<abstract xmlns="http://www.w3.org/1999/xhtml"><p>The paper aims at modelling the electricity generator’s expectations about price development in the Latvian day-ahead electricity market. Correlation and sensitivity analysis methods are used to identify the key determinants of electricity price expectations. A neural network approach is employed to model electricity price expectations. The research results demonstrate that electricity price expectations depend on the historical electricity prices. The price a day ago is the key determinant of price expectations and the importance of the lagged prices reduces as the time backwards lengthens. Nine models of electricity price expectations are prepared for different natural seasons and types of the day. The forecast accuracy of models varies from high to low, since errors are 7.02 % to 59.23 %. The forecasting power of models for weekends is reduced; therefore, additional determinants of electricity price expectations should be considered in the models and advanced input selection algorithms should be applied in future research. Electricity price expectations affect the generator’s loss through the production decisions, which are made considering the expected (forecasted) prices. The models allow making the production decision at a sufficient level of accuracy.</p></abstract>

The paper aims at modelling the electricity generator’s expectations about price development in the Latvian day-ahead electricity market. Correlation and sensitivity analysis methods are used to identify the key determinants of electricity price expectations. A neural network approach is employed to model electricity price expectations. The research results demonstrate that electricity price expectations depend on the historical electricity prices. The price a day ago is the key determinant of price expectations and the importance of the lagged prices reduces as the time backwards lengthens. Nine models of electricity price expectations are prepared for different natural seasons and types of the day. The forecast accuracy of models varies from high to low, since errors are 7.02 % to 59.23 %. The forecasting power of models for weekends is reduced; therefore, additional determinants of electricity price expectations should be considered in the models and advanced input selection algorithms should be applied in future research. Electricity price expectations affect the generator’s loss through the production decisions, which are made considering the expected (forecasted) prices. The models allow making the production decision at a sufficient level of accuracy.

Modelling Electricity Price Expectations in a Day-Ahead Market: A Case of Latvia

Economics and Business

This work is licensed under the Creative Commons Attribution 4.0 Public License.

{"article-title":"Modelling Electricity Price Expectations in a Day-Ahead Market: A Case of Latvia"}

The paper aims at modelling the electricity generator’s expectations about price development in the Latvian day-ahead electricity market. Correlation and...

Modelling Electricity Price Expectations in a Day-Ahead Market: A Case of Latvia

Publié en ligne: 10 sept. 2016

Pages: 12 - 26

DOI: https://doi.org/10.1515/eb-2016-0017

Mots clés
Adaptive expectations, electricity, neural network, price, production decision making, profit

© 2016 Viktorija Bobinaite et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution 4.0 Public License.

Modelling Electricity Price Expectations in a Day-Ahead Market: A Case of Latvia

Publié en ligne: 10 sept. 2016

Pages: 12 - 26

DOI: https://doi.org/10.1515/eb-2016-0017

Mots clésAdaptive expectations, electricity, neural network, price, production decision making, profit

© 2016 Viktorija Bobinaite et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution 4.0 Public License.

Mots clés
Adaptive expectations, electricity, neural network, price, production decision making, profit