Short-term Power Load Forecasting Based on Grey Relational Analysis and Support Vector Machine

Sun, Wei; Pang, Xinfu; Liu, Wei; Wang, Yibao; Luan, Changfeng

doi:10.1109/iai55780.2022.9976828

Cited by 2 publications

(1 citation statement)

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“…There is a lot of work on SVMs for load forecasting that provides ideas for the accuracy of the forecast [19][20][21]. Pang et al [22] used gray relational analysis and support vector machines to study shortterm electrical load forecasting and proved the validity of the method. Liu et al [23] presented a method for electric load forecasting using Elman neural network and particle swarm optimization algorithm, significantly improving load forecasting accuracy.…”

Section: Machine Learningmentioning

confidence: 99%

Research on Peak Load Prediction of Distribution Network Lines Based on Prophet-LSTM Model

Chen

Wang

et al. 2023

Sustainability

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The increasing demand for precise load forecasting for distribution networks has become a crucial requirement due to the continual surge in power consumption. Accurate forecasting of peak loads for distribution networks is paramount to ensure that power grids operate smoothly and to optimize their configuration. Many load forecasting methods do not meet the requirements for accurate data and trend fitting. To address these issues, this paper presents a novel forecasting model called Prophet-LSTM, which combines the strengths of the Prophet model’s high trend fitting and LSTM model’s high prediction accuracy, resulting in improved accuracy and effectiveness of peak load forecasting. The proposed algorithm models the distribution network peak load using the Prophet-LSTM algorithm. The researchers then analyzed the experimental data and model of the algorithm to evaluate its effectiveness. We found that the Prophet-LSTM algorithm outperformed the Prophet and LSTM models individually in peak load prediction. We evaluate the proposed model against commonly used forecasting models using MAE (mean absolute error) and RMSE (root mean square error) as evaluation metrics. The results indicate that the proposed model has better forecasting accuracy and stability. As a result, it can predict the peak load of distribution networks more accurately. In conclusion, this study offers a valuable contribution to load forecasting for distribution networks. The proposed Prophet-LSTM algorithm provides a more precise and stable prediction, making it a promising approach for future applications in distribution network load forecasting.

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