A Deep Learning-Based Dual-Scale Hybrid Model for Ultra-Short-Term Photovoltaic Power Forecasting
Abstract
:1. Introduction
- (1)
- The incorporation of feature decomposition and reconstruction methods serves as the cornerstone of the research. Specifically, the novel AI_VMD algorithm, an iteration beyond the standard VMD decomposition, employs the Akaike information criterion to dynamically select optimal parameter settings responsive to the intrinsic properties of the dataset. This approach effectively diminishes the inherent instability and nonlinearity in raw photovoltaic data, which, in turn, bolsters forecasting precision. The methodology entails the generation of input features and the extraction of high-level features to resolve the challenge of information loss pertinent to the forecasting process.
- (2)
- The HS_CNN model introduced in this investigation addresses the inherent deficiency of conventional CNN models in extracting information across multiple time scales. Unlike the traditional single-channel CNN framework, the HS_CNN model is distinguished by its dual-channel architecture. The first channel, with an expansion rate of 2, is designed to capture the intricate details within the dataset, whereas the second channel employs expansion rates of 2 and 4, facilitating the extraction of the features associated with long-term dependencies. This dual-channel approach allows the model to adeptly handle datasets of varying time scales, significantly enhancing its feature representation capabilities.
- (3)
- The proposed method is concurrently utilized for ultra-short-term PV power prediction on two distinct time scales, demonstrating high forecasting accuracy.
- (4)
- The experimental results were assessed utilizing a deep learning model to analyze seasonality. The method has been demonstrated to exhibit high performance.
2. Materials and Methods
2.1. Basic Models and Methods
2.1.1. Convolutional Neural Network—Long Short-Term Memory Network
2.1.2. Attention Mechanism
2.2. Proposed Model
2.2.1. Akaike Information Criterion Variational Mode Decomposition
2.2.2. HS_CNN-BiLSTM-A
2.3. Performance Evaluation Metrics
3. Case Study
3.1. Dataset Description
3.2. Screening of Impact Factors
3.3. Value Selection for VMD Decomposition
3.4. PV Power Forecasting 15 min in Advance
3.4.1. Ablation Experiment
- (1)
- Comparison of forecasts effects of AI_VMD-HS_CNN-BiLSTM-A decomposition noise reduction models
- (2)
- Comparison of forecasts results based on HS_CNN-BiLSTM-A models
3.4.2. Baseline Model Comparison
3.5. PV Power Forecasting 1 h in Advance
3.5.1. Ablation Experiment
- (1)
- Comparison of forecasts effect of AI_VMD-HS_CNN-BiLSTM-A decomposition noise reduction model
- (2)
- Comparison of forecasts results based on HS_CNN-BiLSTM-A model
3.5.2. Baseline Model Comparison
4. Study Limitations and Future Research Directions
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Wu, H.; Dai, D.; Zhao, C. IEA Releases New Version of 2050 Net-Zero Emissions Roadmap. Foreign Nucl. News 2021, 6, 17–22. [Google Scholar]
- Zhao, J.-J.; Yin, Y.-P.; Cui, J.Y. How can China’s photovoltaic continue to lead the world? Global Times, 28 June 2023. [Google Scholar]
- Shi, R.; Gao, P. China leads the world in renewable energy development. Ecol. Econ. 2023, 39, 9–12. [Google Scholar]
- Tian, J.; Ooka, R.; Lee, D. Multi-scale solar radiation and photovoltaic power forecasting with machine learning algorithms in urban environment: A state-of-the-art review. J. Clean. Prod. 2023, 426, 139040. [Google Scholar] [CrossRef]
- Shang, Y.; Shang, Y.; Yu, H.; Shao, Z.; Jian, L. Achieving efficient and adaptable dispatching for vehicle-to-grid using distributed edge computing and attention-based LSTM. IEEE Trans. Ind. Inform. 2021, 18, 6915–6926. [Google Scholar] [CrossRef]
- Antonanzas, J.; Osorio, N.; Escobar, R.; Urraca, R.; Martinez-De-Pison, F.J.; Antonanzas-Torres, F. Review of photovoltaic power forecasting. Sol. Energy 2016, 136, 78–111. [Google Scholar] [CrossRef]
- Barbieri, F.; Rajakaruna, S.; Ghosh, A. Very short-term photovoltaic power forecasting with cloud modeling: A review. Renew. Sustain. Energy Rev. 2017, 75, 242–263. [Google Scholar] [CrossRef]
- Dong, C.; Wang, Z.; Bai, J.; Jiang, J.; Wang, B.; Liu, G. A review of ultra-short-term prediction methods for photovoltaic power generation. High Volt. Technol. 2023, 49, 2938–2951. [Google Scholar] [CrossRef]
- Bo, Y.; Xia, Y.; Wei, W. Ultra-short-term optimal dispatch for EH-IES considering uncertainty of delay in the distribution network. Int. J. Electr. Power Energy Syst. 2022, 141, 108214. [Google Scholar] [CrossRef]
- Lai, C.W.; Li, J.H.; Chen, B.; Huang, Y.; Wei, S. A review of research on photovoltaic power output prediction techniques. J. Electrotechnol. 2019, 34, 1201–1217. [Google Scholar] [CrossRef]
- He, Y.; Gao, Q.; Jin, Y.; Liu, F. Short-term photovoltaic power forecasting method based on convolutional neural network. Energy Rep. 2022, 8, 54–62. [Google Scholar] [CrossRef]
- Khan, Z.A.; Hussain, T.; Baik, S.W. Dual stream network with attention mechanism for photovoltaic power forecasting. Appl. Energy 2023, 338, 120916. [Google Scholar] [CrossRef]
- Korkmaz, D. SolarNet: A hybrid reliable model based on convolutional neural network and variational mode decomposition for hourly photovoltaic power forecasting. Appl. Energy 2021, 300, 117410. [Google Scholar] [CrossRef]
- Wang, X.; Ma, W. A hybrid deep learning model with an optimal strategy based on improved VMD and transformer for short-term photovoltaic power forecasting. Energy 2024, 295, 131071. [Google Scholar] [CrossRef]
- Li, M.; Wang, W.; He, Y.; Wang, Q. Deep learning model for short-term photovoltaic power forecasting based on variational mode decomposition and similar day clustering. Comput. Electr. Eng. 2024, 115, 109116. [Google Scholar] [CrossRef]
- Hou, Z.; Zhang, Y.; Liu, Q.; Ye, X. A hybrid machine learning forecasting model for photovoltaic power. Energy Rep. 2024, 11, 5125–5138. [Google Scholar] [CrossRef]
- Ferkous, K.; Guermoui, M.; Menakh, S.; Bellaour, A.; Boulmaiz, T. A novel learning approach for short-term photovoltaic power forecasting—A review and case studies. Eng. Appl. Artif. Intell. 2024, 133, 108502. [Google Scholar] [CrossRef]
- Liu, Y.; Guan, L.; Hou, C.; Han, H.; Liu, Z.; Sun, Y.; Zheng, M. Wind power short-term prediction based on LSTM and discrete wavelet transform. Appl. Sci. 2019, 9, 1108. [Google Scholar] [CrossRef]
- Li, Y.; Wu, H.; Liu, H. Multi-step wind speed forecasting using EWT decomposition, LSTM principal computing, RELM subordinate computing and IEWT reconstruction. Energy Convers. Manag. 2018, 167, 203–219. [Google Scholar] [CrossRef]
- Jia, P.; Zhang, H.; Liu, X.; Gong, X. Short-term photovoltaic power forecasting based on VMD and ISSA-GRU. IEEE Access 2021, 9, 105939–105950. [Google Scholar] [CrossRef]
- Jung, Y.; Jung, J.; Kim, B.; Han, S. Long short-term memory recurrent neural network for modeling temporal patterns in long-term power forecasting for solar PV facilities: Case study of South Korea. J. Clean. Prod. 2020, 250, 119476. [Google Scholar] [CrossRef]
- Woo, S.; Park, J.; Lee, J.Y.; Kweon, I.S. Cbam: Convolutional block attention module. In Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany, 8–14 September 2018; pp. 3–19. [Google Scholar] [CrossRef]
- Niu, D.; Yu, M.; Sun, L.; Gao, T.; Wang, K. Short-term multi-energy load forecasting for integratedenergy systems based on CNN-BiGRU optimized by attention mechanism. Appl. Energy 2022, 313, 118801. [Google Scholar] [CrossRef]
- Wang, L.; Liu, Y.; Li, T.; Xie, X.; Chang, C. Short-term PV power prediction based on optimized VMD and LSTM. IEEE Access 2020, 8, 165849–165862. [Google Scholar] [CrossRef]
- Zang, H.; Cheng, L.; Ding, T.; Cheung, K.W.; Liang, Z.; Wei, Z.; Sun, G. Hybrid method for short-term photovoltaic power forecasting based on deep convolutional neural network. IET Gener. Transm. Distrib. 2018, 12, 4557–4567. [Google Scholar] [CrossRef]
- Wu, Z.; Baartman, J.E.; Nunes, J.P.; López-Vicente, M. Intra-annual sediment dynamic assessment in the Wei River Basin, China, using the AIC functional-structural connectivity index. Ecol. Indic. 2023, 146, 109775. [Google Scholar] [CrossRef]
- Zhao, L.; Li, Z.; Qu, L. Forecasting of Beijing PM2.5 with a hybrid ARIMA model based on integrated AIC and improved GS fixed-order methods and seasonal decomposition. Heliyon 2022, 8, e12239. [Google Scholar] [CrossRef] [PubMed]
- Yu, M.; Niu, D.; Wang, K.; Du, R.; Yu, X.; Sun, L.; Wang, F. Short-term photovoltaic power point-interval forecasting based on double-layer decomposition and WOA-BiLSTM-Attention and considering weather classification. Energy 2023, 275, 127348. [Google Scholar] [CrossRef]
- Liu, W.; Mao, Z. Short-term photovoltaic power forecasting with feature extraction and attention mechanisms. Renew. Energy 2024, 226, 120437. [Google Scholar] [CrossRef]
- DKASC. Alice Springs 2 Eco-Kinetics Mono-Si Dual 2010; DKA Solar Centre: Connellan, NT, Australia, 2010. [Google Scholar]
- Ren, X.; Zhang, F.; Zhu, H.; Liu, Y. Quad-kernel deep convolutional neural network for intra-hour photovoltaic power forecasting. Appl. Energy 2022, 323, 119682. [Google Scholar] [CrossRef]
- Sabri, N.M.; El Hassouni, M. Accurate photovoltaic power prediction models based on deep convolutional neural networks and gated recurrent units. Energy Sources Part A Recovery Util. Environ. Eff. 2022, 44, 6303–6320. [Google Scholar] [CrossRef]
- Lim, S.-C.; Huh, J.-H.; Hong, S.-H.; Park, C.-Y.; Kim, J.-C. Solar power forecasting using CNN-LSTM hybrid model. Energies 2022, 15, 8233. [Google Scholar] [CrossRef]
- Xiao, Y.; Zou, C.; Chi, H.; Fang, R. Boosted GRU model for short-term forecasting of wind power with feature-weighted principal component analysis. Energy 2023, 267, 126503. [Google Scholar] [CrossRef]
- Chen, H.; Chang, X. Photovoltaic power prediction of LSTM model based on Pearson feature selection. Energy Rep. 2021, 7, 1047–1054. [Google Scholar] [CrossRef]
- Han, H.; Bazai, S.U.; Bhatti, M.A.; Basit, A.; Wahid, A.; Bhatti, U.A.; Ghadi, Y.Y.; Algarni, A. Hybrid Climate Forecasting: Variational Mode Decomposition and Convolutional Neural Network with Long-Term Short Memory. Pol. J. Environ. Stud. 2024, 33, 1121–1134. [Google Scholar] [CrossRef] [PubMed]
- Zhao, Z.; Yun, S.; Jia, L.; Guo, J.; Meng, Y.; He, N.; Li, X.; Shi, J.; Yang, L. Hybrid VMD-CNN-GRU-based model for short-term forecasting of wind power considering spatio-temporal features. Eng. Appl. Artif. Intell. 2023, 121, 105982. [Google Scholar] [CrossRef]
- Huang, C.J.; Kuo, P.H. Multiple-input deep convolutional neural network model for short-term photovoltaic power forecasting. IEEE Access 2019, 7, 74822–74834. [Google Scholar] [CrossRef]
Layer | |
---|---|
conv1d_channel1 | filters = 64, kernel_size = 3, activation = ‘relu’, dilation_rate = 2, padding = ‘same’ |
conv1d_channel2 | filters = 64, kernel_size = 3, activation = ‘relu’, dilation_rate = (2, 4), padding = ‘same’ |
merged_channels | concatenate([conv1d_channel1, conv1d_channel2] |
flatten | Flatten() |
Fully connected | Dense(128, activation = ‘sigmoid’) |
Output | t + 1 |
Modes | K = 3 | K = 4 | K = 5 | K = 6 | K = 7 |
---|---|---|---|---|---|
IMF 1 | 3.69 | 3.66 | 3.52 | 3.47 | 3.43 |
IMF 2 | 730.03 | 730.01 | 729.58 | 729.55 | 729.55 |
IMF 3 | 1460.39 | 1460.02 | 1044.47 | 1018.61 | 904.68 |
IMF 4 | 2211.58 | 1460.43 | 1460.25 | 1460.19 | |
IMF 5 | 2213.27 | 1463.11 | 1461.46 | ||
IMF 6 | 2218.95 | 1466.57 | |||
IMF 7 | 2222.11 |
Estimation | K = 3 | K = 4 | K = 5 | K = 6 | K = 7 | AI_VMD |
---|---|---|---|---|---|---|
MAE | 0.575 | 0.497 | 0.459 | 0.503 | 0.619 | 0.441 |
MSE | 1.122 | 0.945 | 0.904 | 0.984 | 1.128 | 0.899 |
Model | Abbreviations | Superparameter Settings |
---|---|---|
HS_CNN-BiLSTM-A | #1 | batch_size = 128, learning rate = 0.01 conv1 = 64, conv2 = 64, dilation_rate1 = 2, dilation_rate2 = 4, hiddenunits1 = 128, hiddenunits2 = 64 |
AI_VMD-BiLSTM-A | #2 | batch_size = 128, learning rate = 0.01 hiddenunits1 = 128, hiddenunits2 = 64 |
AI_VMD-CNN-BiLSTM-A (dilation rate = 2) | #3 | batch_size = 128, learning rate = 0.01 conv1 = 64, conv2 = 64, dilation_rate = 2, hiddenunits1 = 128, hiddenunits2 = 64 |
AI_VMD-CNN-BiLSTM-A (dilation rate = 4) | #4 | batch_size = 128, leaning rate = 0.01 conv1 = 64, conv2 = 64, dilation_rate = 4, hiddenunits1 = 128, hidden units 2 = 64 |
AI_VMD-HS_CNN-BiLSTM-A | #5 | batch_size = 128, leaning rate = 0.01 conv1 = 64, conv2 = 64, dilation_rate1 = 2, dilation_rate 2 = 4, hiddenunits1 = 128, hidden units 2 = 64 |
Season | Estimation | #1 | #2 | #3 | #4 | #5 |
---|---|---|---|---|---|---|
Spring | MAE | 0.482 | 0.383 | 0.431 | 0.392 | 0.365 |
MSE | 0.760 | 0.767 | 0.792 | 0.792 | 0.762 | |
R2 | 0.953 | 0.974 | 0.979 | 0.971 | 0.980 | |
Summer | MAE | 0.528 | 0.476 | 0.445 | 0.416 | 0.378 |
MSE | 1.059 | 1.005 | 0.962 | 0.862 | 0.862 | |
R2 | 0.957 | 0.960 | 0.961 | 0.965 | 0.967 | |
Fall | MAE | 0.387 | 0.363 | 0.364 | 0.355 | 0.349 |
MSE | 1.286 | 0.868 | 0.656 | 0.681 | 0.650 | |
R2 | 0.968 | 0.979 | 0.979 | 0.978 | 0.980 | |
Winter | MAE | 1.037 | 0.903 | 0.886 | 0.863 | 0.847 |
MSE | 2.795 | 2.764 | 2.721 | 2.421 | 2.280 | |
R2 | 0.851 | 0.883 | 0.895 | 0.906 | 0.908 |
Season | Estimation | AI_VMD-HS_CNN-BiLSTM-A | CNN-GRU | CNN-LSTM | GRU | LSTM | VMD-CNN-GRU | VMD-CNN-LSTM | CNN |
---|---|---|---|---|---|---|---|---|---|
Spring | MAE | 0.365 | 0.422 | 0.467 | 0.440 | 0.441 | 0.413 | 0.453 | 0.939 |
MSE | 0.762 | 0.987 | 1.022 | 1.028 | 0.899 | 0.867 | 0.967 | 1.904 | |
R2 | 0.980 | 0.974 | 0.973 | 0.973 | 0.977 | 0.977 | 0.967 | 0.874 | |
Summer | MAE | 0.378 | 0.510 | 0.410 | 0.436 | 0.467 | 0.430 | 0.440 | 0.720 |
MSE | 0.862 | 1.004 | 0.984 | 0.807 | 0.946 | 0.944 | 0.912 | 1.533 | |
R2 | 0.967 | 0.960 | 0.960 | 0.967 | 0.950 | 0.967 | 0.960 | 0.939 | |
Fall | MAE | 0.349 | 0.344 | 0.390 | 0.565 | 0.567 | 0.346 | 0.386 | 0.954 |
MSE | 0.650 | 0.554 | 0.670 | 0.845 | 0.894 | 0.590 | 0.554 | 1.196 | |
R2 | 0.980 | 0.979 | 0.979 | 0.956 | 0.944 | 0.979 | 0.976 | 0.931 | |
Winter | MAE | 0.847 | 1.173 | 1.489 | 1.641 | 1.688 | 0.993 | 0.946 | 2.098 |
MSE | 2.280 | 2.154 | 2.491 | 2.663 | 2.999 | 2.298 | 2.174 | 3.857 | |
R2 | 0.908 | 0.841 | 0.840 | 0.829 | 0.835 | 0.863 | 0.871 | 0.836 |
Model | Abbreviations | Superparameter Settings |
---|---|---|
HS_CNN-BiLSTM-A | #1 | batch_size = 128, leaning rate = 0.01 conv1 = 64, conv2 = 64, dilation_rate1 = 2, dilation_rate 2 = 4, hiddenunits1 = 128, hidden units 2 = 64 |
AI_VMD-BiLSTM-A | #2 | batch_size = 128, leaning rate = 0.01 hiddenunits1 = 128, hidden units 2 = 64 |
AI_VMD-CNN-BiLSTM-A (dilation rate = 2) | #3 | batch_size = 128, leaning rate = 0.01 conv1 = 64, conv2 = 64, dilation_rate = 2, hiddenunits1 = 128, hidden units 2 = 64 |
AI_VMD-CNN-BiLSTM-A (dilation rate = 4) | #4 | batch_size = 128, leaning rate = 0.01 conv1 = 64, conv2 = 64, dilation_rate = 4, hiddenunits1 = 128, hidden units 2 = 64 |
AI_VMD-HS_CNN-BiLSTM-A | #5 | batch_size = 128, leaning rate = 0.01 conv1 = 64, conv2 = 64, dilation_rate1 = 2, dilation_rate2 = 4, hiddenunits1 = 128, hidden units 2 = 64 |
Model | MAE (KW) | MSE (KW) | R2 |
---|---|---|---|
#1 | 0.591 | 1.488 | 0.947 |
#2 | 0.529 | 1.472 | 0.947 |
#3 | 0.545 | 1.506 | 0.946 |
#4 | 0.533 | 1.460 | 0.948 |
#5 | 0.492 | 1.335 | 0.952 |
Model | MAE (KW) | MSE (KW) | R2 |
---|---|---|---|
AI_VMD-HS_CNN-BiLSTM-A | 0.492 | 1.335 | 0.952 |
CNN-GRU [32] | 0.574 | 1.572 | 0.944 |
CNN-LSTM [33] | 0.523 | 1.538 | 0.945 |
GRU [34] | 0.567 | 1.599 | 0.943 |
LSTM [35] | 0.505 | 1.406 | 0.950 |
VMD-CNN-GRU [36] | 0.562 | 1.477 | 0.946 |
VMD-CNN-LSTM [37] | 0.515 | 1.505 | 0.945 |
CNN [38] | 1.106 | 3.272 | 0.884 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Zhang, Y.; Ren, X.; Zhang, F.; Liu, Y.; Li, J. A Deep Learning-Based Dual-Scale Hybrid Model for Ultra-Short-Term Photovoltaic Power Forecasting. Sustainability 2024, 16, 7340. https://doi.org/10.3390/su16177340
Zhang Y, Ren X, Zhang F, Liu Y, Li J. A Deep Learning-Based Dual-Scale Hybrid Model for Ultra-Short-Term Photovoltaic Power Forecasting. Sustainability. 2024; 16(17):7340. https://doi.org/10.3390/su16177340
Chicago/Turabian StyleZhang, Yongning, Xiaoying Ren, Fei Zhang, Yulei Liu, and Jierui Li. 2024. "A Deep Learning-Based Dual-Scale Hybrid Model for Ultra-Short-Term Photovoltaic Power Forecasting" Sustainability 16, no. 17: 7340. https://doi.org/10.3390/su16177340