Analysis of Different Neural Networks and a New Architecture for Short-Term Load Forecasting
Short-term load forecasting (STLF) has been widely studied because it plays a very important role in improving the economy and security of electric system operations. Many types of neural networks have been successfully used for STLF. In most of these methods, common neural networks were used, but w...
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MDPI AG
2019-04-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/12/8/1433 |
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doaj-457c1a94ce0449dea64ca28aca348ea12020-11-25T02:18:08ZengMDPI AGEnergies1996-10732019-04-01128143310.3390/en12081433en12081433Analysis of Different Neural Networks and a New Architecture for Short-Term Load ForecastingLintao Yang0Honggeng Yang1College of Electrical Engineering and Information Technology, Sichuan University, Chengdu 610065, ChinaCollege of Electrical Engineering and Information Technology, Sichuan University, Chengdu 610065, ChinaShort-term load forecasting (STLF) has been widely studied because it plays a very important role in improving the economy and security of electric system operations. Many types of neural networks have been successfully used for STLF. In most of these methods, common neural networks were used, but without a systematic comparative analysis. In this paper, we first compare the most frequently used neural networks’ performance on the load dataset from the State Grid Sichuan Electric Power Company (China). Then, considering the current neural networks’ disadvantages, we propose a new architecture called a gate-recurrent neural network (RNN) based on an RNN for STLF. By evaluating all the methods on our dataset, the results demonstrate that the performance of different neural network methods are related to the data time scale, and our proposed method is more accurate on a much shorter time scale, particularly when the time scale is smaller than 20 min.https://www.mdpi.com/1996-1073/12/8/1433short-term load forecastingback-propagation neural networkrecurrent neural networklong-short term memorygate-recurrent neural network |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Lintao Yang Honggeng Yang |
| spellingShingle |
Lintao Yang Honggeng Yang Analysis of Different Neural Networks and a New Architecture for Short-Term Load Forecasting Energies short-term load forecasting back-propagation neural network recurrent neural network long-short term memory gate-recurrent neural network |
| author_facet |
Lintao Yang Honggeng Yang |
| author_sort |
Lintao Yang |
| title |
Analysis of Different Neural Networks and a New Architecture for Short-Term Load Forecasting |
| title_short |
Analysis of Different Neural Networks and a New Architecture for Short-Term Load Forecasting |
| title_full |
Analysis of Different Neural Networks and a New Architecture for Short-Term Load Forecasting |
| title_fullStr |
Analysis of Different Neural Networks and a New Architecture for Short-Term Load Forecasting |
| title_full_unstemmed |
Analysis of Different Neural Networks and a New Architecture for Short-Term Load Forecasting |
| title_sort |
analysis of different neural networks and a new architecture for short-term load forecasting |
| publisher |
MDPI AG |
| series |
Energies |
| issn |
1996-1073 |
| publishDate |
2019-04-01 |
| description |
Short-term load forecasting (STLF) has been widely studied because it plays a very important role in improving the economy and security of electric system operations. Many types of neural networks have been successfully used for STLF. In most of these methods, common neural networks were used, but without a systematic comparative analysis. In this paper, we first compare the most frequently used neural networks’ performance on the load dataset from the State Grid Sichuan Electric Power Company (China). Then, considering the current neural networks’ disadvantages, we propose a new architecture called a gate-recurrent neural network (RNN) based on an RNN for STLF. By evaluating all the methods on our dataset, the results demonstrate that the performance of different neural network methods are related to the data time scale, and our proposed method is more accurate on a much shorter time scale, particularly when the time scale is smaller than 20 min. |
| topic |
short-term load forecasting back-propagation neural network recurrent neural network long-short term memory gate-recurrent neural network |
| url |
https://www.mdpi.com/1996-1073/12/8/1433 |
| work_keys_str_mv |
AT lintaoyang analysisofdifferentneuralnetworksandanewarchitectureforshorttermloadforecasting AT honggengyang analysisofdifferentneuralnetworksandanewarchitectureforshorttermloadforecasting |
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