Power Forecasting of Regional Wind Farms via Variational Auto-Encoder and Deep Hybrid Transfer Learning

Khan, Mansoor; Naeem, Muhammad Rashid; Al‐Ammar, Essam A.; Ko, Wonsuk; Vettikalladi, Hamsakutty; Ahmad, Irfan

doi:10.3390/electronics11020206

Cited by 16 publications

(6 citation statements)

References 47 publications

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“…Therefore, comparing against the AE-NN model provides a fair assessment of the effectiveness of our approach. This is also similar to the baseline models considered in recent studies [14,27] All single-task models are compared against the proposed UAE-TENN method, and their evaluations are conducted separately for each task. As a preliminary insight into the interrelationships among parks within a dataset, we compute the standard deviation of the unified target data across all parks within each dataset.…”

Section: Comparison With Stl Methodsmentioning

confidence: 99%

Multi-Task Representation Learning for Renewable-Power Forecasting: A Comparative Analysis of Unified Autoencoder Variants and Task-Embedding Dimensions

Nivarthi,

Vogt,

Sick

2023

MAKE

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Typically, renewable-power-generation forecasting using machine learning involves creating separate models for each photovoltaic or wind park, known as single-task learning models. However, transfer learning has gained popularity in recent years, as it allows for the transfer of knowledge from source parks to target parks. Nevertheless, determining the most similar source park(s) for transfer learning can be challenging, particularly when the target park has limited or no historical data samples. To address this issue, we propose a multi-task learning architecture that employs a Unified Autoencoder (UAE) to initially learn a common representation of input weather features among tasks and then utilizes a Task-Embedding layer in a Neural Network (TENN) to learn task-specific information. This proposed UAE-TENN architecture can be easily extended to new parks with or without historical data. We evaluate the performance of our proposed architecture and compare it to single-task learning models on six photovoltaic and wind farm datasets consisting of a total of 529 parks. Our results show that the UAE-TENN architecture significantly improves power-forecasting performance by 10 to 19% for photovoltaic parks and 5 to 15% for wind parks compared to baseline models. We also demonstrate that UAE-TENN improves forecast accuracy for a new park by 19% for photovoltaic parks, even in a zero-shot learning scenario where there is no historical data. Additionally, we propose variants of the Unified Autoencoder with convolutional and LSTM layers, compare their performance, and provide a comparison among architectures with different numbers of task-embedding dimensions. Finally, we demonstrate the utility of trained task embeddings for interpretation and visualization purposes.

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Section: Comparison With Stl Methodsmentioning

confidence: 99%

Multi-Task Representation Learning for Renewable-Power Forecasting: A Comparative Analysis of Unified Autoencoder Variants and Task-Embedding Dimensions

Nivarthi,

Vogt,

Sick

2023

MAKE

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“…For larger portfolios it can provide an indication of quality -works only for physical conversion methods and transfer learning functions (e.g. [27])!…”

Section: Power Marketingmentioning

confidence: 99%

IEA Wind Recommended Practice for the Implementation of Renewable Energy Forecasting Solutions

2023

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“…Finally, the authors of [15] trained variational autoencoders on source parks and finetuned this on a target with limited data. They simultaneously utilized five variational datasets as source models to generate several different latent features.…”

Section: Related Workmentioning

confidence: 99%

Multi-Task Autoencoders and Transfer Learning for Day-Ahead Wind and Photovoltaic Power Forecasts

Schreiber

Sick

2022

Energies

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Integrating new renewable energy resources requires robust and reliable forecasts to ensure a stable electrical grid and avoid blackouts. Sophisticated representation learning techniques, such as autoencoders, play an essential role, as they allow for the extraction of latent features to forecast the expected generated wind and photovoltaic power for the next seconds up to days. Thereby, autoencoders reduce the required training time and the time spent in manual feature engineering and often improve the forecast error. However, most current renewable energy forecasting research on autoencoders focuses on smaller forecast horizons for the following seconds and hours based on meteorological measurements. At the same time, larger forecast horizons, such as day-ahead power forecasts based on numerical weather predictions, are crucial for planning loads and demands within the electrical grid to prevent power failures. There is little evidence on the ability of autoencoders and their respective forecasting models to improve through multi-task learning and time series autoencoders for day-ahead power forecasts. We can close these gaps by proposing a multi-task learning autoencoder based on the recently introduced temporal convolution network. This approach reduces the number of trainable parameters by 38 for photovoltaic data and 202 for wind data while having the best reconstruction error compared to nine other representation learning techniques. At the same time, this model decreases the day-ahead forecast error up to 18.3% for photovoltaic parks and 1.5% for wind parks. We round off these results by analyzing the influences of the latent size and the number of layers to fine-tune the encoder for wind and photovoltaic power forecasts.

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Power Forecasting of Regional Wind Farms via Variational Auto-Encoder and Deep Hybrid Transfer Learning

Cited by 16 publications

References 47 publications

Multi-Task Representation Learning for Renewable-Power Forecasting: A Comparative Analysis of Unified Autoencoder Variants and Task-Embedding Dimensions

Multi-Task Representation Learning for Renewable-Power Forecasting: A Comparative Analysis of Unified Autoencoder Variants and Task-Embedding Dimensions

IEA Wind Recommended Practice for the Implementation of Renewable Energy Forecasting Solutions

Multi-Task Autoencoders and Transfer Learning for Day-Ahead Wind and Photovoltaic Power Forecasts

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