Comparison of artificial neural network models of categorized daily electric load
The efficient operation of power systems and future planning, electricity load forecast is very important. Load estimation is based on predicting future electric load by examining past conditions. Short-term load prediction plays a decisive role in the load sharing of power plants. It also allows to...
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| Format: | Article |
| Language: | English |
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Kyrgyz Turkish Manas University
2021-04-01
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| Series: | MANAS: Journal of Engineering |
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| Online Access: | https://dergipark.org.tr/en/download/article-file/1406042 |
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| author | Vildan Evren İlker Ali Ozkan |
| author_facet | Vildan Evren İlker Ali Ozkan |
| author_sort | Vildan Evren |
| collection | DOAJ |
| description | The efficient operation of power systems and future planning, electricity load forecast is very important. Load estimation is based on predicting future electric load by examining past conditions. Short-term load prediction plays a decisive role in the load sharing of power plants. It also allows to overcome shortcomings caused by sudden load increases and power plant losses. Weather conditions are effective in short-term electrical load estimation. Daily or hourly electricity consumption data is generally used for short-term load estimation. In this study, daily electrical energy consumption of Turkey in the four years of data were used. Short-term load prediction modeling has been carried out. In this modeling, past electrical load values and temperature values were used as input, and in order to increase the prediction accuracy, the characteristics of the days were categorized weekly and classified according to the seasons. Different Artificial Neural Network models have been created according to input data, weekly categorization, and season criteria. In the study, mean absolute percentage error values were calculated. Among the models developed with ANN, the best MAPE value was 2.51% and the worst MAPE value was 4.48%. When the season criterion is added, the MAPE value is more successful. |
| format | Article |
| id | doaj-art-ed14bb46c39d41688f03d87253a9c21a |
| institution | Kabale University |
| issn | 1694-7398 |
| language | English |
| publishDate | 2021-04-01 |
| publisher | Kyrgyz Turkish Manas University |
| record_format | Article |
| series | MANAS: Journal of Engineering |
| spelling | doaj-art-ed14bb46c39d41688f03d87253a9c21a2025-02-03T12:07:27ZengKyrgyz Turkish Manas UniversityMANAS: Journal of Engineering1694-73982021-04-019Special 1243410.51354/mjen.8285451437Comparison of artificial neural network models of categorized daily electric loadVildan Evren0https://orcid.org/0000-0003-1654-3731İlker Ali Ozkan1https://orcid.org/0000-0002-5715-1040SELÇUK ÜNİVERSİTESİ, TEKNOLOJİ FAKÜLTESİSELÇUK ÜNİVERSİTESİ, TEKNOLOJİ FAKÜLTESİThe efficient operation of power systems and future planning, electricity load forecast is very important. Load estimation is based on predicting future electric load by examining past conditions. Short-term load prediction plays a decisive role in the load sharing of power plants. It also allows to overcome shortcomings caused by sudden load increases and power plant losses. Weather conditions are effective in short-term electrical load estimation. Daily or hourly electricity consumption data is generally used for short-term load estimation. In this study, daily electrical energy consumption of Turkey in the four years of data were used. Short-term load prediction modeling has been carried out. In this modeling, past electrical load values and temperature values were used as input, and in order to increase the prediction accuracy, the characteristics of the days were categorized weekly and classified according to the seasons. Different Artificial Neural Network models have been created according to input data, weekly categorization, and season criteria. In the study, mean absolute percentage error values were calculated. Among the models developed with ANN, the best MAPE value was 2.51% and the worst MAPE value was 4.48%. When the season criterion is added, the MAPE value is more successful.https://dergipark.org.tr/en/download/article-file/1406042artificial neural networkshort term electric load forecasttime series modelingdaily electric load forecast |
| spellingShingle | Vildan Evren İlker Ali Ozkan Comparison of artificial neural network models of categorized daily electric load MANAS: Journal of Engineering artificial neural network short term electric load forecast time series modeling daily electric load forecast |
| title | Comparison of artificial neural network models of categorized daily electric load |
| title_full | Comparison of artificial neural network models of categorized daily electric load |
| title_fullStr | Comparison of artificial neural network models of categorized daily electric load |
| title_full_unstemmed | Comparison of artificial neural network models of categorized daily electric load |
| title_short | Comparison of artificial neural network models of categorized daily electric load |
| title_sort | comparison of artificial neural network models of categorized daily electric load |
| topic | artificial neural network short term electric load forecast time series modeling daily electric load forecast |
| url | https://dergipark.org.tr/en/download/article-file/1406042 |
| work_keys_str_mv | AT vildanevren comparisonofartificialneuralnetworkmodelsofcategorizeddailyelectricload AT ilkeraliozkan comparisonofartificialneuralnetworkmodelsofcategorizeddailyelectricload |