A short-term electric load forecast method based on improved sequence-to-sequence GRU with adaptive temporal dependence

Li, Dan; Sun, Guangfan; Miao, Shuwei; Gu, Yingzhong; Zhang, Yuanhang; He, Shuai

doi:10.1016/j.ijepes.2021.107627

Cited by 40 publications

(12 citation statements)

References 25 publications

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“…In order to address this shortcoming, sequence-to-sequence models were explored. Marino et al [34], Li et al [35], and Sehovac et al [36] achieved good accuracy using this architecture. These works positioned the S2S RNN algorithm as the state-of-the-art approach for electrical load forecasting tasks by successfully comparing S2S to DNN [36], RNN [35,36], LSTM [34][35][36], and CNN [35].…”

Section: Related Workmentioning

confidence: 79%

“…Marino et al [34], Li et al [35], and Sehovac et al [36] achieved good accuracy using this architecture. These works positioned the S2S RNN algorithm as the state-of-the-art approach for electrical load forecasting tasks by successfully comparing S2S to DNN [36], RNN [35,36], LSTM [34][35][36], and CNN [35]. Subsequently, Sehovac et al [37] further improved their model's accuracy by including attention to their architecture.…”

Section: Related Workmentioning

confidence: 79%

“…The objective of our work is to improve accuracy of electrical load forecasting over diverse data sources by adapting the transformer architecture from the NLP domain. In contrast to the sequential nature of the latest state-of-the-art load forecasting techniques [34][35][36], the transformer architecture is parallelizable. Additionally, to ensure portability and reproducibility, the work presented in this paper is evaluated on 20 data streams over a number of input windows and for various forecasting horizons.…”

Section: Related Workmentioning

confidence: 99%

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Transformer-Based Model for Electrical Load Forecasting

2022

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Amongst energy-related CO2 emissions, electricity is the largest single contributor, and with the proliferation of electric vehicles and other developments, energy use is expected to increase. Load forecasting is essential for combating these issues as it balances demand and production and contributes to energy management. Current state-of-the-art solutions such as recurrent neural networks (RNNs) and sequence-to-sequence algorithms (Seq2Seq) are highly accurate, but most studies examine them on a single data stream. On the other hand, in natural language processing (NLP), transformer architecture has become the dominant technique, outperforming RNN and Seq2Seq algorithms while also allowing parallelization. Consequently, this paper proposes a transformer-based architecture for load forecasting by modifying the NLP transformer workflow, adding N-space transformation, and designing a novel technique for handling contextual features. Moreover, in contrast to most load forecasting studies, we evaluate the proposed solution on different data streams under various forecasting horizons and input window lengths in order to ensure result reproducibility. Results show that the proposed approach successfully handles time series with contextual data and outperforms the state-of-the-art Seq2Seq models.

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Section: Related Workmentioning

confidence: 79%

Section: Related Workmentioning

confidence: 79%

Section: Related Workmentioning

confidence: 99%

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Transformer-Based Model for Electrical Load Forecasting

2022

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“…However, an increase in the size of the look-back window results in decreased prediction accuracy. Another alternative for time series forecasting is the Gated Recurrent Unit network (GRU), which exhibits shorter execution times than LSTM by consolidating forget and input gates into a single update gate [22]. In [23], Ijaz et al propose an ANN-LSTM model for predicting hour-ahead load demand, where the ANN functions as a temporal feature extractor.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Short-Term Load Forecasting for Multiple Locations: An Integrated Approach

Baul,

Sarker,

Sikder

et al. 2024

BDCC

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Short-term load forecasting (STLF) plays a crucial role in the planning, management, and stability of a country’s power system operation. In this study, we have developed a novel approach that can simultaneously predict the load demand of different regions in Bangladesh. When making predictions for loads from multiple locations simultaneously, the overall accuracy of the forecast can be improved by incorporating features from the various areas while reducing the complexity of using multiple models. Accurate and timely load predictions for specific regions with distinct demographics and economic characteristics can assist transmission and distribution companies in properly allocating their resources. Bangladesh, being a relatively small country, is divided into nine distinct power zones for electricity transmission across the nation. In this study, we have proposed a hybrid model, combining the Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU), designed to forecast load demand seven days ahead for each of the nine power zones simultaneously. For our study, nine years of data from a historical electricity demand dataset (from January 2014 to April 2023) are collected from the Power Grid Company of Bangladesh (PGCB) website. Considering the nonstationary characteristics of the dataset, the Interquartile Range (IQR) method and load averaging are employed to deal effectively with the outliers. Then, for more granularity, this data set has been augmented with interpolation at every 1 h interval. The proposed CNN-GRU model, trained on this augmented and refined dataset, is evaluated against established algorithms in the literature, including Long Short-Term Memory Networks (LSTM), GRU, CNN-LSTM, CNN-GRU, and Transformer-based algorithms. Compared to other approaches, the proposed technique demonstrated superior forecasting accuracy in terms of mean absolute performance error (MAPE) and root mean squared error (RMSE). The dataset and the source code are openly accessible to motivate further research.

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“…It is the ultimate goal of transformer fault diagnosis to locate the rapid positioning of the transformer fault by identifying different state signals with classifiers. With the continuous development of artificial intelligence, machine learning-based models have been used for power load forecasting [ 37 , 38 ], power system security assessment [ 39 , 40 ] and circuit fault location [ 41 , 42 ]. The existing methods of transformer fault diagnosis often use machine learning algorithms such as support vector machine (SVM) [ 43 , 44 ], probabilistic neural network (PNN) [ 45 ] and back propagation neural network (BPNN) [ 46 ] as classifiers to effectively identify different transformer faults.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Power Transformer Based on Time-Shift Multiscale Bubble Entropy and Stochastic Configuration Network

Chen

Tian

Zhang

et al. 2022

Entropy

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In order to accurately diagnose the fault type of power transformer, this paper proposes a transformer fault diagnosis method based on the combination of time-shift multiscale bubble entropy (TSMBE) and stochastic configuration network (SCN). Firstly, bubble entropy is introduced to overcome the shortcomings of traditional entropy models that rely too heavily on hyperparameters. Secondly, on the basis of bubble entropy, a tool for measuring signal complexity, TSMBE, is proposed. Then, the TSMBE of the transformer vibration signal is extracted as a fault feature. Finally, the fault feature is inputted into the stochastic configuration network model to achieve an accurate identification of different transformer state signals. The proposed method was applied to real power transformer fault cases, and the research results showed that TSMBE-SCN achieved 99.01%, 99.1%, 99.11%, 99.11%, 99.14% and 99.02% of the diagnostic rates under different folding numbers, respectively, compared with conventional diagnostic models MBE-SCN, TSMSE-SCN, MSE-SCN, TSMDE-SCN and MDE-SCN. This comparison shows that TSMBE-SCN has a strong competitive advantage, which verifies that the proposed method has a good diagnostic effect. This study provides a new method for power transformer fault diagnosis, which has good reference value.

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A short-term electric load forecast method based on improved sequence-to-sequence GRU with adaptive temporal dependence

Cited by 40 publications

References 25 publications

Transformer-Based Model for Electrical Load Forecasting

Transformer-Based Model for Electrical Load Forecasting

Data-Driven Short-Term Load Forecasting for Multiple Locations: An Integrated Approach

Fault Diagnosis of Power Transformer Based on Time-Shift Multiscale Bubble Entropy and Stochastic Configuration Network

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