Short-Term Electricity Load Forecasting Based on Temporal Fusion Transformer Model

Huy, Pham Canh; Minh, Nguyen Quoc; Tien, Nguyen Dang; Anh, Tao Thi Quynh

doi:10.1109/access.2022.3211941

Cited by 40 publications

(5 citation statements)

References 19 publications

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“…Transformer was proposed and widely used in computer vision and natural language processing. Numerous studies have shown that it has the potential to improve predictions [29,45]. Informer is an efficient transformer-based model [30] designed for LSTF, and its ProbSparse self-attention effectively reduces time complexity.…”

Section: Deep Learning Methodsmentioning

confidence: 99%

A hybrid prediction method for short‐term load based on temporal convolutional networks and attentional mechanisms

Tian

Chen

et al. 2023

IET Generation Trans & Dist

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Accurate power load prediction is an important guide for power system planning and operation. High‐ or low‐load prediction results will affect the operation of the power system. In recent years, deep learning technology represented by convolution neural network (CNN) and transformer has been proved to be suitable for power load prediction. This paper proposes a new short‐term power load hybrid forecasting model, called channel enhanced attention (CEA) and temporal convolutional network (TCN)‐based transformer comprehensive forecasting model. This method combines the short‐term feature extraction ability of TCN with the long‐term dependent capture ability of transformer for short‐term load forecasting. And the CEA designed in this study is added to improve the prediction accuracy. On the same dataset, the designed model predicts power load mean square errors of 0.056 and 0.146 for the next 24 h and the next week, respectively, which is 0.002 to 0.073 and 0.012 to 0.024 lower than the baseline model. The experimental results show that the hybrid short‐term power load prediction model proposed in this paper is significantly better than the existing methods. The predicted curve is in agreement with the actual charge change, which provides a good guidance for short‐term power load prediction.

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Section: Deep Learning Methodsmentioning

confidence: 99%

A hybrid prediction method for short‐term load based on temporal convolutional networks and attentional mechanisms

Tian

Chen

et al. 2023

IET Generation Trans & Dist

View full text Add to dashboard Cite

show abstract

“…In addition, Pelekis et al [44] compared the accuracy of TCN and N-BEATS in STLF settings for the case of the Portuguese national load at a 15-minute resolution, with N-BEATS resulting to superior forecasts. With respect to transformers, Zhang et al [66] evaluated a time augmented transformer for STLF on the electrical load data of New South Wales in Australia, Lim et al [33] validated temporal fusion transformers for STLF on the UCI Electricity Load Diagrams Dataset [60], while Huy et al [25] employed the latter architecture to forecast the load of Hanoi city using weather and calendar features.…”

Section: Related Workmentioning

confidence: 99%

A comparative assessment of deep learning models for day-ahead load forecasting: Investigating key accuracy drivers

Pelekis¹,

Seisopoulos²,

Spiliotis³

et al. 2023

Preprint

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Short-term load forecasting (STLF) is vital for the daily operation of power grids. However, the nonlinearity, non-stationarity, and randomness characterizing electricity demand time series renders STLF a challenging task. To that end, different forecasting methods have been proposed in the literature for day-ahead load forecasting, including a variety of deep learning models that are currently considered to achieve state-of-the-art performance. In order to compare the accuracy of such models, we focus on national net aggregated STLF and examine well-established autoregressive neural networks of indicative architectures, namely multi-layer perceptrons, N-BEATS, long short-term memory neural networks, and temporal convolutional networks, for the case of Portugal. To investigate the factors that affect the performance of each model and identify the most appropriate per case, we also conduct a post-hoc analysis, correlating forecast errors with key calendar and weather features. Our results indicate that N-BEATS consistently outperforms the rest of the examined deep learning models. Additionally, we find that external factors can significantly impact accuracy, affecting both the actual and relative performance of the models.

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“…The LSTM-based method proposed by Memarzadeh and Keynia [49] has been validated successfully on load and price data collected from the Pennsylvania-New Jersey-Maryland (PJM) and Spain electricity markets. Similarly, advanced NN architectures have gained popularity [4,13,50], introducing innovative deep STLF approaches based on feed-forward [51,52], convolutional [53], or transformer [54,55] setups.…”

Section: Related Workmentioning

confidence: 99%

Transfer Learning for Day-Ahead Load Forecasting: A Case Study on European National Electricity Demand Time Series

Tzortzis,

Pelekis,

Spiliotis

et al. 2023

Mathematics

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Short-term load forecasting (STLF) is crucial for the daily operation of power grids. However, the non-linearity, non-stationarity, and randomness characterizing electricity demand time series renders STLF a challenging task. Various forecasting approaches have been proposed for improving STLF, including neural network (NN) models which are trained using data from multiple electricity demand series that may not necessarily include the target series. In the present study, we investigate the performance of a special case of STLF, namely transfer learning (TL), by considering a set of 27 time series that represent the national day-ahead electricity demand of indicative European countries. We employ a popular and easy-to-implement feed-forward NN model and perform a clustering analysis to identify similar patterns among the load series and enhance TL. In this context, two different TL approaches, with and without the clustering step, are compiled and compared against each other as well as a typical NN training setup. Our results demonstrate that TL can outperform the conventional approach, especially when clustering techniques are considered.

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Short-Term Electricity Load Forecasting Based on Temporal Fusion Transformer Model

Cited by 40 publications

References 19 publications

A hybrid prediction method for short‐term load based on temporal convolutional networks and attentional mechanisms

A hybrid prediction method for short‐term load based on temporal convolutional networks and attentional mechanisms

A comparative assessment of deep learning models for day-ahead load forecasting: Investigating key accuracy drivers

Transfer Learning for Day-Ahead Load Forecasting: A Case Study on European National Electricity Demand Time Series

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