starting point reference is the hourly error eh:

Pm,h is the average power produced in the hour (or energy kWh) Pp,h is the prediction provided by the forecasting model

From this basic definition, other error definitions have been inquired:• Absolute error based on the hourly output expected power

(p=predicted) [AEEG]: • absolute error based on the hourly output produced power

(m=measured) [AEEG]:

, ,, ,

, ,

m h p h hpu p h

p h p h

eP Pe

P P

, ,h m h p he P P

, ,,

hpu m h

m h

ee

P

AEEG=Italian Authority for Electricity and Gas

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Error Definitions

• Mean absolute error [AEEG et al]:

• Normalized mean absolute error NMAE, based on net capacity of the plant C [AEEG et al]

C could be the rated power, the maximum observed or expected power!!!!

, ,1

1  | |

N

m h p hh

MAE P PN

, ,

1

| |1  100

Nm h p h

h

P PNMAE

N C

%

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Error definitions

• Weighted mean absolute error WMAE% based on total energy production [AEEG et al.]:

• Normalized root mean square nRMSE, based on the maximum observed power [Urlicht et al]:

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2, ,1

,

| |

m )

1

ax(

Nm h p hh

m h

P PNnRMSE

P

, ,1

,1

| |100

Nm h p hh

Nm hh

P PWMAE

P

%

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weather forecasts data analysis: evaluation of their reliability.

Comparison between ANN forecasts and other methods

Ensembled methods

Plant data validation: Theoretical Solar Irradiance (clear sky)

Error definitionsAccuracy assessment of the obtained results

Some examples: Hybrid Models (ANN+Physical) Physical data: Theoretical Solar

Irradiance (clear sky), Sunrise-, Sunset-hour

weather forecasts

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A. Hybrid Models (ANN+Physical) at SolarTech Lab

TRAINED NEURAL NETWORK

Clear SkyPhysical Model

Environmental temperature

4.4kW, Milano, Italy

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NMAEp%= 3.08%

NMAEp% = 30.1%

A. Some Results: Solar Tech Lab

pink line: there was an error in the weather forecast.

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• 285kW PV plant, Cuneo (Italy)• Meteo dataset: Day, hour, Environmental temperature, wind direction, wind

speed, global solar irradiation

Goals:• Analysis of the error due to the weather forecasting• Ensembles method: use more than one trials of stochastic methods to make

the forecast• Absolute hourly error based on predicted power vs measured power

B. Hybrid Models (ANN+Physical) PV Plant in Cuneo

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B. Hybrid Models (ANN+Physical) PV Plant in Cuneo

Error due to the weather forecasting: difference between the irradiation given by weather service and the irradiation measured

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Solar Radiation forecastings are affected by a great error!

B. Hybrid Models (ANN+Physical) PV Plant in Cuneo

Error due to the weather forecasting: Absolute hourly errors of GI are sorted from largest to smallest.

Abs

olut

e ho

urly

err

or b

ased

on

expe

cted

glo

bal

irra

diat

ion

(pre

dict

ed)

and

on th

e m

easu

red

glob

al

irra

diat

ion.

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Some Results: Power Plant

ANN are stochastic methods: Different trials give different forecasting curves.Ensemble: power/energy forecast is calculated considering the hourly average value of different (here 10) trials.

Abs

olut

e ho

urly

err

or b

ased

on

expe

cted

out

put

pow

er (

pred

icte

d) a

nd o

n th

e m

easu

red

outp

ut p

ower

.

Hourly sample (from sunrise to sunset)

Ensemble methods reduce the error!

The error based on the measured power is bigger than the one based on the predicted!

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Some Results: Power Plant

NMAEp% = 10NMAEr% = 5.86

WMAEp% = 16.58

NMAEp% = 29.14NMAEr% = 15WMAEp% = 50

NMAEp% = 16NMAEr% = 7.33WMAEp% = 28.7

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Some Results: Power Plantex

pect

ed o

utpu

t pow

er (

pred

icte

d) a

nd v

ersu

s m

easu

red

outp

ut p

ower

.

1 year: NMAEp = 12.15%, NMAEr%=7,34%

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Sonia Leva

1. Introduction: the energy production forecasting and the role of RES in the world and in Italy

2. The energy forecasting from RES

3. Weather forecasting

4. The PV forecasting and error definition, some examples

5. The wind forecasting, some examples

6. Conclusion

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Wind Forecasting

• Forecasting of wind is an estimation from expected power production of the wind turbines in the future. This power production is expressed in kW or MW depending on the nominal capacity of the wind farm.

• Forecasting methods described for PV can be applied• Error definitions described for PV are used• Kalman or Kolmogorov-Zurbenko are usually adopted to better

extimate the wind speed eliminating the effects of noise and systematic errors

• Hybrid approaches (ANN + CFD computational fluid dynamics software) can improve the accuracy of the forecasting

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Input parameters:• Inviromental temperature [°C]

• Atmospheric pressure [hPa]

• Wind speed intensity [m/s]

• Humidity [%]

• Cloud coverage [%]

Performance parameters• WMAE

• NMAE

Implemented feed-forward ANN with details on input, output, and hidden layers.

Example: Castiglione Messer Marino Wind Farm

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Some Results: Castiglione Messer Marino Wind Farm

84 86 88 90 92 94 960

2

4

6

8

10

12

14

16

Day

Pow

er (

MW

)

Wind plant forecast

P

m,h

Pp,h

1000 iterations:NMAEp = 40.2 %NMAEr= 14%

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The use of tools of CFD (computational fluid dynamics software) can improve the predictive capability of forecasting systems.

The computational cost greatly limits its practical applicability for wind farms with a large number of wind turbines.

Expensive measurement systems (see anemometer towers) to model the field.

Hybrid methods: computational fluid dynamics software

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The most promising method: Hybrid methods

ANN

Physical algorithmCFD Analysis

Historical Wind data

Historical Power data

Ground description

Plant Description

by GSE, ANEMOS.plus

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Sonia Leva

1. Introduction: the energy production forecasting and the role of RES in the world and in Italy

2. The energy forecasting from RES

3. Weather forecasting

4. The PV forecasting and error definition, some examples

5. The wind forecasting, some examples

6. Conclusions

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Conclusions

The meteorological services have an important influence on the power forecasting system for PV and wind energy.

The input data analysis is very important and cost-intensive Hybrid forecasting method are the most promising methods both

for PV and Wind energy forecasting• PV. Clear sky data are very useful to reduce error.• Wind. The use of special filters (eg Kalman or KZ) may be useful for the

removal of systematic errors of the forecasts of wind speed provided by the NWP and used as input to statistical methods.

The performance of the forecasting models are strongly related to the time horizon of the forecast and to the characteristics of the land on which the plant/farm is placed.

The need for energy forecasting from RES is a recent topic!!!

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Sonia Leva

www.solartech.polimi.itDiapartimento di Energia

Via Lambruschini, 420133 Milano

e-mail: sonia.leva@polimi.ite-mail: giampaolo.manzolini@polimi.it

Tel. +39 02 2399 3800 (Centralino)3709 (Leva) – 3810 (Manzolini)

THANK YOU!

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Some Results: Power PlantA

bsol

ute

hour

ly e

rror

bas

ed o

n ex

pect

ed o

utpu

t po

wer

(pr

edic

ted)

and

on

the

mea

sure

d ou

tput

pow

er.