Accurate solar generation forecasting is critical for ensuring power system reliability, economics, and effectiveness and controlling the supply-demand balance. This research offers novel multi-branch spatio-temporal forecasting models to improve forecasting accuracy and minimize forecasting errors. The first step is to build temporal models employing advanced deep learning architectures, such as Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and GRU with Attention (AttGRU). Next, spatio-temporal solar forecasting models are constructed. A novel multi-branch Attentive Gated Recurrent Residual network (ResAttGRU) consisting of multiple branches of residual networks (ResNet), GRU, and the attention mechanism is introduced. The proposed multi-branch ResAttGRU is capable of modeling data at various resolutions, extracting hierarchical features, and capturing short- and long-term dependencies. Moreover, this network also presents a strong multi-time-scale representative, while GRUs can exploit temporal information at less computational cost than the popular LSTM. The novelty of the developed architecture is in the utilization of multiple convolutional-based branches to learn multi-time-scale features jointly, accelerate the learning process, and reduce overfitting. This dissertation also compares the multi-branch ResAttGRU networks with state-of-the-art deep learning methods using 18 years of NSRDB data at 12 solar sites. The proposed multi-branch ResAttGRU requires 7.1% fewer parameters than multi-branch residual LSTM (ResLSTM) while achieving similar average RMSE, MAE, and R-squared values. Finally, to effectively model spatial correlation among neighboring solar sites as well as to alleviate performance degradation due to overfitting of conventional neural networks, a spatio-temporal framework comprised of concatenated multi-branch Residual network and Transformer (ResTrans) is developed. Numerical results indicate that the multi-branch ResTrans structure achieves the highest forecasting accuracy, with an average RMSE of 0.049 ( W/m^2 ), an average MAE of 0.031 (W/m^2 ), and a R^2 coefficient of 97%. / Electrical and Computer Engineering
| Identifer | oai:union.ndltd.org:TEMPLE/oai:scholarshare.temple.edu:20.500.12613/7974 |
| Date | January 2022 |
| Creators | Khoshgoftar Ziyabari, Seyedeh Saeedeh |
| Contributors | Du, Liang, Biswas, Saroj K., Bai, Li, Lu, Xiaonan, Gao, Hongchang |
| Publisher | Temple University. Libraries |
| Source Sets | Temple University |
| Language | English |
| Detected Language | English |
| Type | Thesis/Dissertation, Text |
| Format | 129 pages |
| Rights | IN COPYRIGHT- This Rights Statement can be used for an Item that is in copyright. Using this statement implies that the organization making this Item available has determined that the Item is in copyright and either is the rights-holder, has obtained permission from the rights-holder(s) to make their Work(s) available, or makes the Item available under an exception or limitation to copyright (including Fair Use) that entitles it to make the Item available., http://rightsstatements.org/vocab/InC/1.0/ |
| Relation | http://dx.doi.org/10.34944/dspace/7946, Theses and Dissertations |
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