A physical hybrid artificial neural network

A Physical Hybrid Artificial Neural Network for Short Term Forecasting

of PV Plant Power Output

Alberto Dolara , Francesco Grimaccia, Sonia Leva , Marco Mussetta , and Emanuele Ogliari

Introduction

• Energy management for PV power plant– Demand response– Control– Storage– Regulation – Appropriate forecast

• In recent years several short-term power forecasting models related to PV plants have been published, in ANN and physical methods.

• The errors introduced by ANNs and physical methods sometimes are already too high for electricity market and RES imbalance issues.

• This paper presents a new auxiliary hybrid system model that combines a soft-computing model based on ANN and physical models of the total global irradiance (the clear sky curve) for medium-term power of a PV plant.

Energy Forecast modelsExample: Time Delay Neural Network

• LMA updates the ANN weights as follows:

Structure of the NAR used for forecasting solar global irradiance one hour ahead.

Forecasting data of power output of a PV system one hour ahead.

The New Proposed Hybrid Method

For ANN: number of hidden layers and training data.

For CSRM: longitude, latitude, tilt and azimuth.

Expected power produced by the PV plant for the next 24, 48 or 72 hour according to the weather forecast time horizon in input.

Error Definitions

• Hourly error percentage

• Normalized mean absolute error

• Weighted mean absolute error

• Normalized root mean square error

Case StudyThe Cremosano Greenfield solar park

Set ups:

• ANN– Structure: Multi-Layer Perceptron (MLP)– Training procedure:Error back Propagation (EBP) + LM algorithm

• PHANN– Same ANN + CSRM

Error Back Proragation

• Ek is the error committed by the ANN on the k-th sample.

• n input and p output• j neurons• k samples • tj excepted output• oj actual output

LM algorithmHessian matrixGradient

• nine neurons in the first layer; • seven neurons in the second layer; • sigmoid activation function in the single neuron; • 3000 iterations for EBP training.

overcast sky and heavy rain

Results

• The forecasting activity is performed in order to evaluate the accuracy of the two methods by comparing the previously exposed daily error definitions according to different forecasted time periods (1 day and 30 days).

• Different training data for 60, 90 and 120 days.

Normalized mean absolute error

-Daily error definitions

evaluated for two methods

comparison with 120 days of training.

Normalized root mean square error

Weighted mean absolute error

--Errors as a function of the size of the training set and of the different period of the year

- The accuracy of the forecasts, performed

with the same settings listed in the case study and with 120 days of

training, are evaluated for these three typical weather conditions:

1. sunny day with sunny weather forecasts

2. an unstable day3. and an extremely

cloudy day.

Power curves and daily error definitions applied to the forecasts in a sunny day.

Power curves and daily error definitions applied to the forecasts in an unstable day. Power curves and daily error

definitions applied to the forecasts in a cloudy day.

Conclusions

• The results from the error assessment, according to the error definitions here explained, lead to the conclusion that the hybrid method is more accurate than just the ANN even changing some settings in the neural network.

• Some improvements regarding the reliability of the weather forecast and to the pre-processng of the raw data used to train the network. Additional research directions for future works will include day clustering in the training dataset, to properly forecast the next day production according to the specific day-types.