Optimisation of Run of River Production Forecasting Using Aiolos Forecast Studio
Hydropower is simply the largest source of renewable energy in the Nordic countries, where It compose around 90% of power production in Iceland, 70% in Norway, 40% in Sweden and 20% in Finland. Mountainous terrain and abundance in the surface water is a significant contributing factor in hydropower...
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Lunds universitet/Avdelningen för Teknisk vattenresurslära
2020
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ftulundlupsp:oai:lup-student-papers.lub.lu.se:9020357 2023-07-30T04:04:28+02:00 Optimisation of Run of River Production Forecasting Using Aiolos Forecast Studio Al-Qes, Amer 2020 application/pdf http://lup.lub.lu.se/student-papers/record/9020357 eng eng Lunds universitet/Avdelningen för Teknisk vattenresurslära http://lup.lub.lu.se/student-papers/record/9020357 ISSN: 1101-9824 Run of river hydropower plants power production forecasting Aiolos forecast studio model accuracy assessment analysis and power market Technology and Engineering H2 2020 ftulundlupsp 2023-07-11T20:09:08Z Hydropower is simply the largest source of renewable energy in the Nordic countries, where It compose around 90% of power production in Iceland, 70% in Norway, 40% in Sweden and 20% in Finland. Mountainous terrain and abundance in the surface water is a significant contributing factor in hydropower production. Hydropower is also considered more reliable and continuous than other forms of renewable energy such as solar and wind. In deregulated power markets where large magnitudes of power are bid for and traded continuously, smart trading and accurate power production forecasts give the advantage to the companies participating in the bids. Aiolos Forecast Studio (AFS) is computer software that provides numerous services in power production forecasting; including hydropower production, which is forecasted by the Achelous model. Several power companies currently use this computer software, one of which is Fortum where it forecasts the power production of Småkraft’s hydropower stations. This Report explains how AFS forecasts hydropower as well as the shortcomings and possible improvements; also, it introduces the calibration procedure to the models to achiever better forecast accuracy. The Report starts with a comprehensive understanding of the model, the Nordic power market and the Hydropower stations taken as a case study; leading to the development of a recalibration procedure based on the understanding of the Achelous model and the nature of the watersheds. Seven hydropower stations were used for developing a calibration procedure, where two were used for calibration and five for validation. This study involved developing an excel sheet that analysis the accuracy of the forecasts to interpret the model forecasts results; forecasts of power production, runoff, snow cover and precipitation were compared for the year of 2019 with actual data using the excel sheet. By interpreting the statistical analysis results, several shortcomings of the model were highlighted that cause a decrease in the forecast accuracy. Most ... Other/Unknown Material Iceland Lund University Publications Student Papers (LUP-SP) Norway |
institution |
Open Polar |
collection |
Lund University Publications Student Papers (LUP-SP) |
op_collection_id |
ftulundlupsp |
language |
English |
topic |
Run of river hydropower plants power production forecasting Aiolos forecast studio model accuracy assessment analysis and power market Technology and Engineering |
spellingShingle |
Run of river hydropower plants power production forecasting Aiolos forecast studio model accuracy assessment analysis and power market Technology and Engineering Al-Qes, Amer Optimisation of Run of River Production Forecasting Using Aiolos Forecast Studio |
topic_facet |
Run of river hydropower plants power production forecasting Aiolos forecast studio model accuracy assessment analysis and power market Technology and Engineering |
description |
Hydropower is simply the largest source of renewable energy in the Nordic countries, where It compose around 90% of power production in Iceland, 70% in Norway, 40% in Sweden and 20% in Finland. Mountainous terrain and abundance in the surface water is a significant contributing factor in hydropower production. Hydropower is also considered more reliable and continuous than other forms of renewable energy such as solar and wind. In deregulated power markets where large magnitudes of power are bid for and traded continuously, smart trading and accurate power production forecasts give the advantage to the companies participating in the bids. Aiolos Forecast Studio (AFS) is computer software that provides numerous services in power production forecasting; including hydropower production, which is forecasted by the Achelous model. Several power companies currently use this computer software, one of which is Fortum where it forecasts the power production of Småkraft’s hydropower stations. This Report explains how AFS forecasts hydropower as well as the shortcomings and possible improvements; also, it introduces the calibration procedure to the models to achiever better forecast accuracy. The Report starts with a comprehensive understanding of the model, the Nordic power market and the Hydropower stations taken as a case study; leading to the development of a recalibration procedure based on the understanding of the Achelous model and the nature of the watersheds. Seven hydropower stations were used for developing a calibration procedure, where two were used for calibration and five for validation. This study involved developing an excel sheet that analysis the accuracy of the forecasts to interpret the model forecasts results; forecasts of power production, runoff, snow cover and precipitation were compared for the year of 2019 with actual data using the excel sheet. By interpreting the statistical analysis results, several shortcomings of the model were highlighted that cause a decrease in the forecast accuracy. Most ... |
format |
Other/Unknown Material |
author |
Al-Qes, Amer |
author_facet |
Al-Qes, Amer |
author_sort |
Al-Qes, Amer |
title |
Optimisation of Run of River Production Forecasting Using Aiolos Forecast Studio |
title_short |
Optimisation of Run of River Production Forecasting Using Aiolos Forecast Studio |
title_full |
Optimisation of Run of River Production Forecasting Using Aiolos Forecast Studio |
title_fullStr |
Optimisation of Run of River Production Forecasting Using Aiolos Forecast Studio |
title_full_unstemmed |
Optimisation of Run of River Production Forecasting Using Aiolos Forecast Studio |
title_sort |
optimisation of run of river production forecasting using aiolos forecast studio |
publisher |
Lunds universitet/Avdelningen för Teknisk vattenresurslära |
publishDate |
2020 |
url |
http://lup.lub.lu.se/student-papers/record/9020357 |
geographic |
Norway |
geographic_facet |
Norway |
genre |
Iceland |
genre_facet |
Iceland |
op_relation |
http://lup.lub.lu.se/student-papers/record/9020357 ISSN: 1101-9824 |
_version_ |
1772815931991392256 |