Energy production forecasting based on renewable sources of energyS. Leva
Politecnico di Milano, Dipartimento di Energia
Via La Masa 34, 20156 Milano, Italy
[email protected], www.solartech.polimi.it
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1.1. Introduction: the energy production forecasting and the Introduction: the energy production forecasting and the role of RES set up by the international energy agencyrole of RES set up by the international energy agency
2.2. The energy forecasting from RESThe energy forecasting from RES
3.3. Weather forecastingWeather forecasting
4.4. The PV forecasting and error definitions, some examplesThe PV forecasting and error definitions, some examples
5.5. The wind forecasting, some examplesThe wind forecasting, some examples
6.6. ConclusionsConclusions
Goal and outline
The goal of this speech is to analyze how, starting from weather The goal of this speech is to analyze how, starting from weather forecast, we can predict in term of hourly-curve the energy forecast, we can predict in term of hourly-curve the energy production by RES for one day – two days, a week aheadproduction by RES for one day – two days, a week ahead.
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1.1. Introduction: the energy production forecasting and the Introduction: the energy production forecasting and the role of RES in the world and in Italyrole of RES in the world and in Italy
2.2. The energy forecasting from RESThe energy forecasting from RES
3.3. Weather forecastingWeather forecasting
4.4. The PV forecasting and error definition, some examplesThe PV forecasting and error definition, some examples
5.5. The wind forecasting, some examplesThe wind forecasting, some examples
6.6. ConclusionsConclusions
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Introduction: the energy production the energy production forecasting and the role of RESforecasting 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 the energy production forecasting and the role of RESand 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 the energy production forecasting and the role of RESand the role of RES
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Introduction: the role of RES in Italythe 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 Italythe 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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1.1. Introduction: the energy production forecasting and the Introduction: the energy production forecasting and the role of RES in the world and in Italyrole of RES in the world and in Italy
2.2. The energy forecasting from RESThe energy forecasting from RES
3.3. Weather forecastingWeather forecasting
4.4. The PV forecasting and error definition, some examplesThe PV forecasting and error definition, some examples
5.5. The wind forecasting, some examplesThe wind forecasting, some examples
6.6. ConclusionConclusion
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The energy forecasting from RESThe 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 productionability 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 RESThe 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-term6 hours to 1 day
ahead
- 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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1.1. Introduction: the energy production forecasting and the Introduction: the energy production forecasting and the role of RES in the world and in Italyrole of RES in the world and in Italy
2.2. The energy forecasting from RESThe energy forecasting from RES
3.3. Weather forecastingWeather forecasting
4.4. The PV forecasting and error definition, some examplesThe PV forecasting and error definition, some examples
5.5. The wind forecasting, some examplesThe wind forecasting, some examples
6.6. ConclusionConclusion
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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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1.1. Introduction: the energy production forecasting and the Introduction: the energy production forecasting and the role of RES in the world and in Italyrole of RES in the world and in Italy
2.2. The energy forecasting from RESThe energy forecasting from RES
3.3. Weather forecastingWeather forecasting
4.4. The PV forecasting and error definition, some examplesThe PV forecasting and error definition, some examples
5.5. The wind forecasting, some examplesThe wind forecasting, some examples
6.6. ConclusionConclusion
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The PV forecast: different Models.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 ModelPersistent prediction, Similar-days Model
Stochastic Time SeriesStochastic Time Series
Machine LearningMachine 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.Physical Models.
PhysicalPhysicalAlgorithmAlgorithm
Plant Description; Monitoring SystemPlant Description; Monitoring System
PV energy forecast
Weather forecastWeather forecast
Global Irradiation, Cloud cover, Temperature, ecc
Measured Measured datadata
PV PV energy energy
forecastforecast
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The PV forecast: Statistical ModelsStatistical Models
TRAINED NEURAL NETWORKTRAINED 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)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 LabA. Hybrid Models (ANN+Physical) at SolarTech Lab
TRAINED NEURAL TRAINED NEURAL NETWORKNETWORK
Clear SkyClear SkyPhysical ModelPhysical 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 CuneoB. Hybrid Models (ANN+Physical) PV Plant in Cuneo
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B. Hybrid Models (ANN+Physical) PV Plant in CuneoB. 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 CuneoB. 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.
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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.
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 Plant
1 year: NMAEp = 12.15%, NMAEr%=7,34%
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1.1. Introduction: the energy production forecasting and the Introduction: the energy production forecasting and the role of RES in the world and in Italyrole of RES in the world and in Italy
2.2. The energy forecasting from RESThe energy forecasting from RES
3.3. Weather forecastingWeather forecasting
4.4. The PV forecasting and error definition, some examplesThe PV forecasting and error definition, some examples
5.5. The wind forecasting, some examplesThe wind forecasting, some examples
6.6. ConclusionConclusion
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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
• NMAEImplemented 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
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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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1.1. Introduction: the energy production forecasting and the Introduction: the energy production forecasting and the role of RES in the world and in Italyrole of RES in the world and in Italy
2.2. The energy forecasting from RESThe energy forecasting from RES
3.3. Weather forecastingWeather forecasting
4.4. The PV forecasting and error definition, some examplesThe PV forecasting and error definition, some examples
5.5. The wind forecasting, some examplesThe wind forecasting, some examples
6.6. ConclusionsConclusions
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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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www.solartech.polimi.itDiapartimento di Energia
Via Lambruschini, 420133 Milano
e-mail: [email protected]: [email protected]
Tel. +39 02 2399 3800 (Centralino)3709 (Leva) – 3810 (Manzolini)
THANK YOU!
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Some Results: Power Plant
