Predicting Electricity Prices in Ireland's Market

This article looks into how Electricity Prices in Ireland can be predicted for the next day. The focus is on a specific market called the Integrated Single Electricity Market (I-SEM). The study pays special attention to times when electricity prices have been very unpredictable or volatile.

#Importance of Forecasting Electricity Prices

Accurate forecasting of electricity prices is vital for different market players. These include power generators, suppliers, and traders, who need to make informed decisions about how much electricity to produce, buy, or sell. Understanding future prices helps these participants avoid unnecessary costs and makes their operations more efficient.

#Overview of the Integrated Single Electricity Market

The I-SEM started operating on September 30, 2018. It aims to improve the previous market structure by combining Northern Ireland's and the Republic of Ireland's electricity systems. The goal is to create more competition which can help keep prices lower and ensure a reliable supply of electricity.

In this market, there are different types of trading, such as day-ahead and intra-day trading. Most of the trading happens in the day-ahead market, where participants submit their bids for supplying electricity a day in advance.

#Factors Affecting Electricity Prices

Electricity prices are influenced by many aspects, including:

• Supply: How much electricity is available.
• Demand: How much electricity consumers need.
• Fuel Costs: Prices of fuels used to generate electricity, such as Natural Gas.
• Weather: Conditions that affect renewable energy sources like wind and solar.

Electricity cannot be stored easily, so prices can fluctuate suddenly. As a result, forecasting how much electricity will cost is challenging, especially in volatile times.

#Study Focus

This study analyzed data from the I-SEM between October 2018 and September 2022. It aimed to find out which factors most influence electricity prices and how effective different forecasting methods are. The research also looked at how the relationship between these factors and prices has changed over recent years.

#Key Findings on Influential Factors

One of the main findings of the study is that the price of natural gas and the amount of Wind Energy available are the most important factors in predicting day-ahead prices. In recent years, the daily price of natural gas has become a better indicator for prices than the previous standard, which was based on a different natural gas market in the United States.

Additionally, the study found that as more renewable energy, particularly wind, is added to the grid, it has been pushing down electricity prices overall. However, it also increases the unpredictability of prices.

#Importance of Data in Forecasting

The study used a variety of data sources, including historical electricity prices, natural gas prices, wind generation, and more. The quality of the data is crucial for building an accurate forecasting model. Better and more relevant data leads to better predictions.

Different periods of data were tested to find what works best for predicting prices. Using recent data tends to provide better insights than older data.

#Models Used for Forecasting

Various models were tested to see which one provided the most accurate predictions. Some of the well-known types of models included:

• Linear Regression: A basic statistical method that assumes a straight-line relationship between input factors and electricity prices.
• Random Forest: A more advanced method that uses many decision trees to make predictions based on various input factors.
• Neural Networks: These involve layers of interconnected "neurons" that can capture complex patterns in data.
• Support Vector Machines: A model that works well for both classification and regression tasks, finding the best line to separate different data points.

#Evaluation of Model Performance

To assess how well each forecasting model predicted electricity prices, the study used several metrics. Some of these metrics include:

• Mean Absolute Error (MAE): This shows the average error in predictions. The smaller the MAE, the better the prediction.
• Root Mean Squared Error (RMSE): Similar to MAE but emphasizes larger errors.
• Relative Mean Absolute Error (rMAE): A new metric that helps compare models across different price levels, making it easier to see how well models perform during price swings.

#Results of the Study

The outcomes indicated that electric prices have become increasingly challenging to forecast accurately over the years. Even the best models showed a noticeable rise in forecasting errors.

Interestingly, simpler models like Linear Regression performed very well even in tough market conditions. In some instances, these basic models outperformed more complex neural networks and machine learning techniques, especially as the market faced turbulent pricing.

#Trends Over Time

From the analysis, it’s clear that the correlation between various factors affecting prices has shifted over time. While the demand for electricity was once the most significant factor, the focus has shifted to fuel prices and wind generation. The use of EU natural gas prices has also emerged as a more valuable input for forecasting than previously used measures.

#Implications for Stakeholders

The findings of this study can assist multiple stakeholders within the energy sector. These include:

• Electricity Generators: They can optimize their operations and manage their costs more effectively.
• Retailers: They can adjust their pricing strategies based on better forecasts.
• Policymakers: Insights can inform regulations and energy policies that aim to stabilize prices and encourage the use of renewables.

#Future Research Directions

While this research provides significant insights, there is still room for further study. Additional avenues include:

• Examining different structures of neural networks to enhance accuracy.
• Testing new machine learning models on recent data.
• Exploring how well models can predict during particularly volatile periods.

Ultimately, understanding future electricity prices is crucial for everyone involved in the energy market. By improving forecasting methods, all parties can work towards a more efficient and cost-effective electricity system.