scholarly journals Energy Markets Forecasting. From Inferential Statistics to Machine Learning: The German Case

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 364
Author(s):  
Emma Viviani ◽  
Luca Di Persio ◽  
Matthias Ehrhardt
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Statistical Methods ◽  
Probabilistic Method ◽  
Energy Markets ◽  
Price Forecasting ◽  
Electricity Prices ◽  
Electricity Price Forecasting ◽  
Point Forecast ◽  
Probabilistic Type

In this work, we investigate a probabilistic method for electricity price forecasting, which overcomes traditional ones. We start considering statistical methods for point forecast, comparing their performance in terms of efficiency, accuracy, and reliability, and we then exploit Neural Networks approaches to derive a hybrid model for probabilistic type forecasting. We show that our solution reaches the highest standard both in terms of efficiency and precision by testing its output on German electricity prices data.

scholarly journals Forecasting Electricity Prices: A Machine Learning Approach

Algorithms ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 119
Author(s):  
Mauro Castelli ◽  
Aleš Groznik ◽  
Aleš Popovič
Keyword(s):  
Machine Learning ◽  
Electricity Market ◽  
Dynamic Environment ◽  
Weather Conditions ◽  
Programming Approach ◽  
Future Price ◽  
Energy Prices ◽  
Electricity Prices ◽  
Market Participants ◽  
Electricity Price Forecasting

The electricity market is a complex, evolutionary, and dynamic environment. Forecasting electricity prices is an important issue for all electricity market participants. In this study, we shed light on how to improve electricity price forecasting accuracy through the use of a machine learning technique—namely, a novel genetic programming approach. Drawing on empirical data from the largest EU energy markets, we propose a forecasting model that considers variables related to weather conditions, oil prices, and CO2 coupons and predicts energy prices 24 h ahead. We show that the proposed model provides more accurate predictions of future electricity prices than existing prediction methods. Our important findings will assist the electricity market participants in forecasting future price movements.

scholarly journals Neural Network Based Model Comparison for Intraday Electricity Price Forecasting

Energies ◽  
2019 ◽  
Vol 12 (23) ◽  
pp. 4557 ◽  
Author(s):  
Ilkay Oksuz ◽  
Umut Ugurlu
Keyword(s):  
Neural Networks ◽  
Electricity Markets ◽  
Electricity Market ◽  
Network Architecture ◽  
Model Comparison ◽  
Market Price ◽  
Absolute Error ◽  
Electricity Price ◽  
Price Forecasting ◽  
Electricity Price Forecasting

The intraday electricity markets are continuous trade platforms for each hour of the day and have specific characteristics. These markets have shown an increasing number of transactions due to the requirement of close to delivery electricity trade. Recently, intraday electricity price market research has seen a rapid increase in a number of works for price prediction. However, most of these works focus on the features and descriptive statistics of the intraday electricity markets and overlook the comparison of different available models. In this paper, we compare a variety of methods including neural networks to predict intraday electricity market prices in Turkish intraday market. The recurrent neural networks methods outperform the classical methods. Furthermore, gated recurrent unit network architecture achieves the best results with a mean absolute error of 0.978 and a root mean square error of 1.302. Moreover, our results indicate that day-ahead market price of the corresponding hour is a key feature for intraday price forecasting and estimating spread values with day-ahead prices proves to be a more efficient method for prediction.

scholarly journals A linear regression pattern for electricity price forecasting in the Iberian electricity market

2019 ◽  
pp. 117-127
Author(s):  
Ângela Paula Ferreira ◽  
Jenice Gonçalves Ramos ◽  
Paula Odete Fernandes
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Model ◽  
Electricity Price ◽  
Price Forecasting ◽  
Electricity Prices ◽  
Market Participants ◽  
Electricity Price Forecasting ◽  
The Impact

The Iberian Market for Electricity resulted from a cooperation process developed by the Portuguese and Spanish administrations, aiming to promote the integration of the electrical systems of both countries. This common market consists of organized markets or power exchanges, and non-organised markets where bilateral over-the-counter trading takes place with or without brokers. Within this scenario, electricity price forecasts have become fundamental to the process of decision-making and strategy development by market participants. The unique characteristics of electricity prices such as non-stationarity, non-linearity and high volatility make this task very difficult. For this reason, instead of a simple time forecast, market participants are more interested in a causal forecast that is essential to estimate the uncertainty involved in the price. This work focuses on modelling the impact of various explanatory variables on the electricity price through a multiple linear regression analysis. The quality of the estimated models obtained validates the use of statistical or causal methods, such as the Multiple Linear Regression Model, as a plausible strategy to achieve causal forecasts of electricity prices in medium and long-term electricity price forecasting. From the evaluation of the electricity price forecasting for Portugal and Spain, in the year of 2017, the mean absolute percentage errors (MAPE) were 9.02% and 12.02%, respectively. In 2018, the MAPE, evaluated for 9 months, for Portugal and Spain equals 7.12% and 6.45%, respectively.

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