ELECTRICITY PRICE FORECASTING IN TURKISH DAY-AHEAD MARKET VIA DEEP LEARNING TECHNIQUES
Day-Ahead Market offers electricity market participants the opportunity to trade electricity one day ahead of real-time. For each hour, a separate Market Clearing Price is created in Day-Ahead Market. This study aims to predict the hourly Market Clearing Price using deep learning techniques. In this...
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Mehmet Akif Ersoy University
2022-07-01
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| Series: | Mehmet Akif Ersoy Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi |
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| Online Access: | https://dergipark.org.tr/en/download/article-file/2349722 |
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| author | Arif Arifoğlu Tuğrul Kandemir |
| author_facet | Arif Arifoğlu Tuğrul Kandemir |
| author_sort | Arif Arifoğlu |
| collection | DOAJ |
| description | Day-Ahead Market offers electricity market participants the opportunity to trade electricity one day ahead of real-time. For each hour, a separate Market Clearing Price is created in Day-Ahead Market. This study aims to predict the hourly Market Clearing Price using deep learning techniques. In this context, 24-hour Market Clearing Prices were forecasted with MLP, CNN, LSTM, and GRU. LSTM had the best average forecasting performance with an 8.15 MAPE value, according to the results obtained. MLP followed the LSTM with 8.44 MAPE, GRU with 8.72 MAPE, and CNN with 9.27 MAPE. In the study, the provinces where the power plants producing with renewable resources are dense were selected for meteorological variables. It is expected that the trend towards electricity generation with renewable resources will increase in the future. In this context, it is thought important for market participants to consider the factors that may affect the production with these resources in the electricity price forecasting. |
| format | Article |
| id | doaj-art-82171646b9774e4fb3f5d1bc0a2df992 |
| institution | Kabale University |
| issn | 2149-1658 |
| language | English |
| publishDate | 2022-07-01 |
| publisher | Mehmet Akif Ersoy University |
| record_format | Article |
| series | Mehmet Akif Ersoy Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi |
| spelling | doaj-art-82171646b9774e4fb3f5d1bc0a2df9922025-01-27T14:02:42ZengMehmet Akif Ersoy UniversityMehmet Akif Ersoy Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi2149-16582022-07-01921433145810.30798/makuiibf.1097686273ELECTRICITY PRICE FORECASTING IN TURKISH DAY-AHEAD MARKET VIA DEEP LEARNING TECHNIQUESArif Arifoğlu0https://orcid.org/0000-0003-3361-6760Tuğrul Kandemir1https://orcid.org/0000-0002-3544-7422AFYON KOCATEPE ÜNİVERSİTESİ, İKTİSADİ VE İDARİ BİLİMLER FAKÜLTESİ, İŞLETME BÖLÜMÜAFYON KOCATEPE ÜNİVERSİTESİ, İKTİSADİ VE İDARİ BİLİMLER FAKÜLTESİ, İŞLETME BÖLÜMÜDay-Ahead Market offers electricity market participants the opportunity to trade electricity one day ahead of real-time. For each hour, a separate Market Clearing Price is created in Day-Ahead Market. This study aims to predict the hourly Market Clearing Price using deep learning techniques. In this context, 24-hour Market Clearing Prices were forecasted with MLP, CNN, LSTM, and GRU. LSTM had the best average forecasting performance with an 8.15 MAPE value, according to the results obtained. MLP followed the LSTM with 8.44 MAPE, GRU with 8.72 MAPE, and CNN with 9.27 MAPE. In the study, the provinces where the power plants producing with renewable resources are dense were selected for meteorological variables. It is expected that the trend towards electricity generation with renewable resources will increase in the future. In this context, it is thought important for market participants to consider the factors that may affect the production with these resources in the electricity price forecasting.https://dergipark.org.tr/en/download/article-file/2349722gün öncesi piyasasıfiyat tahminipiyasa takas fiyatıderin öğrenmeday-ahead marketprice forecastingmarket clearing pricedeep learning |
| spellingShingle | Arif Arifoğlu Tuğrul Kandemir ELECTRICITY PRICE FORECASTING IN TURKISH DAY-AHEAD MARKET VIA DEEP LEARNING TECHNIQUES Mehmet Akif Ersoy Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi gün öncesi piyasası fiyat tahmini piyasa takas fiyatı derin öğrenme day-ahead market price forecasting market clearing price deep learning |
| title | ELECTRICITY PRICE FORECASTING IN TURKISH DAY-AHEAD MARKET VIA DEEP LEARNING TECHNIQUES |
| title_full | ELECTRICITY PRICE FORECASTING IN TURKISH DAY-AHEAD MARKET VIA DEEP LEARNING TECHNIQUES |
| title_fullStr | ELECTRICITY PRICE FORECASTING IN TURKISH DAY-AHEAD MARKET VIA DEEP LEARNING TECHNIQUES |
| title_full_unstemmed | ELECTRICITY PRICE FORECASTING IN TURKISH DAY-AHEAD MARKET VIA DEEP LEARNING TECHNIQUES |
| title_short | ELECTRICITY PRICE FORECASTING IN TURKISH DAY-AHEAD MARKET VIA DEEP LEARNING TECHNIQUES |
| title_sort | electricity price forecasting in turkish day ahead market via deep learning techniques |
| topic | gün öncesi piyasası fiyat tahmini piyasa takas fiyatı derin öğrenme day-ahead market price forecasting market clearing price deep learning |
| url | https://dergipark.org.tr/en/download/article-file/2349722 |
| work_keys_str_mv | AT arifarifoglu electricitypriceforecastinginturkishdayaheadmarketviadeeplearningtechniques AT tugrulkandemir electricitypriceforecastinginturkishdayaheadmarketviadeeplearningtechniques |