Journal
APPLIED SCIENCES-BASEL
Volume 8, Issue 8, Pages -Publisher
MDPI
DOI: 10.3390/app8081286
Keywords
solar irradiance forecasting; wavelet decomposition; convolutional neural network; recurrent neural network; long short term memory
Categories
Funding
- National Key R&D Program of China [2018YFB0904200]
- National Natural Science Foundation of China [51577067]
- Beijing Natural Science Foundation of China [3162033]
- Hebei Natural Science Foundation of China [E2015502060]
- State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources [LAPS18008]
- Science and Technology Project of State Grid Corporation of China (SGCC) [NY7117020]
- Open Fund of State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems (China Electric Power Research Institute) [5242001600FB]
- Fundamental Research Funds for the Central Universities [2018QN077]
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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.
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