Short-term wind power ramp event prediction based on LSTM and error correction algorithm

Lin, Tengjiao; Yao, Yang; Xu, Dengguo; Li, Chao; Wang, Peng

doi:10.1109/ccdc52312.2021.9602700

Cited by 3 publications

(2 citation statements)

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“…The algorithm consists of an inner loop and an outer loop. The inner loop is used to calculate the trend and seasonal components of the user's electricity consumption, and the outer loop is to reduce the influence of outliers through the robustness weight [17]. The STL algorithm starts with the preset trend component Q t,tr(0) = 0.…”

Section: Seasonal and Trend Decomposition Using Loessmentioning

confidence: 99%

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Monthly load forecasting for China’s delegate power purchase mechanism considering customer churn

Huang¹,

Wen²,

Wen³

et al. 2023

International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023)

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The delegate power purchase mechanism in China’s electricity retail reform requires power grid companies to make accurate user load forecasting and reasonable market decisions, and the result will affect the user’s electricity price. Under this background, a monthly load forecasting method is studied for power grid company considering the impact of customer churn on the agency electricity consumption. Firstly, a customer churn model based on life cycle theory is proposed to predict the customer churn rate of a power grid company in a specific period of the development process of China’s electricity retail reform. The seasonal and trend decomposition algorithm for decomposing user monthly electricity consumption series is presented, based on which the trend, seasonal, and remainder components of user load are predicted respectively by using polynomial curve fitting methods. Then, the monthly load forecasting model for agent users is proposed according to the expected customer churn rate and the prediction of different load components. A simple electricity market is served for demonstrating the proposed method, and the simulation results show that the proposed method has higher accuracy of monthly load forecasting than the other model that does not consider the predicted customer churn rate of power grid companies.

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Section: Seasonal and Trend Decomposition Using Loessmentioning

confidence: 99%

“…As shown in Fig. 4, the proposed load forecasting method has more accurate results than the conventional STL based method in [16]] and [ [17] that do not consider the customer churn. The RMSE of load forecasting results under the proposed method is 7.69 MWh, while that of the comparison method is 38.65 MWh.…”

Section: Case Studymentioning

confidence: 99%