# On the Influence of Renewable Energy Sources in Electricity Price Forecasting in the Iberian Market

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## Abstract

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## 1. Introduction

- can we find a correlation between renewable production and spot price? If so, can we find the same correlation with renewables forecasts?
- how does forecast of renewables affect prediction of price? is it a distorting variable or an aligned component for electricity price forecasting?

## 2. Related Work

## 3. Preliminaries

#### 3.1. Feedforward Neural Networks

#### 3.2. Recurrent Neural Networks

## 4. The Day-Ahead Iberian Electricity Market

#### 4.1. Market Description

#### 4.2. Analysis of Historical Data

## 5. Methodology

#### 5.1. Dataset

- Day-ahead prices: the hourly prices of previous days are the basic input of almost all electricity forecasting systems. Various works have identified correlations between the prices of day d and the previous prices of days $d-1$, $d-2$ and $d-7$, reason why time lagged prices are regularly used in the EPF models presented in the literature [49,50]. Therefore, in order to predict the electricity price for a given hour h, ${p}_{h}$, we used the lagged prices ${p}_{h-24}$, ${p}_{h-48}$ and ${p}_{h-168}$. This information was published each day after the day-ahead auction by the market operator, OMIE [4].
- Time and calendar: the hour corresponding to each entry in the dataset was registered using an integer variable $h\in [0,23]$. Additionally, we provided two other variables with calendar information, an integer $d\in [0,6]$ for the day of the week, and a boolean $holiday\in \{0,1\}$ to identify the existence or non-existence of a national holiday that day.
- Forecasted generation and demand: as a result of the findings of Section 4.2, we includeD the hourly forecast solar and wind power generation, as well as the hourly forecast demand. The methodology used for the demand forecast was explained in [51]. All three forecasts were provided by the system operator, REE, and can be obtained through its information system, e·sios (Sistema de Información del Operador del Sistema https://www.esios.ree.es/es).

#### 5.2. Proposed Model

- Exogenous variables: the proposed model supported an arbitrary number of exogenous variables, i.e., data outside the price time series. This allowed the inclusion of other explanatory variables, such as the ones presented in Section 5.1.
- Non-linearity: neural networks were able to learn nonlinear dependencies between variables.
- Seasonality: LSTM cells were particularly suitable for learning the order dependence of seasonal data.

## 6. Experimental Results

## 7. Discussion

## 8. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

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Variable | Category | Description | Range |
---|---|---|---|

V1 | Day-ahead prices | ${p}_{h-24}$ | 0–110 €/MWh |

V2 | Day-ahead prices | ${p}_{h-48}$ | 0–110 €/MWh |

V3 | Day-ahead prices | ${p}_{h-168}$ | 0–110 €/MWh |

V4 | Time and calendar | Hour | 0–23 |

V5 | Time and calendar | Week day | 0–6 |

V6 | Time and calendar | National holiday | 0–1 |

V7 | Forecasted generation and demand | Forecasted solar generation for h | 0–3650.2 MWh |

V8 | Forecasted generation and demand | Forecasted wind generation for h | 277–17,385 MWh |

V9 | Forecasted generation and demand | Forecasted demand for h | 17,599–40,050 MWh |

V10 | Derived variable | forecasted ratio of renewable energy for h | 0–1 |

Variable | Description | M1 | M2 | M3 |
---|---|---|---|---|

V1 | ${p}_{h-24}$ | √ | √ | √ |

V2 | ${p}_{h-48}$ | √ | √ | √ |

V3 | ${p}_{h-168}$ | √ | √ | √ |

V4 | Hour | √ | √ | √ |

V5 | Week day | √ | √ | √ |

V6 | National holiday | √ | √ | √ |

V7 | Forecasted wind generation for h | - | √ | √ |

V8 | Forecasted solar generation for h | - | √ | √ |

V9 | Forecasted demand for h | - | √ | √ |

V10 | Forecasted ratio of renewable energy for h | - | - | √ |

Week | Period | Season |
---|---|---|

W1 | 2 February–8 February | Winter |

W2 | 4 May–10 May | Spring |

W3 | 3 August–9 August | Summer |

W4 | 2 November–8 November | Autumn |

Week | M1 | M2 | M3 |
---|---|---|---|

W1 | 24.22 | 9.99 | 9.83 |

W2 | 24.72 | 9.93 | 9.13 |

W3 | 7.51 | 5.07 | 5.01 |

W4 | 12.17 | 7.23 | 6.69 |

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**MDPI and ACS Style**

Aineto, D.; Iranzo-Sánchez, J.; Lemus-Zúñiga, L.G.; Onaindia, E.; Urchueguía, J.F. On the Influence of Renewable Energy Sources in Electricity Price Forecasting in the Iberian Market. *Energies* **2019**, *12*, 2082.
https://doi.org/10.3390/en12112082

**AMA Style**

Aineto D, Iranzo-Sánchez J, Lemus-Zúñiga LG, Onaindia E, Urchueguía JF. On the Influence of Renewable Energy Sources in Electricity Price Forecasting in the Iberian Market. *Energies*. 2019; 12(11):2082.
https://doi.org/10.3390/en12112082

**Chicago/Turabian Style**

Aineto, Diego, Javier Iranzo-Sánchez, Lenin G. Lemus-Zúñiga, Eva Onaindia, and Javier F. Urchueguía. 2019. "On the Influence of Renewable Energy Sources in Electricity Price Forecasting in the Iberian Market" *Energies* 12, no. 11: 2082.
https://doi.org/10.3390/en12112082