A Transfer Learning Strategy for Short-term Wind Power Forecasting

Cao, Longpeng; Wang, Long; Luo, Xiong; Wang, Long

doi:10.1109/cac.2018.8623453

Cited by 6 publications

(3 citation statements)

References 13 publications

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“…While the principle approach of transferring an autoencoder for a target is combinable with our approach, we argue that considering the conditional distribution of the power forecast is more relevant for model selection and combination. The data driven TL approaches presented in [12] and [13] are outside the scope of this article.…”

Section: Related Workmentioning

confidence: 99%

Model Selection, Adaptation, and Combination for Transfer Learning in Wind and Photovoltaic Power Forecasts

Schreiber¹,

Sick²

2022

Preprint

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There is recent interest in using model hubs, a collection of pre-trained models, in computer vision tasks. To utilize the model hub, we first select a source model and then adapt the model for the target to compensate for differences. While there is yet limited research on a model selection and adaption for computer vision tasks, this holds even more for the field of renewable power. At the same time, it is a crucial challenge to provide forecasts for the increasing demand for power forecasts based on weather features from a numerical weather prediction. We close these gaps by conducting the first thorough experiment for model selection and adaptation for transfer learning in renewable power forecast, adopting recent results from the field of computer vision on six datasets. We adopt models based on data from different seasons and limit the amount of training data. As an extension of the current state of the art, we utilize a Bayesian linear regression for forecasting the response based on features extracted from a neural network. This approach outperforms the baseline with only seven days of training data. We further show how combining multiple models through ensembles can significantly improve the model selection and adaptation approach. In fact, with more than 30 days of training data, both proposed model combination techniques achieve similar results to those models trained with a full year of training data.

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Section: Related Workmentioning

confidence: 99%

Model Selection, Adaptation, and Combination for Transfer Learning in Wind and Photovoltaic Power Forecasts

Schreiber¹,

Sick²

2022

Preprint

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“…By clustering similar wind parks through their distribution, an intelligent weighting scheme provides predictions for a new park. The approach of [15] uses various machine learning models to predict short-term wind power time series. To neglect the limited data in the target domain, they build an auxiliary dataset through k-nearest neighbors.…”

Section: Transfer Learning For Renewable Power Forecastsmentioning

confidence: 99%

Task Embedding Temporal Convolution Networks for Transfer Learning Problems in Renewable Power Time Series Forecast

Schreiber

Vogt

Sick

2021

Lecture Notes in Computer Science

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Task embeddings in multi-layer perceptrons for multi-task learning and inductive transfer learning in renewable power forecasts have recently been introduced. In many cases, this approach improves the forecast error and reduces the required training data. However, it does not take the seasonal influences in power forecasts within a day into account, i.e., the diurnal cycle. Therefore, we extended this idea to temporal convolutional networks to consider those seasonalities. We propose transforming the embedding space, which contains the latent similarities between tasks, through convolution and providing these results to the network's residual block. The proposed architecture significantly improves up to 25% for multi-task learning for power forecasts on the EuropeWindFarm and Ger-manSolarFarm dataset compared to the multi-layer perceptron approach. Based on the same data, we achieve a ten percent improvement for the wind datasets and more than 20% in most cases for the solar dataset for inductive transfer learning without catastrophic forgetting. Finally, we are the first proposing zero-shot learning for renewable power forecasts to provide predictions even if no training data is available.

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“…Previously, transfer learning has applied for regression and classification problems in machine learning [ 31 ]. Recently, transfer learning have been applied for time series forecasting for real-world problems [ 32 , 33 , 34 ]. However, according to the best of our knowledge, this is the first time that transfer learning is employed in hierarchical forecasting problems.…”

Section: The Proposed Approachmentioning

confidence: 99%

Deep LSTM-Based Transfer Learning Approach for Coherent Forecasts in Hierarchical Time Series

Sagheer

Hamdoun

Youness

2021

Sensors

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Hierarchical time series is a set of data sequences organized by aggregation constraints to represent many real-world applications in research and the industry. Forecasting of hierarchical time series is a challenging and time-consuming problem owing to ensuring the forecasting consistency among the hierarchy levels based on their dimensional features. The excellent empirical performance of our Deep Long Short-Term Memory (DLSTM) approach on various forecasting tasks motivated us to extend it to solve the forecasting problem through hierarchical architectures. Toward this target, we develop the DLSTM model in auto-encoder (AE) fashion and take full advantage of the hierarchical architecture for better time series forecasting. DLSTM-AE works as an alternative approach to traditional and machine learning approaches that have been used to manipulate hierarchical forecasting. However, training a DLSTM in hierarchical architectures requires updating the weight vectors for each LSTM cell, which is time-consuming and requires a large amount of data through several dimensions. Transfer learning can mitigate this problem by training first the time series at the bottom level of the hierarchy using the proposed DLSTM-AE approach. Then, we transfer the learned features to perform synchronous training for the time series of the upper levels of the hierarchy. To demonstrate the efficiency of the proposed approach, we compare its performance with existing approaches using two case studies related to the energy and tourism domains. An evaluation of all approaches was based on two criteria, namely, the forecasting accuracy and the ability to produce coherent forecasts through through the hierarchy. In both case studies, the proposed approach attained the highest accuracy results among all counterparts and produced more coherent forecasts.

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A Transfer Learning Strategy for Short-term Wind Power Forecasting

Cited by 6 publications

References 13 publications

Model Selection, Adaptation, and Combination for Transfer Learning in Wind and Photovoltaic Power Forecasts

Model Selection, Adaptation, and Combination for Transfer Learning in Wind and Photovoltaic Power Forecasts

Task Embedding Temporal Convolution Networks for Transfer Learning Problems in Renewable Power Time Series Forecast

Deep LSTM-Based Transfer Learning Approach for Coherent Forecasts in Hierarchical Time Series

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