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Energy production forecasting based on renewable sources of energy

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Energy production forecasting based on renewable sources of energy

S. Leva

Politecnico di Milano, Dipartimento di Energia

Via La Masa 34, 20156 Milano, Italy

[email protected], www.solartech.polimi.it

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 â€“ two days, a week ahead.

- Introduction: the energy production forecasting and the role of RES set up by the international energy agency
- The energy forecasting from RES
- Weather forecasting
- The PV forecasting and error definitions, some examples
- The wind forecasting, some examples
- Conclusions

- Introduction: the energy production forecasting and the role of RES in the world and in Italy
- The energy forecasting from RES
- Weather forecasting
- The PV forecasting and error definition, some examples
- The wind forecasting, some examples
- Conclusions

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

Share of global energy demand

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

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

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

Â«KINGÂ» COAL!

In fiveyears the electricitygeneration by RES in Italyhas doubled.

Hydro

Geothermal

Bioenergy

Wind

Solar

The data are really up to date: august 2013!

Electricity generation in Italy in the first seven monthes of 2013

- 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

geothermal

Bioenergy

wind

PV

Thermoelectric fossil

Hydro

- Introduction: the energy production forecasting and the role of RES in the world and in Italy
- The energy forecasting from RES
- Weather forecasting
- The PV forecasting and error definition, some examples
- The wind forecasting, some examples
- Conclusion

- 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 maintainingenergy from inherentlyintermittetsourcesinvolvesmanyaspects: efficicency, reliability, safety, stability of the grid and ability to forecastenergy production.

The energy forecasting from RES

- Forecasting of PV/wind is an estimation from expected power production of the plant in the future.
- For monitoring and maintenancepurposes
- To help the grid operators to better manage the electric balance between power demand and supply and to improveembedding of distributedrenewableenergysources.
- In stand alone hybrid systems energy forecasting can help to size all the components and to improve the reliability of the isolated systems.

Time scale classification for RES Forecasting

- Introduction: the energy production forecasting and the role of RES in the world and in Italy
- The energy forecasting from RES
- Weather forecasting
- The PV forecasting and error definition, some examples
- The wind forecasting, some examples
- Conclusion

Forecasts of RES production is based on weather forecasts.

- 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.

CloudImagery

Influence of local cloudiness is considered to be the most criticalfactor for estimation of solar irradiation.

The use of satellite provide high-quality medium termforecast.

Satellite-based (METEOSAT),

Total SkyImager,

24h-48h

Statistical Methods

based on historical observation data using time series regression models

ARIMA

ArticialNeural Networks (ANN),

FuzzyLogic (FL),

ARMA/TDNN

ANFIS

RBFNN

MLP

Long term

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

Meteorologyremains a field of uncertainty.

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.

- Introduction: the energy production forecasting and the role of RES in the world and in Italy
- The energy forecasting from RES
- Weather forecasting
- The PV forecasting and error definition, some examples
- The wind forecasting, some examples
- Conclusion

- PhysicalModels
- to describe the relation betweenenvironmental data and power
- highly sensitive to the weather prediction
- have to be designed specifically for a particular energy system and location

Statistical Models

are based on persistent prediction or on the time series' history

Persistentprediction, Similar-days Model

Stochastic Time Series

Machine Learning

Artificialneural 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

HybridModelsare 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.

Weather forecast

PV energy forecast

PV energy forecast

Physical

Algorithm

Global Irradiation, Cloud cover, Temperature, ecc

Plant Description; Monitoring System

Measured data

TRAINED NEURAL NETWORK

Environmental temperature

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]:

AEEG=Italian Authority for Electricity and Gas

- 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!!!!

- 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]:

- Physical data: Theoretical Solar Irradiance (clear sky), Sunrise-, Sunset-hour
- weather forecasts

- Plant data validation: Theoretical Solar Irradiance (clear sky)

- weather forecasts data analysis: evaluation of their reliability.
- Comparison between ANN forecasts and other methods
- Ensembled methods

- Error definitions
- Accuracy assessment of the obtained results

TRAINED NEURAL NETWORK

Environmental temperature

Clear Sky

Physical Model

4.4kW, Milano, Italy

NMAEp%= 3.08%

NMAEp% = 30.1%

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

- 285kW PV plant, Cuneo (Italy)
- Meteo dataset: Day, hour, Environmental temperature, winddirection, windspeed, 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

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

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

Absolute hourly error based on expected global irradiation (predicted) and on the measured global irradiation.

Solar Radiation forecastings are affected by a great error!

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.

Absolute hourly error based on expected output power (predicted) and on the measured output power.

Ensemble methods reduce the error!

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

Hourly sample (from sunrise to sunset)

NMAEp% = 10

NMAEr% = 5.86

WMAEp% = 16.58

NMAEp% = 29.14

NMAEr% = 15

WMAEp% = 50

NMAEp% = 16

NMAEr% = 7.33

WMAEp% = 28.7

expected output power (predicted) and versus measured output power.

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

- Introduction: the energy production forecasting and the role of RES in the world and in Italy
- The energy forecasting from RES
- Weather forecasting
- The PV forecasting and error definition, some examples
- The wind forecasting, some examples
- Conclusion

- 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

- Input parameters:
- Inviromental temperature [Â°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.

- 1000 iterations:
- NMAEp=40.2 %
- NMAEr= 14%

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.

Plant Description

ANN

Historical Wind data

Historical Power data

Physical algorithm

CFD Analysis

Ground description

by GSE, ANEMOS.plus

- Introduction: the energy production forecasting and the role of RES in the world and in Italy
- The energy forecasting from RES
- Weather forecasting
- The PV forecasting and error definition, some examples
- The wind forecasting, some examples
- 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!!!

THANK YOU!

www.solartech.polimi.it

Diapartimento di Energia

Via Lambruschini, 4

20133 Milano

e-mail: [email protected]

e-mail: [email protected]

Tel. +39 02 2399 3800 (Centralino)

3709 (Leva) â€“ 3810 (Manzolini)

Absolute hourly error based on expected output power (predicted) and on the measured output power.