BiLSTM Multitask Learning-Based Combined Load Forecasting Considering the Loads Coupling Relationship for Multienergy System

Guo, Yixiu; Li, Yong; Qiao, Xuebo; Zhang, Zhenyu; Zhou, Wangfeng; Mei, Yujie; Lin, Jinjie; Zhou, Yicheng; Nakanishi, Yosuke

doi:10.1109/tsg.2022.3173964

Cited by 98 publications

(26 citation statements)

References 22 publications

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“…To avoid the one-sidedness of strong correlation features obtained by using a single correlation analysis method, when the constructed coupling feature and a certain type of load in the multi-energy loads simultaneously satisfy formula (7) (Guo et al, 2022;Li et al, 2022;Chen et al, 2023), then that coupling feature is considered a strong correlation feature for the multienergy loads.…”

Section: Multi-dimensional Multi-energy Load Coupling Characteristics...mentioning

confidence: 99%

“…To address this issue, some studies have employed multi-energy load forecasting models based on Multi-Task Learning (MTL), achieving high prediction accuracy. For instance, Literature (Guo et al, 2022) developed a multi-energy load forecasting model based on MTL and Bi-directional Long Short-Term Memory Networks (BiLSTM), effectively extracting potential coupling information between loads. Literature (Wang et al, 2021) used a forecasting model combining MTL with Long Short-Term Memory Networks (LSTM) to forecast the trend curves of decomposed and reconstructed multi-energy loads.…”

Section: Introductionmentioning

confidence: 99%

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Ultra-short-term multi-energy load forecasting for integrated energy systems based on multi-dimensional coupling characteristic mining and multi-task learning

Huang,

Wang,

Wang

et al. 2024

Front. Energy Res.

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To address the challenges posed by the randomness and volatility of multi-energy loads in integrated energy systems for ultra-short-term accurate load forecasting, this paper proposes an ultra-short-term multi-energy load forecasting method based on multi-dimensional coupling feature mining and multi-task learning. Firstly, a method for mining multi-dimensional coupling characteristics of multi-energy loads is proposed, integrating multiple correlation analysis methods. By constructing coupling features of multi-energy loads and using them as input features of the model, the complex coupling relationships between multi-energy loads are effectively quantified. Secondly, an ultra-short-term multi-energy load forecasting model based on multi-task learning and a temporal convolutional network is constructed. In the prediction model construction phase, the potential complex coupling characteristics between multiple loads can be fully explored, and the potential temporal associations and long-term dependencies within data can be extracted. Then, the multi-task learning loss function weight optimization method based on homoscedastic uncertainty is used to optimize the forecasting model, realizing automatic tuning of the loss function weight parameters and further improving the prediction performance of the model. Finally, an experimental analysis is conducted using the integrated energy system of Arizona State University in the United States as an example. The results show that the proposed forecasting method has higher prediction accuracy than other prediction methods.

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Section: Multi-dimensional Multi-energy Load Coupling Characteristics...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Ultra-short-term multi-energy load forecasting for integrated energy systems based on multi-dimensional coupling characteristic mining and multi-task learning

Huang,

Wang,

Wang

et al. 2024

Front. Energy Res.

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“…In [28], Cervone et al combined ANN with the analogue ensemble method to forecast the PV generations in both deterministic and probabilistic ways. Recently in [29], Guo et al proposed a combined load forecasting method for a Multi Energy Systems (MES) based on Bi-directional Long Short-Term Memory (BiLSTM). The combined load forecasting framework is trained with a multi-tasking approach for sharing the coupling information among the loads.…”

Section: Forecasting Of Renewable Energy Generationmentioning

confidence: 99%

Renewable energy management in smart home environment via forecast embedded scheduling based on Recurrent Trend Predictive Neural Network

Nakıp

Çopur²,

Biyik

et al. 2023

Applied Energy

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“…To address the challenge of incorporating the influence of multiple features on the changing state of ADNs, researchers have combined feature selection techniques with forecasting models. Currently, the commonly used feature selection method is the maximum information coefficient (MIC) [25]. Compared to methods such as the Pearson coefficient, MIC can assess the strength of the correlation between two variables without the need for in-depth analysis, and it can also capture complex nonlinear relationships.…”

Section: Introductionmentioning

confidence: 99%

Optimal Extreme Random Forest Ensemble for Active Distribution Network Forecasting-Aided State Estimation Based on Maximum Average Energy Concentration VMD State Decomposition

Guo

Jin

2023

Energies

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As the penetration rate of distributed generators (DG) in active distribution networks (ADNs) gradually increases, it is necessary to accurately estimate the operating state of the ADNs to ensure their safe and stable operation. However, the high randomness and volatility of distributed generator output and active loads have increased the difficulty of state estimation. To solve this problem, a method is proposed for forecasting-aided state estimation (FASE) in ADNs, which integrates the optimal extreme random forest based on the maximum average energy concentration (MAEC) and variable mode decomposition (VMD) of states. Firstly, a parameter optimization model based on MAEC is constructed to decompose the state variables of the ADNs into a set of intrinsic mode components using VMD. Then, strongly correlated weather and date features in ADNs state prediction are selected using the multivariate rapid maximum information coefficient (RapidMIC) based on Schmidt orthogonal decomposition. Finally, by combining the set of intrinsic mode functions of the ADNs state, calendar rules, and weather features, an ensemble FASE method based on the extreme random tree (ERT) ensemble for the ADNs based on cubature particle filtering (CPF) is developed. An optimization model based on mean absolute error and root mean square error is established to obtain the optimal integration strategy and final estimation results. Simulation verification is performed on the IEEE 118-bus standard distribution system. The results show that the proposed method achieves higher accuracy compared to other estimation methods, with root mean square errors of 1.4902 × 10−4 for voltage magnitude and 4.8915 × 10−3 for phase angle.

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BiLSTM Multitask Learning-Based Combined Load Forecasting Considering the Loads Coupling Relationship for Multienergy System

Cited by 98 publications

References 22 publications

Ultra-short-term multi-energy load forecasting for integrated energy systems based on multi-dimensional coupling characteristic mining and multi-task learning

Ultra-short-term multi-energy load forecasting for integrated energy systems based on multi-dimensional coupling characteristic mining and multi-task learning

Renewable energy management in smart home environment via forecast embedded scheduling based on Recurrent Trend Predictive Neural Network

Optimal Extreme Random Forest Ensemble for Active Distribution Network Forecasting-Aided State Estimation Based on Maximum Average Energy Concentration VMD State Decomposition

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