Personalized demand response based on sub-CDL considering energy consumption characteristics of customers

Shao, Yunfei; Fan, Shuai; Meng, Yuhang; Jia, Kunqi; He, Guangyu

doi:10.1016/j.apenergy.2024.123964

Applied Energy

2024

DOI: 10.1016/j.apenergy.2024.123964

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Personalized demand response based on sub-CDL considering energy consumption characteristics of customers

Yunfei Shao,

Shuai Fan,

Yuhang Meng

et al.

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2024

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Cited by 1 publication

References 36 publications

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Multi-source Data Fusion-based Grid-level Load Forecasting

Ye,

Teng,

Song

et al. 2024

Preprint

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Grid-level dispatching is generally based on the accumulation of independent load forecasting data from provincial and municipal dispatch centers. However, the differences in economic development levels and the frequency of forecasting result updates among provinces and cities lead to certain limitations in the direct accumulation method, affecting the accuracy of the integrated forecasting results. To address this, this paper proposes a short-term load forecasting method for the power grid based on the i-Transformer model. First, the dataset is constructed through data preprocessing and feature engineering, followed by training and optimizing the model parameters. Further, considering the differences in forecasting results reported by provincial dispatch centers, principal component analysis is used to determine the weights of provinces and cities, thereby effectively integrating the forecasting data from different provinces and cities through weighting. The case study shows that the i-Transformer outperforms traditional statistical and machine learning algorithms on multiple evaluation metrics, and the integration method has considerable potential in handling multi-source heterogeneous data and improving forecasting accuracy. This paper provides a new means of load forecasting result integration for power grid dispatch centers, ensuring the safe, high-quality, and economical operation of the power system.

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Multi-source Data Fusion-based Grid-level Load Forecasting

Ye,

Teng,

Song

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

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Personalized demand response based on sub-CDL considering energy consumption characteristics of customers

Cited by 1 publication

References 36 publications

Multi-source Data Fusion-based Grid-level Load Forecasting

Multi-source Data Fusion-based Grid-level Load Forecasting

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