Wavelet Decomposition and Convolutional LSTM Networks Based Improved Deep Learning Model for Solar Irradiance Forecasting
Solar photovoltaic (PV) power forecasting has become an important issue with regard to the power grid in terms of the effective integration of large-scale PV plants. As the main influence factor of PV power generation, solar irradiance and its accurate forecasting are the prerequisite for solar PV p...
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| Format: | Article |
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MDPI AG
2018-08-01
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| Series: | Applied Sciences |
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| Online Access: | http://www.mdpi.com/2076-3417/8/8/1286 |
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| author | Fei Wang Yili Yu Zhanyao Zhang Jie Li Zhao Zhen Kangping Li |
| author_facet | Fei Wang Yili Yu Zhanyao Zhang Jie Li Zhao Zhen Kangping Li |
| author_sort | Fei Wang |
| collection | DOAJ |
| description | Solar photovoltaic (PV) power forecasting has become an important issue with regard to the power grid in terms of the effective integration of large-scale PV plants. As the main influence factor of PV power generation, solar irradiance and its accurate forecasting are the prerequisite for solar PV power forecasting. However, previous forecasting approaches using manual feature extraction (MFE), traditional modeling and single deep learning (DL) models could not satisfy the performance requirements in partial scenarios with complex fluctuations. Therefore, an improved DL model based on wavelet decomposition (WD), the Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) is proposed for day-ahead solar irradiance forecasting. Given the high dependency of solar irradiance on weather status, the proposed model is individually established under four general weather type (i.e., sunny, cloudy, rainy and heavy rainy). For certain weather types, the raw solar irradiance sequence is decomposed into several subsequences via discrete wavelet transformation. Then each subsequence is fed into the CNN based local feature extractor to automatically learn the abstract feature representation from the raw subsequence data. Since the extracted features of each subsequence are also time series data, they are individually transported to LSTM to construct the subsequence forecasting model. In the end, the final solar irradiance forecasting results under certain weather types are obtained via the wavelet reconstruction of these forecasted subsequences. This case study further verifies the enhanced forecasting accuracy of our proposed method via a comparison with traditional and single DL models. |
| first_indexed | 2024-12-11T06:10:53Z |
| format | Article |
| id | doaj.art-b84ec8d57c3940929421b10859f8cfe1 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-12-11T06:10:53Z |
| publishDate | 2018-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-b84ec8d57c3940929421b10859f8cfe12022-12-22T01:18:07ZengMDPI AGApplied Sciences2076-34172018-08-0188128610.3390/app8081286app8081286Wavelet Decomposition and Convolutional LSTM Networks Based Improved Deep Learning Model for Solar Irradiance ForecastingFei Wang0Yili Yu1Zhanyao Zhang2Jie Li3Zhao Zhen4Kangping Li5State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Baoding 071003, ChinaDepartment of Electrical Engineering, North China Electric Power University, Baoding 071003, ChinaDepartment of Electrical Engineering, North China Electric Power University, Baoding 071003, ChinaDepartment of Electrical Engineering, North China Electric Power University, Baoding 071003, ChinaDepartment of Electrical Engineering, North China Electric Power University, Baoding 071003, ChinaDepartment of Electrical Engineering, North China Electric Power University, Baoding 071003, ChinaSolar photovoltaic (PV) power forecasting has become an important issue with regard to the power grid in terms of the effective integration of large-scale PV plants. As the main influence factor of PV power generation, solar irradiance and its accurate forecasting are the prerequisite for solar PV power forecasting. However, previous forecasting approaches using manual feature extraction (MFE), traditional modeling and single deep learning (DL) models could not satisfy the performance requirements in partial scenarios with complex fluctuations. Therefore, an improved DL model based on wavelet decomposition (WD), the Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) is proposed for day-ahead solar irradiance forecasting. Given the high dependency of solar irradiance on weather status, the proposed model is individually established under four general weather type (i.e., sunny, cloudy, rainy and heavy rainy). For certain weather types, the raw solar irradiance sequence is decomposed into several subsequences via discrete wavelet transformation. Then each subsequence is fed into the CNN based local feature extractor to automatically learn the abstract feature representation from the raw subsequence data. Since the extracted features of each subsequence are also time series data, they are individually transported to LSTM to construct the subsequence forecasting model. In the end, the final solar irradiance forecasting results under certain weather types are obtained via the wavelet reconstruction of these forecasted subsequences. This case study further verifies the enhanced forecasting accuracy of our proposed method via a comparison with traditional and single DL models.http://www.mdpi.com/2076-3417/8/8/1286solar irradiance forecastingwavelet decompositionconvolutional neural networkrecurrent neural networklong short term memory |
| spellingShingle | Fei Wang Yili Yu Zhanyao Zhang Jie Li Zhao Zhen Kangping Li Wavelet Decomposition and Convolutional LSTM Networks Based Improved Deep Learning Model for Solar Irradiance Forecasting Applied Sciences solar irradiance forecasting wavelet decomposition convolutional neural network recurrent neural network long short term memory |
| title | Wavelet Decomposition and Convolutional LSTM Networks Based Improved Deep Learning Model for Solar Irradiance Forecasting |
| title_full | Wavelet Decomposition and Convolutional LSTM Networks Based Improved Deep Learning Model for Solar Irradiance Forecasting |
| title_fullStr | Wavelet Decomposition and Convolutional LSTM Networks Based Improved Deep Learning Model for Solar Irradiance Forecasting |
| title_full_unstemmed | Wavelet Decomposition and Convolutional LSTM Networks Based Improved Deep Learning Model for Solar Irradiance Forecasting |
| title_short | Wavelet Decomposition and Convolutional LSTM Networks Based Improved Deep Learning Model for Solar Irradiance Forecasting |
| title_sort | wavelet decomposition and convolutional lstm networks based improved deep learning model for solar irradiance forecasting |
| topic | solar irradiance forecasting wavelet decomposition convolutional neural network recurrent neural network long short term memory |
| url | http://www.mdpi.com/2076-3417/8/8/1286 |
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