Short-Term Energy Consumption Forecasting Analysis Using Different Optimization and Activation Functions with Deep Learning Models
Modelling events that change over time is one of the most difficult problems in data analysis. Forecasting of time-varying electric power values is also an important problem in data analysis. Regression methods, machine learning, and deep learning methods are used to learn different patterns from da...
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2025-06-01
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| author | Mehmet Tahir Ucar Asim Kaygusuz |
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| author_sort | Mehmet Tahir Ucar |
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| description | Modelling events that change over time is one of the most difficult problems in data analysis. Forecasting of time-varying electric power values is also an important problem in data analysis. Regression methods, machine learning, and deep learning methods are used to learn different patterns from data and develop a consumption prediction model. The aim of this study is to determine the most successful models for short-term power consumption prediction with deep learning and to achieve the highest prediction accuracy. In this study, firstly, the data was evaluated and organized with exploratory data analysis (EDA) on a ready dataset and the features of the data were extracted. Studies were carried out on long short-term memory (LSTM), gated recurrent unit (GRU), simple recurrent neural networks (SimpleRNN) and bidirectional long short-term memory (BiLSTM) architectures. First, four architectures were used with 11 different optimization methods. In this study, it was seen that a high success rate of 0.9972 was achieved according to the R<sup>2</sup> score index. In the following, the first study was tried with different epoch numbers. Afterwards, this study was carried out with 264 separate models produced using four architectures, 11 optimization methods, and six activation functions in order. The results of all these studies were obtained according to the root mean square error (RMSE), mean absolute error (MAE), and R<sup>2</sup>_score indexes. The R<sup>2</sup>_score indexes graphs are presented. Finally, the 10 most successful applications are listed. |
| format | Article |
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| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-06-01 |
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| spelling | doaj-art-fac3f5a3e5304563808df6ff2a7ae7322025-08-20T03:26:15ZengMDPI AGApplied Sciences2076-34172025-06-011512683910.3390/app15126839Short-Term Energy Consumption Forecasting Analysis Using Different Optimization and Activation Functions with Deep Learning ModelsMehmet Tahir Ucar0Asim Kaygusuz1Ergani Vocational School, Dicle University, 21280 Diyarbakır, TürkiyeDepartment of Electrical and Electronics Engineering, Engineering Faculty, Inonu University, 44280 Malatya, TürkiyeModelling events that change over time is one of the most difficult problems in data analysis. Forecasting of time-varying electric power values is also an important problem in data analysis. Regression methods, machine learning, and deep learning methods are used to learn different patterns from data and develop a consumption prediction model. The aim of this study is to determine the most successful models for short-term power consumption prediction with deep learning and to achieve the highest prediction accuracy. In this study, firstly, the data was evaluated and organized with exploratory data analysis (EDA) on a ready dataset and the features of the data were extracted. Studies were carried out on long short-term memory (LSTM), gated recurrent unit (GRU), simple recurrent neural networks (SimpleRNN) and bidirectional long short-term memory (BiLSTM) architectures. First, four architectures were used with 11 different optimization methods. In this study, it was seen that a high success rate of 0.9972 was achieved according to the R<sup>2</sup> score index. In the following, the first study was tried with different epoch numbers. Afterwards, this study was carried out with 264 separate models produced using four architectures, 11 optimization methods, and six activation functions in order. The results of all these studies were obtained according to the root mean square error (RMSE), mean absolute error (MAE), and R<sup>2</sup>_score indexes. The R<sup>2</sup>_score indexes graphs are presented. Finally, the 10 most successful applications are listed.https://www.mdpi.com/2076-3417/15/12/6839deep learning modelselectricity consumption predictionexploratory data analysis (EDA)long short-term memory (LSTM)gated recurrent unit (GRU)simple recurrent neural networks (Simple RNN) |
| spellingShingle | Mehmet Tahir Ucar Asim Kaygusuz Short-Term Energy Consumption Forecasting Analysis Using Different Optimization and Activation Functions with Deep Learning Models Applied Sciences deep learning models electricity consumption prediction exploratory data analysis (EDA) long short-term memory (LSTM) gated recurrent unit (GRU) simple recurrent neural networks (Simple RNN) |
| title | Short-Term Energy Consumption Forecasting Analysis Using Different Optimization and Activation Functions with Deep Learning Models |
| title_full | Short-Term Energy Consumption Forecasting Analysis Using Different Optimization and Activation Functions with Deep Learning Models |
| title_fullStr | Short-Term Energy Consumption Forecasting Analysis Using Different Optimization and Activation Functions with Deep Learning Models |
| title_full_unstemmed | Short-Term Energy Consumption Forecasting Analysis Using Different Optimization and Activation Functions with Deep Learning Models |
| title_short | Short-Term Energy Consumption Forecasting Analysis Using Different Optimization and Activation Functions with Deep Learning Models |
| title_sort | short term energy consumption forecasting analysis using different optimization and activation functions with deep learning models |
| topic | deep learning models electricity consumption prediction exploratory data analysis (EDA) long short-term memory (LSTM) gated recurrent unit (GRU) simple recurrent neural networks (Simple RNN) |
| url | https://www.mdpi.com/2076-3417/15/12/6839 |
| work_keys_str_mv | AT mehmettahirucar shorttermenergyconsumptionforecastinganalysisusingdifferentoptimizationandactivationfunctionswithdeeplearningmodels AT asimkaygusuz shorttermenergyconsumptionforecastinganalysisusingdifferentoptimizationandactivationfunctionswithdeeplearningmodels |