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This study proposes an intelligent techno-economic assessment framework for wind energy end users, using a novel dual-input convolutional bidirectional long short-term memory (Dual-ConvBiLSTM) architecture to predict dynamic levelized cost of energy (LCOE). The proposed architecture separates weight matrices for wind supervisory control and data acquisition (SCADA) data and financial data. This allows the model to integrate both data streams at every time step through a custom dual-input cell. This approach is compared with five baseline architectures: Recurrent Neural Network (RNN), LSTM, BiLSTM, ConvLSTM, and ConvBiLSTM, which process data through separate parallel branches and concatenate outputs before final prediction. The Dual-ConvBiLSTM achieves an LCOE estimate of $4.0391 cents/kWh, closest to the actual value of $4.0450 cents/kWh, with a root mean squared error reduction of 51.8% compared to RNN, 47.0% to LSTM, 40.0% to BiLSTM, 36.7% to ConvLSTM, and 34.4% to ConvBiLSTM, demonstrating superior capability in capturing complex interactions between SCADA data and financial parameters. This intelligent framework potentially enhances economic assessment and enables end users to accelerate renewable energy deployment through more reliable financial prediction.
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Multi-Purpose, Multi-Step Deep Learning Framework for Network-Level Traffic Flow Prediction
This study proposes a data fusion and deep learning (DL) framework that learns high-level traffic features from network-level images to predict large-scale, multi-route, speed and volume of connected vehicles (CVs). We present a scalable and parallel method of processing statewide CVs’ trajectory data that leads to real-time insights on the micro-scale in time and space (two-dimensional (2D) arrays) on graphics processing unit (GPUs) using the Nvidia rapids framework and dask parallel cluster, which provided a 50× speed-up in the data extraction, transform and load (ETL). A UNet model is then applied to perform feature extraction and multi-route speed and volume channels over a multi-step prediction horizon. The accuracy and robustness of the proposed model are evaluated by taking different road types, times of day and image snippets and comparing the model to benchmarks: Convolutional Long–Short-Term Memory (ConvLSTM) and a historical average (HA). The results show that the proposed model outperforms benchmarks with an average improvement of 15% over ConvLSTM and 65% over the HA. Comparing the image snippets from each prediction model to the actual image shows that image textures were highly similar in UNet to the benchmark models used. UNet’s dominance in performing image predictions was also evident in multi-step forecasting, where the increase in errors was relatively minimal over longer prediction horizons.
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- Award ID(s):
- 2045786
- PAR ID:
- 10405737
- Date Published:
- Journal Name:
- Advances in Data Science and Adaptive Analysis
- Volume:
- 14
- Issue:
- 03n04
- ISSN:
- 2424-922X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1142/S2424922X22500103
