Short-term load forecasting based on WM algorithm and transfer learning model

Wei, Nan; Yin, Chuang; Yin, Lihua; Tan, Jingyi; Liu, Jinyuan; Wang, Shouxi; Qiao, Weibiao; Zeng, Fanhua

doi:10.1016/j.apenergy.2023.122087

Cited by 22 publications

(2 citation statements)

References 28 publications

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“…As a result, we will conduct further studies to investigate the reasons for these fluctuations and consider smoothing them after trying to recognize these sections to improve its performance in our future work. In addition, transfer learning, as a deep learning method, has achieved significant results in load forecasting research [36,37]. Therefore, we also consider introducing transfer learning methods in our future work.…”

Section: Discussionmentioning

confidence: 99%

An Empirical Mode Decomposition-Based Hybrid Model for Sub-Hourly Load Forecasting

Yin,

Wei,

et al. 2024

Energies

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Sub-hourly load forecasting can provide accurate short-term load forecasts, which is important for ensuring a secure operation and minimizing operating costs. Decomposition algorithms are suitable for extracting sub-series and improving forecasts in the context of short-term load forecasting. However, some existing algorithms like singular spectrum analysis (SSA) struggle to decompose high sampling frequencies and rapidly changing sub-hourly load series due to inherent flaws. Considering this, we propose an empirical mode decomposition-based hybrid model, named EMDHM. The decomposition part of this novel model first detrends the linear and periodic components from the original series. The remaining detrended long-range correlation series is simplified using empirical mode decomposition (EMD), generating intrinsic mode functions (IMFs). Fluctuation analysis is employed to identify high-frequency information, which divide IMFs into two types of long-range series. In the forecasting part, linear and periodic components are predicted by linear and trigonometric functions, while two long-range components are fitted by long short-term memory (LSTM) for prediction. Four forecasting series are ensembled to find the final result of EMDHM. In experiments, the model’s framework we propose is highly suitable for handling sub-hourly load datasets. The MAE, RMSE, MARNE, and R2 of EMDHM have improved by 20.1%, 26.8%, 22.1%, and 5.4% compared to single LSTM, respectively. Furthermore, EMDHM can handle both short- and long-sequence, sub-hourly load forecasting tasks. Its R2 only decreases by 4.7% when the prediction length varies from 48 to 720, which is significantly lower than other models.

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Section: Discussionmentioning

confidence: 99%

An Empirical Mode Decomposition-Based Hybrid Model for Sub-Hourly Load Forecasting

Yin,

Wei,

et al. 2024

Energies

Self Cite

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“…A limited training dataset is prone to overfitting deep learning models, resulting in a sub-optimal prediction accuracy. The latest research suggests that transfer learning can solve the above problems, and it has a large number of successful applications in the field of energy prediction [24,25]. In detail, transfer learning takes the knowledge gained from one domain containing a rich training dataset and uses it to solve problems in the target domain.…”

Section: Literature Reviewmentioning

confidence: 99%

A Deep Learning Approach Based on Novel Multi-Feature Fusion for Power Load Prediction

Xiao,

An,

Zhang

2024

Processes

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Adequate power load data are the basis for establishing an efficient and accurate forecasting model, which plays a crucial role in ensuring the reliable operation and effective management of a power system. However, the large-scale integration of renewable energy into the power grid has led to instabilities in power systems, and the load characteristics tend to be complex and diversified. Aiming at this problem, this paper proposes a short-term power load transfer forecasting method. To fully exploit the complex features present in the data, an online feature-extraction-based deep learning model is developed. This approach aims to extract the frequency-division features of the original power load on different time scales while reducing the feature redundancy. To solve the prediction challenges caused by insufficient historical power load data, the source domain model parameters are transferred to the target domain model utilizing Kendall’s correlation coefficient and the Bayesian optimization algorithm. To verify the prediction performance of the model, experiments are conducted on multiple datasets with different features. The simulation results show that the proposed model is robust and effective in load forecasting with limited data. Furthermore, if real-time data of new energy power systems can be acquired and utilized to update and correct the model in future research, this will help to adapt and integrate new energy sources and optimize energy management.

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Efficiency-based Pareto Optimization of Building Energy Consumption and Thermal Comfort: A Case Study of a Residential Building in Bushehr, Iran

Nasouri,

Delgarm

2023

J. Therm. Sci.

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Short-term load forecasting based on WM algorithm and transfer learning model

Cited by 22 publications

References 28 publications

An Empirical Mode Decomposition-Based Hybrid Model for Sub-Hourly Load Forecasting

An Empirical Mode Decomposition-Based Hybrid Model for Sub-Hourly Load Forecasting

A Deep Learning Approach Based on Novel Multi-Feature Fusion for Power Load Prediction

Efficiency-based Pareto Optimization of Building Energy Consumption and Thermal Comfort: A Case Study of a Residential Building in Bushehr, Iran

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