Short-term load forecasting based on DenseNet-LSTM fusion model

Zhu, Wei; Sining, Wang; Han, Lu

doi:10.1109/icei52466.2021.00021

Cited by 2 publications

(1 citation statement)

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“…Statistical methods and traditional machine learning methods can not take into account the high volatility, uncertainty, and time correlation of load data at the same time, so that the prediction accuracy is far from efficient, and there is still room for improvement [43]. Single methods often come with several types of disadvantages including low computational efficiency, high computational complexity, and high error percentage.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Load Forecasting and Associated Weather Variables Prediction Using ResNet-LSTM Based Deep Learning

Chen

Dinavahi

et al. 2023

IEEE Access

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Short-term load forecasting is mainly utilized in control centers to explore the changing patterns of consumer loads and predict the load value at a certain time in the future. It is one of the key technologies for the smart grid implementation. The load parameters are affected by multi-dimensional factors. To sufficiently exploit the time series characteristics in load data and improve the accuracy of load forecasting, a hybrid model based on Residual Neural network (ResNet) and Long Short-Term Memory (LSTM) is proposed in this paper. First, the data with multiple feature parameters is reconstructed and input into ResNeT network for feature extraction. Second, the extracted feature vector is used as the input of LSTM for short-term load forecasting. Lastly, a practical example is used to compare this method with other models, which verifies the feasibility and superiority of input parameter feature extraction, and shows that the proposed combined method has higher prediction accuracy. In addition, this paper also carries out prediction experiments on the variables in the weather influencing factors.INDEX TERMS Long short-term memory (LSTM), residual neural network (ResNet), ResNet-LSTM, short-term load forecasting, time-series features.

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Section: Introductionmentioning

confidence: 99%