# Forecasting of Wind and Solar Farm Output in the Australian National Electricity Market: A Review

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## Abstract

**:**

## 1. Introduction

## 2. Forecasting Methods

## 3. Point Forecasting

- Though machine learning techniques are used a lot (note that they include ANN as an ML technique), deep learning techniques have not been utilised as much.
- Very short term, very long term and regional forecasting are subjects that are not covered well.
- Most artificial intelligence (AI) methods work well on sunny days but poorly on cloudy ones.
- Hybrid models work best.

## 4. Interval Forecasting

## 5. Ramp Forecasting

- Start with 24 h ahead forecasts that combine NWP forecasts with hour ahead forecasts.
- Add persistence forecasts and use the random forecast procedure to produce better forecasts.
- Add in the ramp rate, which is the forecast for the present hour minus the actual for the previous hour.
- Use a random forecast technique on this augmented set of forecasts.

## 6. Synthetic Solar Time Series

- State 1—1–21 MJ/m
^{2}. Class Mark 10. - State 2—21–30 MJ/m
^{2}. Class Mark 25. - State 3—30–35 MJ/m
^{2}. Class Mark 33.

- Select a random number r in $(0,1)$ using a random number generator.
- If $r1<0.05$, then the initial state is 1 and the initial solar irradiation value is 10 MJ/m
^{2}. - If $r1<0.47$, then the initial state is 2 and the initial solar irradiation value is 25 MJ/m
^{2}. - Otherwise, the initial state is 3 and the initial solar irradiation value is 33 MJ/m
^{2}. - So, let us assume $r1=0.465$, so we start in state 2, in this experiment.
- Then select $r2$ randomly from $(0,1)$.
- Assume that $r2=0.95$, so we transition to state 3.
- Then select $r3=0.035$, so we transition to state 2.
- Repeat as long as is needed.

## 7. Additional Considerations for Wind and Solar Farms

#### 7.1. Characteristics of Power Output

#### 7.2. Value of Forecasts

#### 7.3. Heterogeneity of Variance

## 8. Conclusions

- Clear sky output from solar farms, as compared to clear sky models for solar irradiation.
- Heterogeneity of solar and wind farm output.
- The value of forecasting, as compared to the skill of forecasting.

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## Abbreviations

AWEFS | Australian Wind Energy Forecasting System |

ASEFS | Australian Solar Energy Forecasting System |

FCAS | Frequency Control and Ancillary Services |

NEM | Australian National Electricity Market |

CSM | Clear Sky Model |

CSI | Clear Sky Index |

CSO | Clear Sky Output |

CSOI | Clear Sky Output Index |

AEMO | Australian Renewable Energy Agency |

SPEF | Solar Power Ensemble Forecaster |

ARMA | Autoregressive Moving Average |

ANN | Artificial Neural Network |

LSTM | Long Short Term Memory |

RCC | Radiation classification coordinate |

NWP | Numerical Weather Prediction |

ARENA | Australian Renewable Energy Agency |

RIS | AEMO Renewable Integration Study |

SCADA | Supervisory Control and Data Acquisition |

UIGF | Unconstrained Intermittent Generation Forecasts |

EWMA | Exponentially Weighted Moving Average |

EWMV | Exponentially Weighted Moving Variance |

RMSE | Root mean square error |

MAE | Mean absolute error |

MBE | Mean bias error |

SS | Skill score |

## Appendix A. Error Measures

#### Appendix A.1. Root Mean Square Error

#### Appendix A.2. Mean Absolute Error

#### Appendix A.3. Mean Bias Error

#### Appendix A.4. Skill Score

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Forecast Model | Evaluation Metrics (Best Results) | Reference |
---|---|---|

Elmann artificial neural network | NMBE (−0.21%), NMAE (6.50%), SD (0.11%), NRMSE (10.91%) | De Giorgi et al. [4] |

Regressions in logs, autoregressive integrated moving average (ARIMA), unobserved components models, transfer functions, neural networks and hybrid models | MAPE (0.1263) | Reikard [5] |

Multiple feed-forward neural networks for irradiance forecast + PV model | MAE (7.03 W/m^{2}), MAPE (3.41%), RMSE (8.60 W/m^{2}), R (0.99) | Durrani et al. [6] |

Least square support vector machines (LS-SVM), LS-SVM with wavelet decomposition, ANN | NMBE (0.12%), NMAE (6.40%), NRMSE (9.60%) | De Giorgi et al. [7] |

Correlation-based feature selection for univariate and multivariate NN ensemble and SVR | MAE (45.11 kW), MRE (3.92%) | Rana et al. [8] |

Feed-forward neural networks and physical hybrid ANN | NMAE (<1.0%), WMAE (1.96%) | Nespoli et al. [9] |

Fourier series with coupled autoregressive (AR) and dynamical system (CARDS) model | MeAPE (7.53%, 10.85%), MBE (0.45, 0.0002), KSI (17.92%, –), NRMSE (16.50%, 17.16%) | Huang et al. [10], Huang and Boland [11] |

Autoregressive moving average (ARMA) and ARIMA models fitted by the log-likelihood function | MAE (37.95 W/m^{2}), MAPE (0.1%) | Colak et al. [12] |

Fourier series plus autoregressive models, clear sky index plus plus neural net models and clear sky index plus ARMA models | NMBE (0.08%), NRMSE (10.91%), NMAD (5.12%) | Boland et al. [13] |

Global and mesoscale numerical weather prediction models combined with persistence model, time series models, k-nearest neighbours (KNN) models, ANN models and adaptive neuro-fuzzy models | RMSE (4243.01 Wh), NRMSE (11.79%), ME (−42.8 Wh), NME(−0.12%), MAE (2308.3 Wh), NMAE (6.41%) | Fernandez-Jimenez et al. [14] |

Reforcasting model combined with cloud tracking, ARMA and KNN models | MBE (0.1 kW), MAE (20.7 kW), RMSE (35.5 kW), SRMSE (26.2%) | Chu et al. [15] |

Verification of deterministic forecasts | A review paper | Yang et al. [16] |

Forecast Model | Reference |
---|---|

Non-parametric predictive density of solar irradiance for probabilistic forecasting | Grantham et al. [17] |

Probabilistic forecasting of solar radiation | Grantham et al. [17] |

Probabilistic forecasting of PV power | Ni et al. [18] |

Probabilistic forecasting of solar radiation | Boland and Grantham [19] |

Probabilistic forecasting of solar radiation | Golestaneh et al. [20] |

Ensemble solar forecasting with probabilistic post processing | Yagli et al. [21] |

ARMA and GARCH for prediction intervals | David et al. [22] |

Review of tools for probabilistic forecasting of PV power | Ahmed et al. [23] |

Review of tools for probabilistic forecasting of wind power generation | Zhang et al. [24] |

Probabilistic forecasting of wind power generation using predictive distribution optimisation | Sun et al. [25] |

Probabilistic forecasting of wind power generation using Gaussian mixture models | Jin et al. [26] |

Probabilistic forecasting of wind power generation using ensemble methods | Kim and Hur [27] |

Site | Best Model (RMSE) | Skill | Best Model (MAE) | Skill |
---|---|---|---|---|

Darling Downs | Ensemble ML | 9.2% | Ensemble Median | 13.0% |

Daydream | Ensemble Mean | 16.2% | Ensemble Median | 18.2% |

Gannawarra | SkyCam | 19.3% | Smart Persistence | 16.8% |

Emerald | Ensemble Mean | 2.8% | ASEFS | - |

Manildra | SkyCam | 21.2% | SkyCam | 16.6% |

Site | ASEFS Fee | Dispatch Model | Fee | Savings | Percentage Savings |
---|---|---|---|---|---|

Darling Downs | 432,500 | SF12 | 281,018 | 151,482 | 18% |

Daydream | 70,793 | SF10 | 57,983 | 12,810 | 35% |

Gannawarra | 39,778 | SF5 | 28,485 | 11,293 | 28% |

Emerald | 50,726 | SF10 | 2129 | 48,595 | 98% |

Average | Savings | 56,045 | 44% |

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## Share and Cite

**MDPI and ACS Style**

Boland, J.; Farah, S.; Bai, L.
Forecasting of Wind and Solar Farm Output in the Australian National Electricity Market: A Review. *Energies* **2022**, *15*, 370.
https://doi.org/10.3390/en15010370

**AMA Style**

Boland J, Farah S, Bai L.
Forecasting of Wind and Solar Farm Output in the Australian National Electricity Market: A Review. *Energies*. 2022; 15(1):370.
https://doi.org/10.3390/en15010370

**Chicago/Turabian Style**

Boland, John, Sleiman Farah, and Lei Bai.
2022. "Forecasting of Wind and Solar Farm Output in the Australian National Electricity Market: A Review" *Energies* 15, no. 1: 370.
https://doi.org/10.3390/en15010370