Short-Term Electrical Load Forecasting in Power Systems Using Deep Learning Techniques
The use of big data in deep neural networks has recently surpassed traditional machine learning techniques in many application areas. The main reasons for the use of deep neural networks are the increase in computational power made possible by graphics processing units and tensor processing units, a...
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Sakarya University
2023-10-01
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| Series: | Sakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi |
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| Online Access: | https://dergipark.org.tr/tr/download/article-file/2975741 |
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| author | Nihat Pamuk |
| author_facet | Nihat Pamuk |
| author_sort | Nihat Pamuk |
| collection | DOAJ |
| description | The use of big data in deep neural networks has recently surpassed traditional machine learning techniques in many application areas. The main reasons for the use of deep neural networks are the increase in computational power made possible by graphics processing units and tensor processing units, and the new algorithms created by recurrent neural networks and CNNs. In addition to traditional machine learning methods, deep neural networks have applications in anticipating electricity load. Using a real dataset for one-step forecasting, this article compares three deep learning algorithms for short-term power load forecasting: LSTM, GRUs, and CNN. The statistics come from the Turkish city of Zonguldak and include hourly electricity usage loads and temperatures over a period of three years, commencing in 2019 and ending in 2021. The mean absolute percentage error is used to compare the performances of the techniques. Forecasts are made for twelve representative months from each season. The main reason for the significant deviations in the forecasts for January, May, September, and December is the presence of religious and national holidays in these months. This was solved by adding the information obtained from religious and national holidays to the modeling. This is not to say that CNNs are not good at capturing long-term dependencies and modeling sequential data. In all experiments, LSTM, GRUs and encoder-decoder LSTM outperformed simple CNN designs. In the future, these new architectural methods can be applied to long- or short-term electric charge predictions and their results can be compared to LSTM, GRUs and their variations. |
| format | Article |
| id | doaj-art-e76cbce6aa69439ba2b955aa429199e8 |
| institution | Kabale University |
| issn | 2147-835X |
| language | English |
| publishDate | 2023-10-01 |
| publisher | Sakarya University |
| record_format | Article |
| series | Sakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi |
| spelling | doaj-art-e76cbce6aa69439ba2b955aa429199e82024-12-23T08:15:40ZengSakarya UniversitySakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi2147-835X2023-10-012751111112110.16984/saufenbilder.125674328Short-Term Electrical Load Forecasting in Power Systems Using Deep Learning TechniquesNihat Pamuk0https://orcid.org/0000-0001-8980-6913Zonguldak Bülent Ecevit ÜniversitesiThe use of big data in deep neural networks has recently surpassed traditional machine learning techniques in many application areas. The main reasons for the use of deep neural networks are the increase in computational power made possible by graphics processing units and tensor processing units, and the new algorithms created by recurrent neural networks and CNNs. In addition to traditional machine learning methods, deep neural networks have applications in anticipating electricity load. Using a real dataset for one-step forecasting, this article compares three deep learning algorithms for short-term power load forecasting: LSTM, GRUs, and CNN. The statistics come from the Turkish city of Zonguldak and include hourly electricity usage loads and temperatures over a period of three years, commencing in 2019 and ending in 2021. The mean absolute percentage error is used to compare the performances of the techniques. Forecasts are made for twelve representative months from each season. The main reason for the significant deviations in the forecasts for January, May, September, and December is the presence of religious and national holidays in these months. This was solved by adding the information obtained from religious and national holidays to the modeling. This is not to say that CNNs are not good at capturing long-term dependencies and modeling sequential data. In all experiments, LSTM, GRUs and encoder-decoder LSTM outperformed simple CNN designs. In the future, these new architectural methods can be applied to long- or short-term electric charge predictions and their results can be compared to LSTM, GRUs and their variations.https://dergipark.org.tr/tr/download/article-file/2975741short-term forecastingelectricity loadgraphical process unitstensor process units |
| spellingShingle | Nihat Pamuk Short-Term Electrical Load Forecasting in Power Systems Using Deep Learning Techniques Sakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi short-term forecasting electricity load graphical process units tensor process units |
| title | Short-Term Electrical Load Forecasting in Power Systems Using Deep Learning Techniques |
| title_full | Short-Term Electrical Load Forecasting in Power Systems Using Deep Learning Techniques |
| title_fullStr | Short-Term Electrical Load Forecasting in Power Systems Using Deep Learning Techniques |
| title_full_unstemmed | Short-Term Electrical Load Forecasting in Power Systems Using Deep Learning Techniques |
| title_short | Short-Term Electrical Load Forecasting in Power Systems Using Deep Learning Techniques |
| title_sort | short term electrical load forecasting in power systems using deep learning techniques |
| topic | short-term forecasting electricity load graphical process units tensor process units |
| url | https://dergipark.org.tr/tr/download/article-file/2975741 |
| work_keys_str_mv | AT nihatpamuk shorttermelectricalloadforecastinginpowersystemsusingdeeplearningtechniques |