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 sensitivity analysis methods are used to identify the key determinants of electricity price expectations. A neural network approach is employed to...
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Online Access: | https://doi.org/10.1515/eb-2016-0017 |
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doaj-0be5e7c2cbeb473aa20a46e910ddc2562021-09-05T20:44:45ZengSciendoEconomics and Business1407-73372256-03942016-08-01291122610.1515/eb-2016-0017Modelling Electricity Price Expectations in a Day-Ahead Market: A Case of LatviaBobinaite Viktorija0Zuters Jānis1 University of Latvia Latvia University of Latvia LatviaThe 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.https://doi.org/10.1515/eb-2016-0017adaptive expectationselectricityneural networkpriceproduction decision makingprofit |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Bobinaite Viktorija Zuters Jānis |
spellingShingle |
Bobinaite Viktorija Zuters Jānis Modelling Electricity Price Expectations in a Day-Ahead Market: A Case of Latvia Economics and Business adaptive expectations electricity neural network price production decision making profit |
author_facet |
Bobinaite Viktorija Zuters Jānis |
author_sort |
Bobinaite Viktorija |
title |
Modelling Electricity Price Expectations in a Day-Ahead Market: A Case of Latvia |
title_short |
Modelling Electricity Price Expectations in a Day-Ahead Market: A Case of Latvia |
title_full |
Modelling Electricity Price Expectations in a Day-Ahead Market: A Case of Latvia |
title_fullStr |
Modelling Electricity Price Expectations in a Day-Ahead Market: A Case of Latvia |
title_full_unstemmed |
Modelling Electricity Price Expectations in a Day-Ahead Market: A Case of Latvia |
title_sort |
modelling electricity price expectations in a day-ahead market: a case of latvia |
publisher |
Sciendo |
series |
Economics and Business |
issn |
1407-7337 2256-0394 |
publishDate |
2016-08-01 |
description |
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. |
topic |
adaptive expectations electricity neural network price production decision making profit |
url |
https://doi.org/10.1515/eb-2016-0017 |
work_keys_str_mv |
AT bobinaiteviktorija modellingelectricitypriceexpectationsinadayaheadmarketacaseoflatvia AT zutersjanis modellingelectricitypriceexpectationsinadayaheadmarketacaseoflatvia |
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1717785141012594688 |